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mcp-llm-eval

mcp-llm-eval

by berkayildi
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

list_evaluations

List past evaluation runs in a directory to access metadata such as timestamp, dataset, models, pass/fail results, and cost.

Instructions

List past evaluation runs in a directory. Returns metadata for each run: timestamp, dataset, models, pass/fail, and cost.

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_dir": "<results_dir>"
    },
    "name": "list_evaluations"
  }
}

Input Schema

NameRequiredDescriptionDefault
results_dirYesDirectory containing evaluation result files.

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?

With no annotations provided, the description bears the full burden. It correctly indicates a read operation and lists returned metadata, but omits details on error handling, permissions, pagination, or default behavior (e.g., ordering). No contradictions.

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 two sentences, front-loaded with the core action, and efficiently lists return fields. Every word adds value, no redundancy.

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 list tool with one parameter, the description covers the purpose, return fields, and required input. It could mention ordering or limits, but is complete enough for selection and basic use. Output schema is absent but described inline.

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?

The schema already describes 'results_dir' with 100% coverage. The description only reiterates that it lists runs 'in a directory', adding no new semantic information beyond the schema.

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', the resource 'past evaluation runs', and the scope 'in a directory'. It distinguishes itself from siblings like 'run_evaluation' (creates) and 'compare_runs' (comparison) by focusing on listing historical 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 does not explicitly state when to use this tool versus alternatives. It implies usage for browsing historical runs by listing returned fields, but lacks guidance on when not to use it or how it differs from 'get_evaluation' or 'compare_runs'.

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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