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nyc-tennis-courts

Full detail for one place to play

get_court

Everything the directory holds about one venue: courts, surfaces, floodlights, indoor season, accessibility, reservation programmes, coordinates, and the source each fact was read from. Takes the slug returned by find_courts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesVenue slug, e.g. central-park. From find_courts.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations provided, the description carries the full behavioral burden. It discloses the comprehensive data returned (courts, surfaces, floodlights, accessibility, etc.) and adds provenance ('source each fact was read from'), which is valuable context. It implies a read operation without side effects, though it doesn't explicitly state that it is non-destructive, but the description is sufficiently transparent about its scope.

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 two sentences, front-loaded with the core purpose ('Everything the directory holds about one venue') and then lists specific details. Every sentence earns its place—no fluff, no repetition. It's both concise and informative.

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

Completeness5/5

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

For a tool with a single parameter, no output schema, and no annotations, the description is fully self-contained: it explains what the input is, where it comes from, and what the output includes. The enumerated data fields effectively substitute for an output schema, and the description is complete given the tool's simplicity.

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 already provides 100% coverage with a clear description ('Venue slug, e.g. central-park. From find_courts.'). The description restates the origin ('Takes the slug returned by find_courts') but adds no new semantic meaning beyond reinforcing the source. According to the baseline for high schema coverage, a score of 3 is appropriate.

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 'Everything the directory holds about one venue' and enumerates specific data fields (courts, surfaces, floodlights, etc.). It distinguishes from sibling find_courts, which presumably returns a list of venues, by focusing on full detail for a single venue.

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 explicitly instructs that it 'Takes the slug returned by find_courts', establishing a clear usage pattern. It doesn't explicitly state when not to use it or name alternatives, but the dependency on find_courts makes the workflow explicit, so the guidance is strong though not exhaustive.

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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