mcp-docqa-server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| DOCQA_STORE | No | Vector store backend: 'sqlite' (default) or 'pgvector' | |
| DATABASE_URL | No | PostgreSQL connection string for pgvector backend (e.g., postgresql://user:pass@host:5432/dbname) | |
| OPENAI_API_KEY | No | OpenAI API key for semantic embeddings (optional; if set, will be used) | |
| DOCQA_EMBEDDINGS | No | Embedding model: 'auto' (auto-detect from .env), 'openai', or 'hash' (default) |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| pingA | Health check. Returns 'pong' to confirm the docqa server is reachable. Use this to verify the connection between the host and this server. |
| search_documentsA | Search the indexed document corpus. Returns the k best-matching text chunks, each with its source metadata: doc_id, chunk_index, title, url, the chunk text, and a relevance score (higher is better; scores are comparable within one response, not across modes). Results may include multiple chunks from the same document. Use fetch_document with a result's doc_id to read the full source document. Args: query: A natural-language question or search phrase. k: How many chunks to return (default 5, max 25). mode: 'hybrid' (default) fuses semantic and keyword search — best for most questions. 'vector' is semantic-only — best for paraphrased or conceptual questions. 'lexical' is keyword-only — best when the exact term must appear (identifiers, drug names, error codes). |
| fetch_documentA | Fetch one full source document by its id. Use this after search_documents to read a promising source in full instead of reasoning from a chunk. Returns id, title, url, and the complete text. Args: doc_id: The document id exactly as returned by search_documents. |
| corpus_statsA | Describe the indexed corpus: document/chunk counts, storage backend, and which embedding model built the index. Call this first if you are unsure whether the corpus is relevant to the user's question or whether anything has been indexed at all. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 4 tools
Each tool serves a unique, clearly defined purpose: ping for health check, corpus_stats for corpus metadata, search_documents for querying, and fetch_document for retrieving full documents. No functional overlap exists.
Three tools follow the verb_noun pattern (corpus_stats, fetch_document, search_documents), while 'ping' is a standalone verb. This minor deviation is acceptable as ping is a standard health check command.
With 4 tools, the server is well-scoped for a document QA system: health check, corpus statistics, search, and document retrieval. Each tool is essential and none are extraneous.
The tool set covers the core workflow of searching and fetching documents. A minor gap is the absence of a tool to list or browse documents, but for the stated purpose of answering questions via search, it is sufficient.