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Rate match results

rate_matches
Read-onlyIdempotent

Computes player skill ratings from match results using OpenSkill, Glicko-2, or Elo, supporting 1v1, team, free-for-all, and draw outcomes; pass prior ratings back in to continue.

Instructions

Compute player skill ratings from match results with OpenSkill, Glicko-2 or Elo. Handles 1v1, teams, free-for-alls and draws. Stateless: pass earlier ratings back in as players to continue.

Input Schema

NameRequiredDescriptionDefault
paramsNoAlgorithm parameter overrides, e.g. {"k": 24} for Elo
matchesYes
playersNoRatings from a previous rate_matches call, keyed by player id; unknown players start at the default
algorithmNoopenskill (default, Weng-Lin, for teams and free-for-alls), glicko2, elo, or custom (Club Points)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint and openWorldHint=false, so the safety profile is covered. The description usefully adds that the operation is stateless and how to continue a rating history, but it says nothing about the returned payload (per-player mu/sigma/games), error behavior, or the 5000-match input cap — details an agent cannot get anywhere else since there is no 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 short sentences with zero waste: capability first, supported scenarios second, the non-obvious statelessness contract last. Everything is front-loaded and nothing repeats the name or title.

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 nested, multi-algorithm batch tool the description covers what matters for correct invocation, and the rich input schema supplies the field-level detail. The main residual gap is the absence of any statement about the return value, which matters given no output schema exists.

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 75% and the schema already documents ranks, teams, weight, winner and the algorithm enum. The description reinforces that `players` carries prior ratings, but adds no new semantics for the `params` override object or the per-match fields beyond what the schema states.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Compute player skill ratings from match results') and narrows the supported formats and algorithms (1v1, teams, free-for-alls, draws; OpenSkill/Glicko-2/Elo). It implicitly separates itself from predict_match by framing the work as deriving ratings from completed results, but it never explicitly names that sibling, so an agent weighing the two must infer the split.

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?

Usage is implied rather than stated: use it when you have finished match results and need updated ratings, and re-feed prior ratings via `players` to chain calls. There is no explicit when-not guidance and no mention of the obvious alternatives (predict_match for hypothetical outcomes, list_rating_algorithms for choosing an algorithm).

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