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by Abhiindigloo
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Validate an eval config

modelfinder_validate_eval_config
Read-onlyIdempotent

Validate an existing eval config before a paid run by checking schema, secret keys, HTTPS endpoints, placeholders, and checks; returns problems, estimated API calls, and required env vars.

Instructions

Check an existing eval config before a paid run: schema, no keys in the config, HTTPS endpoints, no template placeholders, valid checks. Use on a config the user wrote or edited; to build a new one use modelfinder_create_eval_config. Read-only and local: no network calls, no files written. Returns valid, problems, the number of API calls the run would make, required environment variable names, unpriced models and whether the personal-data guard would block the run.

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": {
      "config": "<config>"
    },
    "name": "modelfinder_validate_eval_config"
  }
}

Input Schema

NameRequiredDescriptionDefault
configYesThe eval config object (as saved for eval_models.py).
response_formatNomarkdown (default) for a readable summary, json for the full machine-readable result.markdown

Output Schema

NameRequiredDescriptionDefault
validYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.3.5

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description still adds real value by stating 'no network calls, no files written' and by enumerating the specific validation checks and the guard behavior, which the annotations cannot express. It loses a point only because the return-value sentence overlaps with the existing output schema.

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

Conciseness4/5

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

Single front-loaded paragraph that leads with the action and follows with the sibling routing, then behavior. It is dense but not padded; the only weak element is the trailing return-value list, which partly restates the output schema.

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

Completeness5/5

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

For a two-parameter, read-only validation tool with an output schema, the description covers purpose, routing, side-effect profile, and the categories of problems it surfaces, including the personal-data guard and unpriced models. An agent has everything needed to decide to call it and to interpret the outcome.

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% and both parameters are documented in the schema, so the baseline is 3. The description adds only that the config is the one 'the user wrote or edited' and says nothing about response_format or the expected config shape beyond what the schema already states.

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?

States a specific verb ('Check') and resource ('eval config') and enumerates the actual validations performed: schema, key leakage, HTTPS endpoints, template placeholders, and checks. It explicitly names the sibling it is not (modelfinder_create_eval_config), so the agent can distinguish it without opening either schema.

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

Usage Guidelines5/5

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

Gives an explicit when-to-use ('on a config the user wrote or edited') and a when-not with the alternative ('to build a new one use modelfinder_create_eval_config'). The 'before a paid run' framing also tells the agent the sequencing context.

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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