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Cloud engine evaluation of a position

evaluate_position
Read-onlyIdempotent

Fetch Lichess cloud engine evaluation for a chess position (FEN): score, best move, and SAN principal line up to 3 candidates; returns available=false if uncached.

Instructions

Get the engine evaluation of a chess position (FEN) from Lichess's cloud cache: score, best move and principal line in SAN, for up to 3 candidate moves. If the position isn't cached, it returns available=false rather than a guess.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fenYesPosition in FEN, e.g. "rnbqkbnr/pppppppp/8/8/4P3/8/PPPP1PPP/RNBQKBNR b KQkq - 0 1". Move counters may be omitted.
multi_pvNoHow many candidate lines (1-3).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
fenYes
depthNo
linesYes
knodesNoThousands of nodes searched.
sourceNo
messageYes
availableYesFalse when Lichess has no cached cloud evaluation for this position.
side_to_moveYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already establish readOnly, idempotent and non-destructive behavior, but the description adds genuine context the annotations don't: results come from a cache and an uncached position yields available=false rather than a guessed evaluation. That fallback contract is exactly the kind of trait an agent needs before relying on the result.

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?

A single front-loaded sentence that delivers the verb, resource, source, output contents and the cache-miss behavior with zero filler. Nothing is padded or redundant.

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 an output schema present, the return values need not be spelled out, and annotations plus schema cover safety and parameters; the description closes the remaining gap by explaining the cache-miss case. It is only slightly short of complete because it omits any routing hint toward the tablebase sibling for endgame positions.

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% and the FEN and multi_pv parameters are both documented with formats and bounds in the schema. The description's 'up to 3 candidate moves' merely restates the multi_pv maximum, adding no syntax or semantic detail beyond the structured fields, so the baseline 3 applies.

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 the engine evaluation of a chess position (FEN)') and scopes the source as 'Lichess's cloud cache', which meaningfully separates it from a local engine or the tablebase sibling. It does not name a sibling tool to contrast against, so it falls short of the top mark.

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?

It implies usage (feed it a FEN, get an eval) and notes the up-to-3 candidate-move capability, but gives no explicit when-to-use guidance or exclusions. Notably it never tells the agent to prefer the 'tablebase' sibling for endgame positions, which is the most likely selection ambiguity here.

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