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Glama

Predict a matchup

predict_match
Read-onlyIdempotent

Predict each team's win probability and match quality from current ratings to compare lineups and assess how evenly matched a game is.

Instructions

Predict each team's win probability and the match quality (how even it is) from current ratings.

Input Schema

NameRequiredDescriptionDefault
teamsYes
paramsNoAlgorithm parameter overrides, e.g. {"k": 24} for Elo
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

B3.4/5.0
Behavior3/5

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

Annotations already declare readOnly, idempotent, and closed-world, so the safety profile is covered. The description adds that the prediction is a pure function of current ratings (no rating mutation), which is useful, but says nothing about what happens with unrated/unknown players or whether results are deterministic across algorithms.

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?

A single well-formed sentence with the action and outputs front-loaded and no filler. It is lean to the point of omitting input requirements, but nothing in it is wasted.

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?

For a 4-parameter tool with nested objects and no output schema, the description covers the conceptual output but leaves gaps: it never explains the nested teams array, the players payload requirement, or how algorithm choice affects results. Adequate but incomplete.

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?

At 75% schema coverage the schema already documents params, players, and algorithm. The description adds only the interpretive detail that teams produce one probability per team and that quality measures evenness; it does not clarify the nested team-of-players structure or the meaning of the free-form params overrides.

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 (predict) and precisely names the outputs: each team's win probability plus match quality. That distinguishes it functionally from the rating-update sibling rate_matches, though the description never names that sibling explicitly.

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?

'from current ratings' implies the prerequisite that ratings must already exist, and the players schema hints at a prior rate_matches call, but the description itself gives no explicit when-to-use or when-not-to-use guidance relative to rate_matches or list_rating_algorithms.

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