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feat: trim MCP query response payloads (#240)
This commit is contained in:
Andrey Avtomonov
GitHub
parent
cbbcf8e8bd
commit
25f639fba2
7 changed files with 235 additions and 1703 deletions
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@ -12,6 +12,7 @@ import type {
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KtxMcpToolHandlerContext,
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KtxMcpToolResult,
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KtxMcpUserContext,
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KtxSemanticLayerQueryResponse,
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NonArrayObject,
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} from './types.js';
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@ -60,7 +61,7 @@ const toolDescriptions = {
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sl_read_source:
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'Read a semantic-layer YAML source by connection id and source name. Example: sl_read_source({ connectionId: "warehouse", sourceName: "orders" }).',
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sl_query:
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'Execute a semantic-layer query and return rows, headers, generated SQL, and plan details. Example: sl_query({ connectionId: "warehouse", measures: ["orders.order_count"], dimensions: [{ field: "orders.created_at", granularity: "month" }] }).',
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'Execute a semantic-layer query and return headers, rows, and total row count, plus correctness notes (e.g. compile-only or fan-out) when relevant. The generated SQL and full query plan are omitted by default; request them with include: ["sql"] and/or include: ["plan"]. Example: sl_query({ connectionId: "warehouse", measures: ["orders.order_count"], dimensions: [{ field: "orders.created_at", granularity: "month" }], include: ["sql"] }).',
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sql_execution:
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'Execute one parser-validated read-only SQL query against a configured KTX connection. Example: sql_execution({ connectionId: "warehouse", sql: "select count(*) from public.orders", maxRows: 100 }).',
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memory_ingest:
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@ -73,7 +74,7 @@ const connectionListSchema = z.object({});
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const knowledgeSearchSchema = z.object({
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query: z.string().min(1).describe('Natural-language wiki search query, e.g. "revenue recognition policy".'),
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limit: z.number().int().min(1).max(50).default(10).describe('Maximum wiki pages to return. Defaults to 10.'),
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limit: z.number().int().min(1).max(50).default(10).describe('Maximum wiki pages to return.'),
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});
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const knowledgeReadSchema = z.object({
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@ -109,10 +110,7 @@ const slQueryOrderBySchema = z.object({
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.describe(
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'Field/measure/dimension id to order by, e.g. "orders.created_at", a dimension key like "mart_nrr_quarterly.quarter_label", or a measure alias.',
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),
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direction: z
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.enum(['asc', 'desc'])
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.default('asc')
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.describe('Sort direction: "asc" or "desc". Defaults to "asc".'),
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direction: z.enum(['asc', 'desc']).default('asc').describe('Sort direction for this field.'),
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});
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const slQuerySchema = z.object({
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@ -136,8 +134,12 @@ const slQuerySchema = z.object({
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.array(slQueryOrderBySchema)
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.default([])
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.describe('Sort clauses. Use {field, direction?} entries.'),
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limit: z.number().int().min(0).default(1000).describe('Maximum rows to return. Defaults to 1000.'),
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include_empty: z.boolean().default(true).describe('Whether to include empty dimension groups. Defaults to true.'),
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limit: z.number().int().min(0).default(1000).describe('Maximum rows to return.'),
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include_empty: z.boolean().default(true).describe('Whether to include empty dimension groups.'),
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include: z
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.array(z.enum(['plan', 'sql']))
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.default([])
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.describe('Extra detail to attach to the response: "sql" for the generated SQL, "plan" for the full query plan.'),
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});
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const entityDetailsTableRefSchema = z.object({
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@ -184,13 +186,13 @@ const discoverDataSchema = z.object({
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.optional()
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.describe('Optional connection id. Pass it when user intent pins a specific warehouse.'),
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kinds: z.array(discoverDataKindSchema.describe('Reference kind to include.')).optional().describe('Optional kind filter.'),
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limit: z.number().int().min(1).max(50).default(15).optional().describe('Maximum refs to return. Defaults to 15.'),
