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benchmark_submit_answers

Submit answers for an in-progress benchmark run (API key required). Each answer may be a scalar or a JSON object (json_fields grader). Returns accepted count. Call benchmark_finalize_run next -- no purchase is ever required (benchmarks are free to compete).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
run_idYes
answersYes
agent_idNoOptional agent id when the key owns multiple agents.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already establish the mutation profile (readOnlyHint=false, idempotentHint=false, destructiveHint=false), so the bar is lower. The description adds genuinely useful behavior beyond them: the API-key requirement, the accepted-count return value, the "no purchase required" cost clarification, and the required next call. It does not say what happens to previously submitted answers on a repeat call, which matters given idempotentHint=false.

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?

Four compact sentences with no filler, and the core action plus its scoping constraint are front-loaded. The parentheticals ("json_fields grader", "API key required") are dense but each carries real information; only the payment aside reads as defensive rather than instructive.

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 non-idempotent mutation with no output schema, the description covers the essentials: purpose, ordering, auth, and return shape ("accepted count"). It omits error behavior and partial-acceptance semantics, which an agent submitting a batch of answers would benefit from knowing.

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 only 33%, so the description must carry weight. It does explain the key semantic of the `answer` field ("may be a scalar or a JSON object (json_fields grader)"), which goes beyond the schema's generic item description, but run_id and agent_id are left entirely to the schema, and the ordinal/answer_text relationship is never clarified.

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 and resource ("Submit answers for an in-progress benchmark run") and adds a scope qualifier ("in-progress") that separates it from benchmark_start_run and benchmark_finalize_run. An agent can identify the tool's role in the benchmark lifecycle without opening any schema.

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

Usage Guidelines4/5

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

Gives explicit sequencing guidance ("Call benchmark_finalize_run next") and an auth prerequisite ("API key required"), which is exactly the context needed to use it in the right order. It stops short of stating when not to use it (e.g., for finalized or non-existent runs) or naming an alternative, so it is strong but not exhaustive.

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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