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Cito API

standings

Read-only

League/event standings or world/division rankings normalized to ranked rows.

When to use:

  • Table / playoff picture

  • UFC rankings; CS2 world or event standings; CDL standings; LoL league/tournament tables

Prefer over: raw standings via call_api for agent-normalized rows.

Do not use when: single team form → team_profile; live scores → live_matches. Dota has no first-class standings (may NOT_IMPLEMENTED or weak worldRanking).

Required scope keys by game:

  • lol: leagueId OR tournamentId

  • cs2: omit for world rankings; eventId for event standings

  • cod: optional season/stage

  • ufc: optional division (scope=division)

  • dota2: best-effort worldRanking only

Parallel-safe: yes. Upstream cost: 1–2. Example: { "game": "cod", "season": "2026", "limit": 50 }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
gameYesGame title: lol | cs2 | dota2 | cod | ufc | tennis. Example: "cs2".
limitNoMax items to return (default 50, max 100). Example: 50.
scopeNoInterpretation depends on game; defaults sensibly per game.
stageNoStage key (COD / LoL).
seasonNoSeason key (COD).
eventIdNoCS2 event id for event standings.
divisionNoUFC division key.
leagueIdNoLoL league id for league standings.
tournamentIdNoLoL/COD tournament id.

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?

Annotations already mark it readOnly, openWorld, and non-destructive. The description adds useful behavior beyond those flags: data is normalized to ranked rows, Dota may NOT_IMPLEMENTED or return weak worldRanking, the call is parallel-safe, and upstream cost is 1–2. No behavior contradicts the 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?

Organized into bullets with a one-sentence summary at top, game-specific groupings, operational hints, and an example. Every section earns its place, and the formatting front-loads the most critical routing information.

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?

For a 9-parameter, multi-game tool the description covers use cases, exclusions, per-game parameter combinations, an edge case (Dota), parallelism, cost, and gives an example. The only minor omission is guidance for the tennis enum value, so it is not absolutely exhaustive.

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

Parameters5/5

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

Schema coverage is 100%, so baseline is 3; the description goes further by mapping each game to required scope keys (leagueId/tournamentId for LoL, eventId for CS2, division for UFC, etc.) and explaining defaults like omitting scope for CS2 world rankings. This is meaningfully beyond the one-line schema descriptions.

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 definition: 'League/event standings or world/division rankings normalized to ranked rows.' It also names concrete use cases (UFC rankings, CS2 world/event standings, CDL standings, LoL league/tournament tables) and explicitly contrasts with team_profile and live_matches, so an agent can distinguish it from siblings without opening schemas.

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?

It provides an explicit 'When to use' list, a 'Prefer over' statement steering agents to this normalized tool over raw call_api, and a 'Do not use when' section naming team_profile and live_matches. It even notes Dota's weak first-class support, giving a conditional usage boundary.

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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