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limit: z.number().int().min(1).max(50).default(10).optional().describe('Maximum refs to return.'),
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});
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const sqlExecutionSchema = z.object({
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connectionId: connectionIdSchema.describe('Connection id to execute against. Required for raw SQL.'),
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sql: z.string().min(1).describe('Parser-validated read-only SQL, e.g. "select count(*) from public.orders".'),
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maxRows: z.number().int().min(1).max(10_000).default(1000).optional().describe('Maximum rows to return. Defaults to 1000.'),
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maxRows: z.number().int().min(1).max(10_000).default(1000).optional().describe('Maximum rows to return.'),
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});
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const memoryIngestSchema = z.object({
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@ -266,10 +268,14 @@ const slReadSourceOutputSchema = z.object({
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const slQueryOutputSchema = z.object({
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connectionId: z.string().optional(),
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dialect: z.string().optional(),
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sql: z.string(),
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headers: z.array(z.string()),
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rows: z.array(z.array(z.unknown())),
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totalRows: z.number(),
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// Correctness signals hoisted out of `plan` so they survive default projection (e.g. compile-only
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// status, fan-out warnings). Present only when there is something to report.
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notes: z.array(z.string()).optional(),
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// Opt-in detail, attached only when requested via the `include` input.
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sql: z.string().optional(),
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plan: unknownRecordSchema.optional(),
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});
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@ -411,12 +417,59 @@ const memoryIngestStatusOutputSchema = z.object({
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/** @internal */
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export function jsonToolResult<T extends NonArrayObject>(structuredContent: T): KtxMcpToolResult<T> {
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// Compact (non-indented) JSON: this `content` text is the copy the model reads. Pretty-printing
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// arrays-of-arrays (every `rows` payload) puts one scalar per line, inflating tabular results by
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// a large constant factor. `structuredContent` carries the same data for structured-output clients.
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return {
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content: [{ type: 'text', text: JSON.stringify(structuredContent, null, 2) }],
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content: [{ type: 'text', text: JSON.stringify(structuredContent) }],
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structuredContent,
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};
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}
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/**
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* Pull the correctness-critical signals out of a query plan so they survive even when the caller
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* did not opt into the full `plan`. Returns an empty list when there is nothing to flag.
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*/
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function slQueryNotes(plan: Record<string, unknown> | undefined): string[] {
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if (!plan) {
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return [];
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}
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const notes: string[] = [];
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const execution = plan.execution;
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if (
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execution &&
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typeof execution === 'object' &&
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(execution as Record<string, unknown>).mode === 'compile_only'
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) {
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const reason = (execution as Record<string, unknown>).reason;
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notes.push(typeof reason === 'string' ? reason : 'Compiled SQL only; no rows were executed.');
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}
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if (plan.has_fan_out === true) {
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const description = typeof plan.fan_out_description === 'string' ? plan.fan_out_description.trim() : '';
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notes.push(description.length > 0 ? description : 'Fan-out detected: measure totals may be inflated by joins.');
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}
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return notes;
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}
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/**
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* Default sl_query response is the minimum the agent needs to read the result: connection, headers,
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* rows, totals, plus any correctness notes. The generated `sql` and the full `plan` are attached only
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* when explicitly requested via `include`, since both are large and echo information the caller already has.
