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DanielTomaro13

sportsdata-mcp

apitennis_fixtures

Read-onlyIdempotent

Retrieve tennis fixtures for a date range, with scores and set-by-set details provided once matches are played.

Instructions

Matches in a date range, with scores and set-by-set detail once played.

Returns: {success:1, result:[{event_key, event_date, event_time, event_first_player, first_player_key, event_second_player, second_player_key, event_final_result:'2 - 0', event_status, tournament_name, tournament_round, scores:[{score_first, score_second, score_set}], pointbypoint:[…]}]} — SHAPE FROM VENDOR DOCS.

NOTE: this shape is from the vendor's documentation and has NOT been verified against a live response (we hold no key for this provider). Treat it as approximate — inspect the actual payload before relying on a field name.

Example: A day's matches {"method": "get_fixtures", "date_start": "2025-01-20", "date_stop": "2025-01-20"}

Auth: needs your own key in API_TENNIS_KEY.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
methodNoLeave as-is.get_fixtures
date_stopYesYYYY-MM-DD.
date_startYesYYYY-MM-DD.
player_keyNoRestrict to one player.
event_type_keyNoRestrict to one event type.
tournament_keyNoRestrict to one tournament.
Behavior4/5

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

Beyond the readOnly, openWorld, and idempotent annotations, the description discloses that the output shape is from vendor documentation and has not been verified against a live response, urging caution. It also mentions the authentication requirement. These are valuable behavioral details not present in annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with a clear one-sentence purpose, followed by the return shape, a critical caveat, an example, and auth note. Each section earns its place, though the return shape block adds length. It is well-structured and information-dense without being rambling.

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?

Given the lack of an output schema, the description provides a detailed (if unverified) return shape, which is essential for agents. It includes an example call and auth requirements. It does not explain optional filter parameters, but the schema covers those. The description is reasonably complete for a read-only data fetch tool.

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 fully describes all parameters with 100% coverage, including formats and defaults. The description adds only a usage example that mirrors the schema. Since schema coverage is high, the description's added semantics are minimal, aligning with the baseline score of 3.

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?

The description clearly states the tool's function: retrieving matches in a date range with scores and set-by-set details once played. It uses a specific verb and resource, making the purpose clear. However, it does not explicitly differentiate from sibling tools like apitennis_events or apitennis_livescore, so it falls short of a 5.

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 for when to use the tool: for matches within a specific date range. It includes an example showing a one-day query. There are no explicit exclusions or alternative tool recommendations, but the context is sufficient for an agent to infer usage.

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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