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DanielTomaro13

sportsdata-mcp

apisports_football_fixture_statistics

Read-onlyIdempotent

Retrieve team match statistics for a football fixture, including shots, possession, corners, and expected goals. Specify a fixture ID to get per-team stats.

Instructions

Team match statistics for one fixture — shots, possession, corners, and expected goals where covered.

Returns: {response:[{team:{id, name}, statistics:[{type:'Shots on Goal'|'Ball Possession'|'expected_goals'|…, value}]}]} — SHAPE FROM VENDOR DOCS. NOTE this is LONG format keyed by a HUMAN-READABLE type string, and value is sometimes a string with a percent sign ('54%'). Do not assume a numeric type.

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: One fixture's team stats {"fixture": 1035037}

Auth: needs your own key in API_SPORTS_KEY.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
teamNoOnly this team's column.
fixtureYesFixture id.
Behavior5/5

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

Beyond annotations (readOnly, openWorld, idempotent), the description discloses the long-format return structure, potential string values with percent signs, the unverified nature of the vendor-documented shape, and the need for an API key. These warnings significantly help the agent handle unexpected payloads.

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?

Every sentence earns its place: purpose, return shape, format caveat, verification warning, example, and auth requirement. It is compact yet information-dense, with the essential purpose front-loaded.

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?

The tool is simple (two params, one required), and the description covers the return format with an example, warns about data reliability, notes the optional team param's behavior, and clarifies auth. This is complete for its complexity, especially for a read-only, idempotent operation.

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 already describes both parameters well (fixture: 'Fixture id.', team: 'Only this team's column.'). The description adds an example using fixture but no new semantics beyond what the schema provides. Baseline 3 applies due to 100% schema coverage.

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 opens with a specific verb and resource: 'Team match statistics for one fixture — shots, possession, corners, and expected goals where covered.' This clearly states the tool's scope and distinguishes it from sibling tools like apisports_football_fixtures or apisports_football_predictions.

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 clearly contextualizes when to use the tool: when team match statistics for a single fixture are needed. It does not explicitly name alternatives or exclusions, but its focused scope makes the use case unambiguous. The example input reinforces this.

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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