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*/
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function projectSlQueryResult(result: KtxSemanticLayerQueryResponse, include: ('plan' | 'sql')[]) {
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const notes = slQueryNotes(result.plan);
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return {
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...(result.connectionId !== undefined ? { connectionId: result.connectionId } : {}),
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...(result.dialect !== undefined ? { dialect: result.dialect } : {}),
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headers: result.headers,
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rows: result.rows,
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totalRows: result.totalRows,
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...(notes.length > 0 ? { notes } : {}),
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...(include.includes('sql') ? { sql: result.sql } : {}),
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...(include.includes('plan') && result.plan ? { plan: result.plan } : {}),
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};
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}
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function jsonErrorToolResult(text: string): KtxMcpToolResult<Record<string, never>> {
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return {
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content: [{ type: 'text', text }],
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@ -618,23 +671,22 @@ export function registerKtxContextTools(deps: RegisterKtxContextToolsDeps): void
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slQuerySchema,
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async (input, context) => {
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const onProgress = mcpProgressCallback(context);
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return jsonToolResult(
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await semanticLayer.query(
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{
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connectionId: input.connectionId,
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query: {
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measures: input.measures,
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dimensions: input.dimensions,
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filters: input.filters,
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segments: input.segments,
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order_by: input.order_by,
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limit: input.limit,
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include_empty: input.include_empty,
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},
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const result = await semanticLayer.query(
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{
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connectionId: input.connectionId,
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query: {
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measures: input.measures,
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dimensions: input.dimensions,
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filters: input.filters,
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segments: input.segments,
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order_by: input.order_by,
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limit: input.limit,
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include_empty: input.include_empty,
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},
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onProgress ? { onProgress } : undefined,
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),
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},
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onProgress ? { onProgress } : undefined,
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);
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return jsonToolResult(projectSlQueryResult(result, input.include));
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},
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);
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}
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@ -110,7 +110,10 @@ interface KtxSemanticLayerReadResponse {
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yaml: string;
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}
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interface KtxSemanticLayerQueryResponse {
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/** @internal */
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export interface KtxSemanticLayerQueryResponse {
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connectionId?: string;
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dialect?: string;
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sql: string;
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headers: string[];
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rows: unknown[][];
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@ -167,7 +167,7 @@ async function wikiCandidates(
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query: input.query,
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userId: options.userId,
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embeddingService: options.embeddingService ?? null,
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limit: Math.max(input.limit ?? 15, 25),
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limit: Math.max(input.limit ?? 10, 25),
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});
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const records: CandidateRecord[] = [];
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for (const result of searchResults) {
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@ -421,7 +421,8 @@ function hydrate(
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}
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return {
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...ref,
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score: maxScore > 0 ? Number((candidate.score / maxScore).toFixed(6)) : 0,
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// 3 decimals is plenty for a relative-rank hint; 6 just spent bytes on noise.
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score: maxScore > 0 ? Number((candidate.score / maxScore).toFixed(3)) : 0,
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};
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})
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.filter((result): result is KtxDiscoverDataRef => result !== null);
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@ -433,7 +434,7 @@ export function createKtxDiscoverDataService(
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): { search(input: KtxDiscoverDataInput): Promise<KtxDiscoverDataResponse> } {
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return {
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async search(input) {
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const limit = Math.max(1, Math.min(input.limit ?? 15, 50));
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const limit = Math.max(1, Math.min(input.limit ?? 10, 50));
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const query = input.query.trim();
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if (!query) {
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return [];
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@ -28,7 +28,12 @@ You have access to KTX MCP tools for data discovery, semantic-layer analysis, ra
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- Read entity details before writing SQL against an unfamiliar table. Do not assume column names.
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- Treat `sql_execution` as read-only. Writes are rejected by the server.
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- Validate value mentions with `dictionary_search` instead of guessing case or spelling. Treat a `dictionary_search` miss as non-authoritative. The index is built from profile-sampled values, so a missing value may simply have been outside the sample. Follow up with `sql_execution` against the most plausible columns before concluding the value is absent.
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- When `connection_list` shows multiple connections, pass an explicit `connectionId` to every tool that takes one and where user intent pins a specific warehouse. Required: `entity_details`, `sl_read_source`, and `sql_execution`. Required when user intent is warehouse-specific, including wording like "in our warehouse" or "this warehouse": `memory_ingest`; without `connectionId`, the memory agent cannot update the semantic layer and the knowledge lands as wiki-only. Pass `connectionId` when intent pins a warehouse, otherwise omit for unscoped discovery: `sl_query`, `discover_data`, and `dictionary_search`. Never pass `connectionId` to `connection_list`, `wiki_search`, `wiki_read`, or `memory_ingest_status`. If intent is ambiguous for a required-or-scoped tool, ask the user which warehouse before calling.
