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Glama

Cito API

live_matches

Read-only

Live matches board across primary games, or a single game filter. Normalized labels, scores, and matchIds.

When to use:

  • "What's live right now?"

  • Ops/dashboard live strip

Prefer over: sequential per-game call_api live probes.

Do not use when: user wants upcoming fixtures → upcoming_schedule; historical results → match_summary.

CS2 live path is /cs2/live; UFC is included in multi-game fan-out. UFC empty board: section.note + emptyReason + health (workerAlive/lag) + optional supervisor/nextCard (non-live); never fake match items from events[].

Parallel-safe: yes. Upstream cost: 1–5 (allSettled). Example: { "game": "all", "limitPerGame": 10 }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
gameNoSingle game or all. Omit for all primary games. Example: "lol".
limitPerGameNoMax matches kept per game (default 10, max 25).
includeLabelsOnlyNoToken-light mode: shorter item payloads (label + matchId + status).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okYestrue if the tool succeeded
dataNoResult payload when ok is true; null on error
metaYes
errorNo
partialNo
paginationNo

TDQS

A4.9/5.0
Behavior5/5

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

Beyond the readOnly/openWorld/destructive annotations, the description discloses important behavioral details: parallel-safety, upstream cost range, CS2 live path, UFC empty-board handling including health fields and a warning never to fabricate match items from events[]. This is exactly the kind of operational nuance an agent needs.

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 well-structured with clear sections: a one-line purpose, when-to-use, when-not-to-use, per-game edge cases, and performance notes. It is front-loaded with the core behavior and every section earns its place without unnecessary padding.

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 read-only live-matches tool with 3 optional parameters and rich annotations, the description is complete: it covers alternatives, edge cases (UFC empty board), performance characteristics, and example usage. The presence of an output schema means return-value documentation is not required here, so nothing important is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already covers all parameters fully (100% schema description coverage), so the baseline is 3. The description adds value with a concrete usage example ({ "game": "all", "limitPerGame": 10 }), clarifies the single-game filter concept, and reinforces batching behavior via limitPerGame, slightly exceeding the schema alone.

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 states the tool provides a live matches board across primary games or a filtered single game, with normalized labels, scores, and matchIds. It explicitly distinguishes itself from upcoming_schedule, match_summary, and per-game call_api probes, so an agent can clearly understand what this tool is for.

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

Usage Guidelines5/5

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

The description gives explicit when-to-use scenarios ('What's live right now?', ops/dashboard live strip), names a preferred alternative path (avoid sequential call_api probes), and provides do-not-use guidance with direct sibling alternatives (upcoming_schedule, match_summary). This leaves minimal ambiguity about tool selection.

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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TDQS

A4.6/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose. Composite tools like match_preview, match_summary, and match_details are well-separated by lifecycle stage, and resolve_entity vs search_entities are differentiated by intended use (single best match vs browsing list). No two tools appear to do the same thing.

Naming Consistency4/5

All names follow snake_case and are descriptive, but the pattern is not strictly verb_noun: most retrieval tools use noun phrases (match_summary, team_profile, standings) while actions use verb_noun (call_api, list_capabilities, resolve_entity). This is consistent within each category, so it remains predictable.

Tool Count4/5

16 tools is slightly above the ideal 3-15 range, but the server covers multiple games (LoL, CS2, UFC, Dota, COD, Tennis) and provides composite tools to reduce upstream calls. Each tool earns its place, and the breadth justifies the count.

Completeness5/5

The tool surface covers health checks, live matches, schedules, profiles, standings, head-to-head, previews, recaps, deep match details, event cards, entity resolution, and includes an escape hatch (call_api) for long-tail paths. No obvious gaps for a read-only sports data API.

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