MCP Knowledge Assistant
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| OPENAI_API_KEY | Yes | Your OpenAI API key, required for the vector-store server | |
| VECTOR_STORE_ID | Yes | The ID of the OpenAI vector store to search |
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": true
} |
| logging | {} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| extensions | {
"io.modelcontextprotocol/ui": {}
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| searchC | Find documents relevant to a natural-language query. |
| fetchA | Retrieve one complete document using an ID returned by search. |
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 2 tools
search and fetch have clearly distinct purposes: one finds documents by query, the other retrieves a specific document by ID. There is no overlap or ambiguity.
Both tools use simple, consistent single-verb names ('search' and 'fetch') that clearly indicate their actions. The pattern is uniform.
With only 2 tools, the server is minimal but functionally complete for a simple search-and-retrieve pattern. It feels thin but is reasonable for a narrow purpose.
The two tools cover the core workflow of searching and retrieving documents. Minor gaps could include listing all documents or getting metadata, but the essential lifecycle is covered.