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- `connectionId` scoping when `connection_list` shows multiple connections:
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- Always pass it: `entity_details`, `sl_read_source`, `sql_execution`.
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- Pass it when intent pins a warehouse, otherwise omit for unscoped discovery: `sl_query`, `discover_data`, `dictionary_search`.
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- `memory_ingest`: pass it for warehouse-specific knowledge (e.g. "in our warehouse"); without it the memory lands as wiki-only and cannot update the semantic layer.
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- Never pass it: `connection_list`, `wiki_search`, `wiki_read`, `memory_ingest_status`.
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- If scoping is required but intent is ambiguous, ask which warehouse before calling.
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- Show compact result tables for small outputs. For broad results, summarize the top findings and mention the applied limit.
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- Ask a concise clarification only when the metric, date range, entity, or grain is genuinely ambiguous and cannot be inferred from context.
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</rules>
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@ -65,7 +65,7 @@
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},
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"limit": {
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"default": 10,
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"description": "Maximum wiki pages to return. Defaults to 10.",
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"description": "Maximum wiki pages to return.",
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"type": "integer",
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"minimum": 1,
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"maximum": 50
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@ -307,7 +307,7 @@
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{
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"name": "sl_query",
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"title": "Semantic Layer Query",
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"description": "Execute a semantic-layer query and return rows, headers, generated SQL, and plan details. Example: sl_query({ connectionId: \"warehouse\", measures: [\"orders.order_count\"], dimensions: [{ field: \"orders.created_at\", granularity: \"month\" }] }).",
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"description": "Execute a semantic-layer query and return headers, rows, and total row count, plus correctness notes (e.g. compile-only or fan-out) when relevant. The generated SQL and full query plan are omitted by default; request them with include: [\"sql\"] and/or include: [\"plan\"]. Example: sl_query({ connectionId: \"warehouse\", measures: [\"orders.order_count\"], dimensions: [{ field: \"orders.created_at\", granularity: \"month\" }], include: [\"sql\"] }).",
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"inputSchema": {
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"type": "object",
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"properties": {
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@ -403,7 +403,7 @@
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},
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"direction": {
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"default": "asc",
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"description": "Sort direction: \"asc\" or \"desc\". Defaults to \"asc\".",
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"description": "Sort direction for this field.",
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"type": "string",
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"enum": [
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"asc",
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@ -418,15 +418,27 @@
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},
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"limit": {
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"default": 1000,
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"description": "Maximum rows to return. Defaults to 1000.",
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"description": "Maximum rows to return.",
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"type": "integer",
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"minimum": 0,
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"maximum": 9007199254740991
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},
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"include_empty": {
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"default": true,
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"description": "Whether to include empty dimension groups. Defaults to true.",
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"description": "Whether to include empty dimension groups.",
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"type": "boolean"
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},
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"include": {
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"default": [],
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"description": "Extra detail to attach to the response: \"sql\" for the generated SQL, \"plan\" for the full query plan.",
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"type": "array",
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"items": {
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"type": "string",
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"enum": [
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"plan",
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"sql"
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]
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}
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}
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},
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"required": [
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@ -443,9 +455,6 @@
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"dialect": {
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"type": "string"
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},
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"sql": {
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"type": "string"
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},
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"headers": {
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"type": "array",
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"items": {
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@ -462,6 +471,15 @@
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"totalRows": {
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"type": "number"
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},
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"notes": {
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"type": "array",
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"items": {
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"type": "string"
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}
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},
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"sql": {
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"type": "string"
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},
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"plan": {
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"type": "object",
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"propertyNames": {
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@ -471,7 +489,6 @@
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}
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},
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"required": [
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"sql",
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"headers",
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"rows",
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"totalRows"
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@ -1241,8 +1258,8 @@
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}
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},
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"limit": {
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"description": "Maximum refs to return. Defaults to 15.",
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"default": 15,
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"description": "Maximum refs to return.",
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"default": 10,
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"type": "integer",
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"minimum": 1,
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"maximum": 50
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@ -1396,7 +1413,7 @@
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"description": "Parser-validated read-only SQL, e.g. \"select count(*) from public.orders\"."
