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cheungbrenden

croissant-mcp

search_games

Search your rated Lichess games by color, result, date, opponent, or opening. Retrieves live data from the Lichess API with fallback to local export.

Instructions

Search the user's own rated Lichess games (blitz by default).

Filters are from the USER's side: colour is the colour they played, result is their result ('win', 'loss', 'draw'). Dates are 'YYYY.MM.DD'. Fetches live from the Lichess API, falling back to the local export.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ecoNo
sinceNo
untilNo
colourNo
resultNo
opponentNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
gamesYesThe matching games, newest first.
sourceYes'lichess-api' (live) or 'sidecar' (offline fallback).
sample_sizeYesHow many games matched. Always report this alongside any percentage — with ~30 blitz games per colour in a 2-year window, a bare percentage is misleading.
failed_to_parseYesGames skipped as unreadable, never silently dropped.
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses that the tool fetches live from the Lichess API with a fallback to local export, and notes the default blitz mode. However, it does not mention auth requirements, read-only status, or potential rate limits. The live-fetch disclosure is meaningful.

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 three sentences long, front-loaded with the core purpose, and every sentence provides necessary information: scope/default, filter semantics, and data source behavior. There is no filler or repetition.

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?

The description covers the tool's scope, default behavior, filter semantics, date format, and data source. Since an output schema exists, return values need not be described. The main gaps are the lack of detail on 'opponent' and 'eco', but overall the description is quite complete for a search tool with 6 optional parameters.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It explains the semantics of colour, result, and date format for since/until, which are the most ambiguous. It does not explain eco or opponent, but these are relatively self-explanatory. Overall, it adds significant value beyond the schema.

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 clearly states the tool searches the user's own rated Lichess games, with a specific verb ('Search') and resource ('user's own rated Lichess games'). It also mentions a default (blitz) which adds specificity and differentiates it from sibling tools that deal with databases and repertoires.

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 context: it is for searching the user's own rated games, with an implicit filter scope from the user's perspective. It does not explicitly mention alternatives or when not to use this tool, but the context is strong enough to distinguish it from siblings.

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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