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Cito API

api_health

Read-only

API reachability, API key validity, plan tier, rate-limit headers, and best-effort included games.

When to use:

  • Once per session before heavy work

  • After 401/403/UNSUPPORTED_GAME/RATE_LIMIT

  • App scaffolding entitlement checks

Prefer over: probing random game endpoints to test the key.

Do not use when: you only need live scores — use live_matches.

Parallel-safe: yes. Upstream cost: 1–6. Example: { "includeGameProbes": true }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
includeGameProbesNoIf true, light allSettled probes per primary game product/status path.

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 adds valuable behavioral context: parallel-safety, upstream cost range, best-effort probing behavior, and which error situations warrant calling it. This gives the agent a clear model of the tool's side effects and operational profile.

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 tightly organized with front-loaded output information, a compact when-to-use list, and a short example. Every section earns its place, and there is no filler or redundant repetition of schema fields.

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?

Despite having an output schema, the description still supplies operational context such as upstream cost, parallel-safety, and error recovery triggers. For a simple single-parameter health-check tool, this is complete enough for an agent to select and invoke it correctly.

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 schema already documents includeGameProbes at 100% coverage, so the baseline is 3. The description adds a concrete usage example and links the parameter to the 'best-effort included games' behavior, making the optional flag's purpose clearer without repeating the schema.

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 exactly what the tool reports: API reachability, API key validity, plan tier, rate-limit headers, and best-effort included games. It clearly differentiates from siblings by naming live_matches as the alternative for live scores.

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?

Provides explicit when-to-use scenarios, including once per session, after specific errors, and for entitlement checks. It also gives a 'Prefer over' instruction and a 'Do not use when' rule, which is exactly the kind of routing guidance an agent needs.

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.

Resources