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Test a reply against checks

modelfinder_check_output
Read-onlyIdempotent

Runs deterministic checks on one model reply and reports which fail, so you can debug eval checks or routing guards before a full run.

Instructions

Run deterministic checks (contains, not_contains, exact, regex, json_keys, max_chars) on one model reply and report which failed. Use it to design and debug eval checks or routing guards against a sample reply before spending money on a run; to score a whole eval run use modelfinder_rank_eval_results. Read-only and local: no network calls, no files written. Returns passed, failures (check and reason) and checks_run.

Input Schema

NameRequiredDescriptionDefault
textYesThe model reply to test.
checksYesDeterministic checks; all must pass. Types: contains, not_contains, exact (value, ignore_case?), regex (pattern), json_keys (keys), max_chars (value). Example: [{"type": "json_keys", "keys": ["category"]}, {"type": "contains", "value": "refund"}]
response_formatNomarkdown (default) for a readable summary, json for the full machine-readable result.markdown

Output Schema

NameRequiredDescriptionDefault
passedYes
failuresYes
checks_runYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.3.5

TDQS

A4.5/5.0
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, but the description adds cost context ('before spending money on a run') and states 'no network calls, no files written', which sharpens the local/no-side-effect profile. It also names the response fields (passed, failures, checks_run), though that partly duplicates the 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.

Conciseness5/5

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

Three tight sentences, front-loaded with the action, then the usage routing, then the side-effect and return summary. No filler or restated title.

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?

With an output schema, full parameter coverage and rich annotations, the description only needs to establish purpose, routing and safety, all of which it does. Nothing an agent needs to call this correctly is missing.

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% and the schema already enumerates the check types, per-type fields (value, pattern, keys, ignore_case) and the 1-50 item bound. The description's list of check types only echoes the enum, adding no syntax or format detail beyond the schema, so the baseline 3 applies.

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?

Names a specific verb and resource: 'Run deterministic checks ... on one model reply and report which failed', and enumerates the check types. It also distinguishes itself from modelfinder_rank_eval_results, so an agent can pick 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?

States the use case ('design and debug eval checks or routing guards against a sample reply before spending money on a run') and names the alternative for the other case ('to score a whole eval run use modelfinder_rank_eval_results'). The when-to-use and when-to-use-something-else conditions are both explicit.

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