Skip to main content
Glama
Sign In
BlindWrite MCP

BlindWrite MCP

by ygstudio-game
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

benchmark_get_leaderboard

Retrieve personal or global model rankings using Bradley-Terry MLE, Elo, or win-rate metrics. Filter by writing category and minimum battles to identify top-performing models.

Instructions

Get personal or global model rankings calculated with Bradley-Terry Maximum Likelihood Estimation and Elo.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "benchmark_get_leaderboard"
  }
}

Input Schema

NameRequiredDescriptionDefault
scopeNoRankings scopepersonal
metricNoRanking algorithmbradley_terry
categoryNoFilter leaderboard by writing category
min_battlesNoMinimum battles required to appear on the leaderboard

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.7/5.0
Behavior3/5

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

No annotations are provided, so the description carries the behavioral disclosure burden. It discloses the ranking methodology (Bradley-Terry and Elo), which is useful, and 'Get' implies a read operation. However, it does not state whether authentication is needed for 'personal' scope, what the response format is, or whether any side effects occur.

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?

The description is a single, front-loaded sentence with no wasted words. It states the action and key scoping information immediately and remains focused on what the tool does.

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

Completeness3/5

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

The description is adequate for a straightforward leaderboard retrieval tool, but it lacks context about return values, default metrics, and whether 'personal' requires caller identity. With no output schema and no annotations, the agent must infer several important details from the parameter defaults and examples.

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%, so the parameters are already documented. The description adds semantic context for 'scope' and 'metric' by mentioning personal/global and Bradley-Terry/Elo, but it does not add meaning for 'category' or 'min_battles'. This matches the baseline of 3 for full schema coverage.

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 uses a specific verb ('Get') and resource ('personal or global model rankings'), making the tool's purpose immediately clear. It also distinguishes itself from siblings like benchmark_get_model_stats and benchmark_compare_models by focusing on leaderboard rankings rather than stats or comparisons.

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?

The description implies the tool should be used when the agent needs leaderboard rankings, but it provides no explicit when-to-use guidance or alternatives. There is no mention of when to prefer this over benchmark_get_model_stats or benchmark_compare_models, leaving the choice mostly to inference from the tool name.

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

Deploy Server

Other Tools