cn-llm-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| CN_LLM_MODEL | No | The model name for the provider (e.g., deepseek-chat, qwen-plus) | |
| CN_LLM_API_KEY | Yes | Your API key for the LLM provider | |
| CN_LLM_BASE_URL | No | Base URL for custom OpenAI-compatible API (only needed when provider=custom) | |
| CN_LLM_PROVIDER | No | The LLM provider. Supported values: deepseek, qwen, kimi, zhipu, doubao, custom |
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
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| run_cn_modelA | Delegate a small, low-risk task to a Chinese LLM provider. Best for drafts, summaries, simple code generation, and mechanical edits. The supervising agent must review the result before using it. |
| draft_code_patchA | Ask a Chinese LLM provider to draft a minimal unified diff for a small code task. Use this only for low-risk changes, then inspect and test the patch before applying it. |
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 for drafting code patches and one for general low-risk tasks. No overlap in functionality.
Both tools use a consistent verb_noun pattern in snake_case (draft_code_patch, run_cn_model), making the naming predictable.
With only 2 tools, the server feels minimal but still focused. The count is borderline for the scope but not extreme.
The tool surface is severely incomplete for delegating tasks to a Chinese LLM; missing operations like listing models, managing tasks, or retrieving results beyond the initial call.