mcp-agentic-rag
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| FIRECRAWL_API_KEY | Yes | Your Firecrawl API key for web fallback |
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 |
|---|---|
| query_ragD | Query the RAG knowledge base |
| query_rag_with_fallbackC | Query the RAG knowledge base with web fallback |
| seed_faqA | Seed the knowledge base with the ML FAQ dataset |
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 3 tools
The two query tools are distinguished by the fallback feature, and seed is clearly separate. Some confusion possible but descriptions help.
All tools follow a consistent snake_case verb_noun pattern, making naming predictable and logical.
With only 3 tools, the set feels minimal but is within acceptable bounds for a simple RAG server. However, missing management tools make it slightly thin.
The toolset lacks capabilities to update, delete, or list documents, which are essential for a complete RAG knowledge base management. This is a significant gap.