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Highlightly

by io.github.pipeworx-io
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Highlightly Matches

highlightly_matches
Read-onlyIdempotent

Live scores / matches for a sport on a date or league. Returns id, home/away teams, score, status, kickoff date, and league. Example: highlightly_matches({ sport: "football", date: "2026-05-01", _apiKey: "your-key" })

Example tool call

This call uses example arguments supplied in the tool’s input schema.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "date": "2026-05-01",
      "sport": "football",
      "_apiKey": "your-highlightly-api-key"
    },
    "name": "highlightly_matches"
  }
}

Input Schema

NameRequiredDescriptionDefault
dateNoMatch date in YYYY-MM-DD format, e.g. "2026-05-01" (optional)
sportNoSport slug that selects the API path prefix. Default "football" (association football / soccer). Other values: "basketball", "hockey", "baseball", "rugby", "volleyball". "soccer" is normalized to "football".
_apiKeyYesHighlightly API key (free 100/day at highlightly.net)
leagueIdNoNumeric league ID to filter by (optional)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior, so the safety profile is covered. The description adds return-field context but does not discuss rate limits, live-data freshness, or what happens when no date or league is supplied. It does not contradict annotations.

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 compact, front-loaded with the core purpose, and includes a directly usable example. Every sentence adds value and there is no redundant or filler content.

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 simple schema, strong annotations, and explicit list of return fields, the description is largely complete for an agent to select and invoke the tool. It could be slightly clearer about behavior when no optional date or league is provided, but the schema and examples mitigate that gap.

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 provides 100% coverage with descriptions for all four parameters, including examples and normalization behavior for 'soccer'. The tool description adds little parameter meaning beyond a sample call, so the baseline of 3 is appropriate.

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 identifies the tool's purpose: providing live scores/matches for a sport filtered by date or league. It also lists the main returned fields, which helps distinguish it from related tools like highlightly_highlights or highlightly_predictions, though it does not explicitly name those alternatives.

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 implies when to use this tool—when needing live scores or matches—but gives no explicit guidance on when to prefer it over sibling tools, nor any exclusions or alternative routing. This is left mostly to inference from the tool name and sibling list.

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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