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mcp-llm-eval

mcp-llm-eval

by berkayildi
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

get_evaluation

Retrieve per-question per-model scores, responses, and judge reasoning from a specific evaluation run.

Instructions

Retrieve the full details of a specific evaluation run: per-question per-model scores, responses, and judge reasoning.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "results_path": "<results_path>"
    },
    "name": "get_evaluation"
  }
}

Input Schema

NameRequiredDescriptionDefault
results_pathYesPath to a specific evaluation result file.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.9.2

TDQS

A3.8/5.0
Behavior3/5

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

No annotations are provided, so the description carries full burden. It indicates a read operation but does not disclose potential side effects, permissions, or output format. It is not contradictory but could be more transparent about what the agent can expect.

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, well-structured sentence that front-loads the action and delivers specific details without any waste. Every word adds value.

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

Completeness4/5

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

For a simple retrieval tool with one parameter and no output schema, the description adequately explains what the tool returns. It could optionally mention return format, but the described contents ('scores, responses, reasoning') provide sufficient context for an agent to decide to invoke it.

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

Parameters3/5

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

Schema description coverage is 100% for the single parameter 'results_path'. The description adds detail about what the tool returns (scores, responses, reasoning) but does not add new meaning to the parameter itself beyond the schema's description of it as a file path.

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 it retrieves full details of an evaluation run, including specific contents like per-question per-model scores, responses, and judge reasoning. It distinguishes itself from siblings like 'list_evaluations' (which lists runs) and 'run_evaluation' (which creates runs).

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

Usage Guidelines3/5

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

The description implies use when you need detailed information about a specific evaluation run, but it does not provide explicit when-to-use or when-not-to-use guidance, nor does it mention alternatives like 'compare_runs' or 'list_evaluations' for different purposes.

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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