MCP server for RAG-enabled knowledge base management with ChromaDB, supporting web scraping, GitHub repository ingestion, document summarization, and conversational AI
Static source inference · medium confidence · detected: Logging
Deprecated protocol patterns detected
Summary
The server defines 10 tools with solid naming conventions and reasonable parameter schemas. However, critical gaps exist: (1) Most tool descriptions are present but lack the depth needed for LLM optimization, many are under 150 chars, missing context about WHEN to use them or what prerequisites apply. (2) Output schemas are NOT documented anywhere in the visible source code, the rubric requires documented return types for A-tier quality. (3) Error handling guidance is absent, tools provide no recovery steps or categorization. (4) Security considerations are under-specified: sensitive operations like scrape_web_documentation and scrape_github_repo lack permission declarations or destructive hints. (5) Parameter descriptions are minimally present but lack constraints, e.g., max_depth, max_concurrent, chunk_size have no ranges or rationale. (6) No tool explicitly declares idempotency or retry safety, critical for autonomous agent composition. Average across 10 tools yields 62; this is solidly below the 70+ threshold for production recommendation.
Output schemas are NOT documented for any tool. The rubric (Section D) requires documented return types for A-tier quality. LLMs cannot plan downstream tool calls or extract data without knowing what fields to expect.
Document the output schema for EVERY tool. For list_collections, document: {collections: [{name: string, count: integer, metadata: object}]}. For query_collection: {results: [{id: string, score: number, content: string}], total_count: integer}. Output schemas are non-negotiable for A-tier quality.
Add min/max bounds to ALL numeric parameters. E.g., 'n_results: integer, range 1 - 100 (default 3)', 'max_concurrent: integer, range 1 - 10 (default 3)', 'similarity_threshold: number, range 0.0 - 1.0 (default 0.0)'. Use parameter validation rules in descriptions.
Extend tool descriptions to 150 - 250 characters. Example: 'Search a collection with RAG (embeddings-based semantic search). Use this to find relevant documents before asking follow-up questions. Returns document chunks ranked by similarity. Similarity ranges 0 - 1; threshold 0.7+ filters high-confidence results.'
Add error handling guidance to all tool descriptions. E.g., 'If the collection does not exist, try list_collections first. If results are sparse, lower the similarity_threshold or use query_collection instead of rag_chat for more control.'
Declare scope/permission requirements for WRITE and external-network tools. E.g., 'Requires: write:chromadb, network:external. Rate limited to 10 requests/min.'
Error handling and recovery guidance is absent. Tools provide no actionable error messages. E.g., if scrape_web_documentation times out or if a collection doesn't exist, the LLM receives no guidance on what to try next or whether to retry.
Destructive/external operations lack permission/scope declarations. scrape_web_documentation and scrape_github_repo perform network requests and write to ChromaDB but do not declare required permissions or scopes. LLMs cannot be configured with least-privilege access.
No tool annotations (idempotentHint, destructiveHint, readOnlyHint). The MCP spec supports per-tool metadata to help LLMs reason about safety and retry behavior. All WRITE tools are unmarked, and all READ tools are unmarked. This forces LLMs to infer safety from descriptions.
Tool descriptions are under 150 characters on average, missing LLM-critical context: WHEN to use the tool, prerequisites, and output format. E.g., 'Get current RAG settings' tells the LLM nothing about when to call it vs other tools.
No pagination or result limits documented. query_collection defaults to n_results=3 but no guidance on typical large-scale responses or maximum safe result count. list_collections provides no limit/offset, risking unbounded responses.
No confirmation or dry-run pattern for irreversible operations. scrape_web_documentation, scrape_github_repo, summarize_document, and update_rag_config are WRITE operations that agents should confirm before executing. Missing dry-run capability invites mistakes.
Implement result pagination for list_collections. Add optional parameters: limit (1 - 100, default 20), offset (default 0), and return: {collections: [...], total_count: integer, limit: integer, offset: integer}.
Add a dry-run mode to update_rag_config and scrape_* tools. E.g., 'dry_run: boolean (default false), if true, validate inputs and return a summary without applying changes.' This lets agents plan safely.
For scrape_web_documentation and scrape_github_repo, add timeout and max_pages parameters. E.g., 'timeout_seconds: integer, range 10 - 300 (default 60), max_pages: integer, range 1 - 1000 (default 100). Prevent runaway scrapes that exhaust quotas or context.'
Document idempotency for each tool. E.g., 'list_collections is idempotent; safe to retry on timeout. scrape_github_repo is NOT idempotent; repeated calls append duplicate content, use dry_run to check before executing.'
Add rag_n_results context to query_collection and rag_chat descriptions: 'Higher rag_n_results (5 - 20) provides more context but slower queries; lower (1 - 3) is faster but may miss relevant docs.'
For summarize_document, document: output format (hierarchical summary structure?), storage location (PostgreSQL table name?), and whether calling summarize_document twice on the same file updates or duplicates the summary.