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},
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"maxRows": {
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"description": "Maximum rows to return. Defaults to 1000.",
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"description": "Maximum rows to return.",
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"default": 1000,
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"type": "integer",
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"minimum": 1,
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|
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@ -307,16 +307,12 @@ describe('createKtxMcpServer', () => {
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content: [
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{
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type: 'text',
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text: JSON.stringify(
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{
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headers: ['status', 'count'],
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headerTypes: ['text', 'bigint'],
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rows: [['paid', 42]],
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rowCount: 1,
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},
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null,
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2,
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),
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text: JSON.stringify({
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headers: ['status', 'count'],
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headerTypes: ['text', 'bigint'],
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rows: [['paid', 42]],
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rowCount: 1,
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}),
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},
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],
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structuredContent: {
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@ -598,6 +594,92 @@ describe('createKtxMcpServer', () => {
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);
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});
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it('sl_query default response omits plan and sql but keeps compile-only and fan-out notes', async () => {
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const fake = makeFakeServer();
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const semanticLayer: KtxSemanticLayerMcpPort = {
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readSource: vi.fn(),
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query: vi.fn<KtxSemanticLayerMcpPort['query']>().mockResolvedValue({
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connectionId: 'warehouse',
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dialect: 'postgres',
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sql: 'select count(*) from public.orders',
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headers: ['order_count'],
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rows: [],
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totalRows: 0,
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plan: {
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sources_used: ['orders'],
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has_fan_out: true,
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fan_out_description: 'orders fans out across line_items',
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execution: { mode: 'compile_only', reason: 'No execution adapter configured.' },
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},
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}),
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};
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createKtxMcpServer({
|
||||
server: fake.server,
|
||||
userContext: { userId: 'local-user' },
