feat(mcp): add search_docs tool over docs corpus (closes #295) (#316)

* feat(mcp): add search_docs tool over Mintlify docs corpus

Closes #295. The docs at https://docs.dograh.com promise "Search the
Dograh docs for how to configure a TURN server" as an MCP example
prompt, but no search_docs tool exists in the MCP server — agents can
list workspace resources but cannot search the documentation.

This adds a dependency-free, in-process keyword search over the
`docs/` tree shipped into the API image (`COPY ./docs ./docs`):

- New `api/mcp_server/tools/docs_search.py` — async `search_docs(query,
  limit=10)` with weighted scoring (path > title > body), a 25-result
  hard cap, snippet extraction around the first term hit, and graceful
  empty-list degradation when docs aren't on disk. `DOGRAH_DOCS_PATH`
  env var overrides location discovery for non-Docker layouts.

- Registered in `api/mcp_server/server.py` alongside the other tools,
  keeping the existing list-alphabetical convention.

- `api/tests/test_mcp_docs_search.py` — 18 unit tests covering the
  pure helpers (tokenizer, frontmatter stripping, title extraction,
  scoring weights, URL building) and end-to-end ranking, limit
  clamping, empty-corpus degradation, and input-validation errors.
  Mocks `authenticate_mcp_request` to avoid the DB dependency,
  mirroring `test_mcp_save_workflow.py`.

Implementation notes:
- The docs corpus is ~100 files / ~140k LoC, so a per-call scan runs
  well under 50 ms; avoiding a vector index / embedding backend keeps
  the tool zero-dependency and works for fully offline self-hosted
  deployments.
- Authentication is required for consistency with the other MCP tools
  (and to route through the existing rate-limit middleware), even
  though docs are not org-scoped data.
- Title/path matches deliberately outweigh body matches so a page
  whose subject IS the query term outranks one that merely mentions
  it incidentally.

* feat: improve docs search

---------

Co-authored-by: Abhishek Kumar <abhishek@a6k.me>

api/mcp_server/instructions.py

@@ -16,6 +16,11 @@ You build and edit Dograh voice-AI workflows by emitting TypeScript that uses th
 
 ## Call order
 
+### Reading documentation
+1. `search_docs(query)` — use first for keyword or acronym lookup when the user is asking how Dograh works or how to configure something.
+2. `read_doc(path)` — fetch the full page once one result looks likely. Prefer this over reasoning from search summaries alone.
+3. `list_docs(path=None, depth=1)` — use when the user wants to browse a topic area or when search terms are too vague. Returned section paths feed back into `list_docs`; returned page paths feed into `read_doc`.
+
 ### Editing an existing workflow
 1. `list_workflows` — locate the target workflow.
 2. `get_workflow_code(workflow_id)` — fetch the current source.