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lichess-mcp

lichess-mcp

by Anay704
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Get game analysis

get_game_analysis

Analyze a single Lichess game for per-player accuracy, average centipawn loss, and every blunder, mistake, or inaccuracy with played and engine moves plus evaluation swing.

Instructions

Computer analysis of a single Lichess game: per-player accuracy and average centipawn loss, plus every blunder, mistake and inaccuracy with the move that was played, the engine's preferred move, and the evaluation swing.

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": {
      "game_id": "<game_id>"
    },
    "name": "get_game_analysis"
  }
}

Input Schema

NameRequiredDescriptionDefault
game_idYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full disclosure burden. It describes the returned data in detail but does not mention whether the game must already be analyzed, whether it triggers on-demand engine analysis, or any error/edge cases. The description implies a read-only analysis but does not explicitly state side-effect-free behavior.

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 entire description is one sentence that efficiently packs all key output details without redundancy. Each clause contributes new information, making it very concise and well-structured.

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?

With one parameter and an output schema available, the description gives a solid overview of the tool's purpose and result content. However, it omits usage caveats (e.g., game must be finished, may require existing analysis) and fails to explicitly differentiate from sibling tools, leaving some completeness gaps.

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 schema has no description for game_id (0% coverage), so the description must add meaning. It mentions 'single Lichess game', which implies game_id is the game ID, but it does not specify format or how to obtain it. This adds partial but insufficient semantics for full parameter understanding.

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 the specific verb 'get' with resource 'game analysis', and clearly specifies it applies to a single Lichess game. It enumerates the analysis contents (accuracy, centipawn loss, blunders/mistakes/inaccuracies) and thus distinguishes itself from sibling tools like analyze_position or get_opening_stats.

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 provides clear context that this is for analyzing a single completed game, but it does not explicitly state when to use it versus alternatives like analyze_position or get_opening_stats. There are no direct exclusion or alternative recommendations, so usage guidance is implied rather than explicit.

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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