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score-venues

Read-only

Rank candidate venues by travel time fairness for groups, computing travel times and minimizing longest journey, total travel, or variance.

Instructions

Score candidate venues by travel time fairness for multiple participants. Computes travel times from each participant to each venue and ranks by fairness strategy. The AI should suggest venues (from its own knowledge or via search-venues) and pass them here for scoring.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
participantsYesParticipant locations (2–10 people)
venuesYesCandidate venues to score (1–50)
transport_modeYesHow participants will travel
fairnessNoScoring strategy: min_max (default, minimise longest journey), min_total (minimise total travel), min_variance (equalise travel times)
Behavior3/5

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

The description mentions computing travel times and ranking by fairness strategy, consistent with the readOnlyHint annotation (no mutation). No additional behavioral traits (e.g., unreachable venues handling, caching, or output details) are disclosed beyond the annotations, but annotations already convey non-destructive nature.

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, consisting of three sentences that front-load the main action, explain the computation, and provide usage guidance. No unnecessary words or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description lacks information about the output format. Given no output schema, it is critical to specify what the tool returns (e.g., ranked list with scores, travel times). This omission hinders the agent's understanding of how to use the result.

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 each parameter's purpose is clear from the schema alone. The description adds minimal extra semantic value beyond reiterating the function; it does not elaborate on fairness strategies or transport mode options beyond schema definitions.

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's purpose: scoring candidate venues by travel time fairness for multiple participants. It specifies the action (score), resource (venues), and context (travel time fairness), and distinguishes it from the sibling 'search-venues' by indicating it is used after venues are gathered.

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 advises the AI to suggest venues from its own knowledge or via 'search-venues' before using this tool, providing clear usage context. While it does not explicitly state when not to use or list alternative tools, it implies the appropriate workflow.

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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