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ferasbbm

Sportmonks MCP Server

by ferasbbm

get_fixtures_by_date_range

Retrieve football fixtures for any date range up to 100 days. Filter by league, state, and include detailed match data like lineups, statistics, odds, and predictions.

Instructions

Get fixtures within a date range (max 100 days).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
start_dateYesStart date YYYY-MM-DD.
end_dateYesEnd date YYYY-MM-DD.
includeNoSemicolon-separated includes. Available: sport;round;stage;group;aggregate;league;season;coaches;tvStations;venue;state;weatherReport;lineups;events;timeline;comments;trends;statistics;periods;participants;odds;premiumOdds;inplayOdds;prematchNews;postmatchNews;metadata;sidelined;predictions;referees;formations;ballCoordinates;scores;xGFixture;pressure;expectedLineups;predictedLineups;matchfacts
selectNoComma-separated fields to return on base entity.
filtersNoFilters. e.g. fixtureLeagues:501,271 or fixtureStates:1 or markets:12,14
sortByNoField to sort by. e.g. starting_at
localeNoLanguage for name fields. e.g. en, de
timezoneNoTimezone for datetime fields. e.g. Europe/London
pageNoPage number for paginated results.
per_pageNoResults per page (max 50).
Behavior2/5

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

The only behavioral disclosure is the 'max 100 days' constraint. There is no mention of pagination (page/per_page), rate limits, idempotency, or error behavior. Since annotations are absent, the description carries the full burden of transparency, which it fails to meet.

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 extremely concise (one sentence) and front-loaded. However, it lacks necessary depth; brevity here sacrifices completeness. No wasted words, but also insufficient information.

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?

With 10 parameters, no output schema, and many sibling tools, the description is too minimal. It does not clarify the response format, how to use includes/select/filters, or the implications of the date limit. The agent may struggle to invoke the tool correctly without additional external knowledge.

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?

All 10 parameters have descriptions in the schema (100% coverage). The tool description adds only the date range limit, which is relevant but not parameter-specific. Baseline 3 is appropriate because the schema already documents the parameters adequately.

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 action ('Get fixtures') and the resource ('within a date range') with an important constraint ('max 100 days'). It distinguishes from sibling tools like 'get_fixtures_by_date' (single day) and 'get_fixtures_by_date_range_for_team' (team-specific), but does not explicitly call out these distinctions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus its many siblings. There is no mention of alternatives, prerequisites, or scenarios (e.g., 'for broad queries' vs 'for team-specific queries'). The agent must infer from context.

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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