Cito API

by com.citoapi.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

Name	Required	Description	Default
`includeGameProbes`	No	If true, light allSettled probes per primary game product/status path.

Output Schema

TableJSON Schema

Name	Required	Description
`ok`	Yes	true if the tool succeeded
`data`	No	Result payload when ok is true; null on error
`meta`	Yes
`error`	No
`partial`	No
`pagination`	No

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

First observedSep 1, 2026

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

A4.5/5.0

Disambiguation5/5

Each tool has a clearly distinct job, and the descriptions explicitly separate temporal states (live vs upcoming vs preview vs recap vs deep-dive) and interaction modes (one-best resolve vs browse search, curated tool vs raw escape hatch). Even the match/event families are carefully tiered so an agent should rarely misselect.

Naming Consistency3/5

Names are readable and grouped into recognizable families like list_*, match_*, and *_profile, but the overall convention is mixed: verb-prefixed names like resolve_entity and call_api sit alongside noun-phrase names like event_card, match_summary, and standings. Consistent snake_case prevents chaos, but there is no uniform verb_noun pattern.

Tool Count4/5

16 tools is at the upper edge of the ideal range, but the server spans multiple games and several composite read workflows, so each tool has a distinct purpose. It feels slightly heavy but not bloated.

Completeness4/5

The set covers health, discovery, entity resolution, profiles, standings, schedules, live matches, previews, recaps, and deep-dive match data, with call_api as an escape hatch for long-tail routes. Notable omissions like a standalone historical results search and some per-game upstream gaps keep it from a perfect score.

Resources

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