Deepseek-Thinking-Claude-3.5-Sonnet-CLINE-MCP
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 task, while the other initiates the task itself. There is no overlap or ambiguity between monitoring and execution functions.
Both tools follow a consistent verb_noun pattern (check_response_status, generate_response) with clear action-oriented names. The naming is uniform and predictable across the set.
With only 2 tools, the server feels thin for its apparent scope of AI response generation with reasoning and status tracking. This minimal set may force agents to work around missing operations like error handling or configuration adjustments.
The toolset is severely incomplete for a response generation service. It lacks essential operations such as canceling tasks, retrieving task history, configuring generation parameters, or handling errors, which are typical in such AI workflow domains.