RAT MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| CLAUDE_MODEL | No | Claude model for responses | anthropic/claude-3.5-sonnet:beta |
| DEEPSEEK_MODEL | No | DeepSeek model for reasoning | deepseek/deepseek-r1 |
| OPENROUTER_API_KEY | Yes | OpenRouter API key for both DeepSeek and Claude models |
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
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| generate_responseC | Generate a response using DeepSeek's reasoning and Claude's response generation through OpenRouter. |
| check_response_statusB | Check the status of a response generation task |
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
The two tools have clearly distinct purposes: one checks the status of a response generation task, while the other initiates the generation of a response. There is no overlap or ambiguity between these functions, making it easy for an agent to select the correct tool.
Both tools follow a consistent verb_noun naming pattern (check_response_status and generate_response), using snake_case throughout. The verbs 'check' and 'generate' appropriately describe their actions, and there are no deviations in style or convention.
With only 2 tools, the server feels thin for its apparent purpose of response generation through OpenRouter. A more complete surface might include tools for managing tasks, handling errors, or configuring parameters, but the current set is minimal and may limit agent workflows.
The tool surface is severely incomplete for response generation tasks. While it covers initiating and checking status, it lacks tools for canceling tasks, retrieving results beyond status, handling errors, or managing task history. This will likely cause agent failures in more complex scenarios.