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mcp-llm-eval

mcp-llm-eval

by berkayildi
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

evaluate_retrieval

Run retrieval metrics like recall@k, precision@k, MRR, and nDCG@k on labelled datasets with a configurable adapter. Returns per-query and aggregate scores plus latency percentiles.

Instructions

Run retrieval metrics (recall@k, precision@k, MRR, nDCG@k) against a labelled dataset with a configurable retrieval adapter. Returns per-query metrics, dataset-level aggregate, and p50/p95 retrieval latency.

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": {
      "dataset_path": "<dataset_path>",
      "corpus_path": "<corpus_path>"
    },
    "name": "evaluate_retrieval"
  }
}

Input Schema

NameRequiredDescriptionDefault
kNoTop-k cutoff for all metrics (default 5).
adapterNoRetrieval adapter to use (default bm25).bm25
output_dirNoDirectory to save results (optional).
corpus_pathYesPath to JSONL corpus file.
dataset_pathYesPath to JSONL dataset with relevant_chunk_ids on each entry.

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?

Without annotations, the description adds some behavioral context (returns metrics, latency), but does not disclose side effects, permissions, or safety properties. A higher score would require explicit read-only or destructive hints.

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?

Two crisp sentences, front-loaded with the tool's purpose and output summary. No unnecessary words.

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?

Covers core metrics and outputs (per-query, aggregate, latency). Missing details on JSONL format expectations beyond 'relevant_chunk_ids', but sufficient for a run tool without output schema.

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 coverage is 100%, baseline 3. The description adds context about 'relevant_chunk_ids' and configurable adapter but does not significantly deepen understanding 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 explicitly states running specific retrieval metrics (recall@k, precision@k, MRR, nDCG@k) against a labelled dataset, clearly distinguishing it from sibling tools like compare_runs or evaluate_rag_end_to_end.

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?

No explicit guidance on when to use versus alternatives. The description implies usage for retrieval evaluation but does not define prerequisites or when to avoid this tool.

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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