|
||||
contextTools: { semanticLayer },
|
||||
});
|
||||
|
||||
const result = await getTool(fake.tools, 'sl_query').handler({
|
||||
connectionId: 'warehouse',
|
||||
measures: ['orders.order_count'],
|
||||
});
|
||||
|
||||
expect(result).toMatchObject({
|
||||
structuredContent: {
|
||||
connectionId: 'warehouse',
|
||||
dialect: 'postgres',
|
||||
headers: ['order_count'],
|
||||
rows: [],
|
||||
totalRows: 0,
|
||||
notes: ['No execution adapter configured.', 'orders fans out across line_items'],
|
||||
},
|
||||
});
|
||||
const structured = (result as { structuredContent: Record<string, unknown> }).structuredContent;
|
||||
expect(structured.sql).toBeUndefined();
|
||||
expect(structured.plan).toBeUndefined();
|
||||
});
|
||||
|
||||
it('sl_query attaches sql and plan only when include requests them', async () => {
|
||||
const fake = makeFakeServer();
|
||||
const plan = { sources_used: ['orders'], execution: { mode: 'executed' } };
|
||||
const semanticLayer: KtxSemanticLayerMcpPort = {
|
||||
readSource: vi.fn(),
|
||||
query: vi.fn<KtxSemanticLayerMcpPort['query']>().mockResolvedValue({
|
||||
connectionId: 'warehouse',
|
||||
dialect: 'postgres',
|
||||
sql: 'select count(*) from public.orders',
|
||||
headers: ['order_count'],
|
||||
rows: [[3]],
|
||||
totalRows: 1,
|
||||
plan,
|
||||
}),
|
||||
};
|
||||
|
||||
createKtxMcpServer({
|
||||
server: fake.server,
|
||||
userContext: { userId: 'local-user' },
|
||||
contextTools: { semanticLayer },
|
||||
});
|
||||
|
||||
const result = await getTool(fake.tools, 'sl_query').handler({
|
||||
connectionId: 'warehouse',
|
||||
measures: ['orders.order_count'],
|
||||
include: ['plan', 'sql'],
|
||||
});
|
||||
|
||||
expect(result).toMatchObject({
|
||||
structuredContent: {
|
||||
sql: 'select count(*) from public.orders',
|
||||
plan,
|
||||
rows: [[3]],
|
||||
totalRows: 1,
|
||||
},
|
||||
});
|
||||
const structured = (result as { structuredContent: Record<string, unknown> }).structuredContent;
|
||||
expect(structured.notes).toBeUndefined();
|
||||
});
|
||||
|
||||
it('entity_details rejects sql-style schema table ref aliases', async () => {
|
||||
const fake = makeFakeServer();
|
||||
const entityDetails = makeAllContextTools().entityDetails!;
|
||||
|
|
@ -798,7 +880,7 @@ describe('createKtxMcpServer', () => {
|
|||
connectionId: '00000000-0000-4000-8000-000000000001',
|
||||
}),
|
||||
).resolves.toEqual({
|
||||
content: [{ type: 'text', text: JSON.stringify({ runId: 'run-1' }, null, 2) }],
|
||||
content: [{ type: 'text', text: JSON.stringify({ runId: 'run-1' }) }],
|
||||
structuredContent: { runId: 'run-1' },
|
||||
});
|
||||
expect(ingest.ingest).toHaveBeenCalledWith({
|
||||
|
|
@ -825,21 +907,17 @@ describe('createKtxMcpServer', () => {
|
|||
content: [
|
||||
{
|
||||
type: 'text',
|
||||
text: JSON.stringify(
|
||||
{
|
||||
runId: 'run-1',
|
||||
status: 'done',
|
||||
stage: 'done',
|
||||
done: true,
|
||||
captured: { wiki: ['revenue'], sl: [], xrefs: [] },
|
||||
error: null,
|
||||
commitHash: 'abc123',
|
||||
skillsLoaded: ['wiki_capture'],
|
||||
signalDetected: true,
|
||||
},
|
||||
null,
|
||||
2,
|
||||
),
|
||||
text: JSON.stringify({
|
||||
runId: 'run-1',
|
||||
status: 'done',
|
||||
stage: 'done',
|
||||
done: true,
|
||||
captured: { wiki: ['revenue'], sl: [], xrefs: [] },
|
||||
error: null,
|
||||
commitHash: 'abc123',
|
||||
skillsLoaded: ['wiki_capture'],
|
||||
signalDetected: true,
|
||||
}),
|
||||
},
|
||||
],
|
||||
structuredContent: {
|
||||
|
|
@ -1047,19 +1125,15 @@ describe('createKtxMcpServer', () => {
|
|||
content: [
|
||||
{
|
||||
type: 'text',
|
||||
text: JSON.stringify(
|
||||
{
|
||||
connections: [
|
||||
{
|
||||
id: '00000000-0000-4000-8000-000000000001',
|
||||
name: 'Warehouse',
|
||||
connectionType: 'POSTGRES',
|
||||
},
|
||||
],
|
||||
},
|
||||
null,
|
||||
2,
|
||||
),
|
||||
text: JSON.stringify({
|
||||
connections: [
|
||||
{
|
||||
id: '00000000-0000-4000-8000-000000000001',
|
||||
name: 'Warehouse',
|
||||
connectionType: 'POSTGRES',
|
||||
},
|
||||
],
|
||||
}),
|
||||
},
|
||||
],
|
||||
structuredContent: {
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue