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ygstudio-game

BlindWrite MCP

benchmark_start_duel

Start a randomized, blind A/B battle between two outputs for evaluation, keeping model identities masked to reduce bias.

Instructions

Start a randomized, blind A/B battle between two outputs for evaluation. Model identities remain masked.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
task_idYesThe benchmark task ID
output_a_idNoOptional specific anonymous output ID for Response A
output_b_idNoOptional specific anonymous output ID for Response B

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.0.0

TDQS

B3.4/5.0
Behavior3/5

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

With no annotations, the description carries the full burden, and it does disclose meaningful behavioral traits: randomization, blind evaluation, and masked model identities. However, it doesn't explain side effects, what the call returns, whether execution is asynchronous, or what happens when no output IDs are provided.

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 short sentences, both informative and non-wasteful. The core action is front-loaded and the key detail about hidden model identities comes immediately after, with no redundant material.

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

Completeness2/5

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

Despite a clear one-line purpose, the description is incomplete for a mutation-like state-changing tool. It doesn't state what the response contains, whether output IDs must come from a prior step, or how the duel flows into sibling voting/result tools. No annotations or output schema compensate for these gaps.

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 documents all three parameters with 100% coverage, so the baseline is appropriate. The description adds no new parameter-level detail beyond implying that two outputs are involved; the optional output IDs and task_id semantics are left to 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 names a specific action ('Start'), a specific resource ('a randomized, blind A/B battle'), and the evaluation purpose. It also distinguishes the tool from siblings like benchmark_compare_models and benchmark_submit_vote by emphasizing that it initiates a battle rather than analyzing, voting, or retrieving results.

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

Usage Guidelines2/5

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

No guidance is given about when to use this tool versus the many benchmark siblings. It doesn't mention prerequisites, how it relates to benchmark_generate_outputs or benchmark_submit_vote, or when an agent should choose it over benchmark_compare_models.

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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