Local RAG MCP

Local RAG MCP

When to use

Use when the task requires semantic search, document ingestion, or querying a local vector database for context retrieval, and an appropriate MCP server is available.

Requirements / Checks

• Verify if a local RAG MCP server is configured in the environment (e.g., via apx mcp list).
• Do NOT attempt to install or spin up Docker containers for vector databases without explicit user permission.

Workflow

1. Identify Need: Determine if a query requires semantic retrieval vs. standard grep/glob.
2. Check Configuration: Verify the connection to the MCP RAG server.
3. Inventory Corpus: Use status/list tools if available before ingesting anything.
4. Ingest (if necessary): Use file ingestion for approved files and string ingestion for approved fetched/web/clipboard content with clear source metadata.
5. Query: Preserve exact user terms, add disambiguating context, and keep result limits small first.
6. Expand: If a hit lacks surrounding context, fetch neighboring chunks before drawing conclusions.
7. Clean Up: Delete stale or incorrectly ingested sources when requested.
8. Synthesize: Incorporate retrieved context with citations to source/file and chunk.

Tool Model To Preserve

• query_documents: keyword + semantic query; lower score means stronger match.
• ingest_file: absolute-path document ingestion.
• ingest_data: string/HTML/Markdown ingestion with source + format metadata.
• delete_file: remove ingested file/source.
• list_files and status: corpus inventory and DB health.
• read_chunk_neighbors: expand around a search hit.

Safety Constraints

• Do NOT ingest sensitive personal data, secrets, or .env files into the local RAG store.
• Warn the user if the local RAG implementation relies on external API calls for embeddings (e.g., sending data to OpenAI).
• Do not ingest whole repos by default; start with approved docs or scoped folders.
• Do not rely on semantic hits without reading neighbors/source when precision matters.

Validation / Done Criteria

• Relevant context is successfully retrieved.
• Ingestion explicitly excludes sensitive paths.
• Query result synthesis distinguishes retrieved evidence from inference.

References

• references/rag-tool-model.md