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sjtu_models

Retrieve the list of available models from the SJTU Zhiyuan No.1 API for use in Claude Code and Codex.

Instructions

List available models from the SJTU endpoint.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The tool handler function for 'sjtu_models'. Decorated with @mcp.tool(), it calls client.list_models() to fetch available models from the SJTU API.
    @mcp.tool()
    async def sjtu_models() -> list[dict[str, Any]]:
        """List available models from the SJTU endpoint."""
        return await client.list_models()
  • Registration of 'sjtu_models' as an MCP tool via the @mcp.tool() decorator on the FastMCP instance 'mcp'.
    @mcp.tool()
  • The function signature shows no input parameters and returns a list of dictionaries (model data).
    async def sjtu_models() -> list[dict[str, Any]]:
  • The SjtuClient.list_models() helper method that makes the HTTP GET request to /models endpoint and extracts the 'data' field from the JSON response.
    data = response.json()
    return data.get("data", [])
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description should disclose behavioral traits. It only states the action (list) but does not explain that it is a read-only operation, any potential side effects, or required permissions. The agent has no additional context beyond the basic purpose.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence with no unnecessary words. It conveys the core functionality efficiently.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has no parameters, the description is somewhat adequate but lacks usage context. It does not explain how the output schema relates to usage or provide hints for integration with sibling tools. The presence of an output schema mitigates the need for return value details, but the description could be more helpful by mentioning use cases.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

There are no parameters, so schema coverage is trivially 100%. The description adds no parameter info, which is acceptable as there is nothing to describe. Baseline 4 for zero parameters is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb (List), resource (available models), and source (SJTU endpoint). It effectively differentiates from sibling tools like sjtu_cheap_task, sjtu_text, and sjtu_vision, which target different operations.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance provided on when to use this tool versus alternatives. There is no mention of prerequisites, context, or conditions for using sjtu_models.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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