Crawlora MCP

chesscom_player_game_month_pgn

Read-only

Get one player’s monthly PGN archive; returns source PGN content and metadata for the requested year and month. The service calls Chess.com’s documented Published-Data API serially. This independent service is not affiliated with Chess.com; do not use results for AI training or dataset creation.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "username": "<username>",
      "year": "<year>",
      "month": "<month>"
    },
    "name": "chesscom_player_game_month_pgn"
  }
}

Input Schema

Name	Required	Description
`year`	Yes	Required source year.
`month`	Yes	Required source month.
`username`	Yes	Required source username.

Output Schema

Name	Required	Description	Default
`data`	Yes	The tool result payload (shape varies per tool; see each tool's docs resource).

Schema Changelog

Changes observed during successful MCP inspections.

AddedNov 13, 2026

TDQS

A3.7/5.0

Behavior4/5

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

Annotations cover read-only and open-world behavior, and the description adds meaningful context beyond that: the service hits Chess.com's Published-Data API serially (a latency/serialization signal) and is an independent, unaffiliated service. It also discloses a licensing restriction on downstream use. Return contents are described at a high level, which is adequate given an output schema exists.

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?

Three sentences, front-loaded with the purpose and return content before the operational and legal caveats. Each sentence carries information, though the affiliation disclaimer is boilerplate-adjacent.

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 three-parameter, read-only fetch with a full output schema and safety annotations, the description covers purpose, data source, and use restrictions. The remaining gap is guidance on parameter formatting and behavior when no games exist for the month, which keeps it short of a 5.

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 three parameters are already documented as required. The description confirms the year/month semantics of 'requested year and month' but adds no format details (e.g., whether year is '2025' and month is '01' or '1'), so it does not enrich the schema.

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: 'Get one player's monthly PGN archive,' and specifies scope as 'the requested year and month.' The emphasis on 'source PGN content' signals that this returns raw PGN, which implicitly separates it from the JSON-oriented chesscom_player_game_month sibling, though that distinction is never made explicit.

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 only implied: an agent can infer this is the tool to call when raw PGN for a user-month is needed. No alternatives are named and no when-not-to-use conditions are given beyond the AI-training disclaimer, which is a policy restriction rather than tool selection guidance.

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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