Skip to main content
Glama
miyakejima
by miyakejima

get_elo_history

Reconstruct a player's ELO trajectory across their last matches, showing gains and losses in chronological order. Returns a summary with peak, trough, delta, and streaks, plus a full timeline.

Instructions

Reconstruct a player's ELO trajectory across their last N matches — every gain and loss in chronological sequence. Returns a summary (peak, trough, delta, streaks) and a full timeline of ELO changes. Useful for spotting tilt spirals, hot streaks, or sustained momentum.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
countNoNumber of matches to include (default 50, max 100)
seasonNoFilter by season number
identifierYesPlayer username or UUID
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It correctly implies a read operation (no mention of side effects) and describes the return structure (summary and timeline). However, it does not disclose potential limitations like rate limits, data freshness, or whether the tool requires authentication, which would be valuable.

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 extremely concise—two sentences that immediately convey the tool's purpose, output, and utility. Every word is functional, with no redundancy or filler. The structure is front-loaded with the core action, followed by output details and use cases.

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?

Given the absence of an output schema, the description compensates by detailing the return: a summary with peak, trough, delta, streaks, and a full timeline. This is sufficient for an agent to understand what to expect. The tool's complexity is moderate, and the description covers the key aspects, though it omits details like pagination or data format.

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 input schema has 100% description coverage, so the baseline is 3. The description adds minimal extra meaning beyond the schema: it frames 'count' as 'last N matches' but doesn't specify defaults or constraints beyond what the schema already states. It does not elaborate on 'identifier' or 'season' beyond their schema descriptions.

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 explicitly states the tool reconstructs a player's ELO trajectory across their last N matches with gains and losses in chronological sequence. It clearly identifies the specific verb (reconstruct) and resource (ELO trajectory), and the scope is well-defined. While it doesn't explicitly distinguish from siblings, the unique purpose of detailed ELO history is evident.

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?

The description provides clear guidance on when to use the tool, citing specific use cases like spotting tilt spirals, hot streaks, or sustained momentum. However, it lacks explicit exclusions or comparisons to sibling tools, which would help agents decide between alternatives like analyze_player_form or scout_opponent.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/miyakejima/mcsr-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server