Cito API

head_to_head

Read-only

Composed head-to-head record between two teams, two UFC fighters, or two tennis players. No first-class REST H2H exists — this tool filters match history server-side.

When to use:

Rivalry / series record questions
Supporting context for previews

Prefer over: agent-side double match-list filtering.

Do not use when: single-side form only → team_profile or player_profile.

Caveat: Dota filters are weaker; expect meta.warnings when data is sparse.

Parallel-safe: yes. Upstream cost: 2–4. Example: { "game": "cs2", "sideA": "faze", "sideB": "navi", "limit": 20 }

Input Schema

TableJSON Schema

Name	Required	Description	Default
`to`	No	ISO-8601 upper bound on meeting startTime (client filter).
`from`	No	ISO-8601 lower bound on meeting startTime (client filter).
`game`	Yes	Game title: lol \| cs2 \| dota2 \| cod \| ufc \| tennis. Example: "cs2".
`limit`	No	Max meetings returned.
`sideA`	Yes	Id or slug for side A.
`sideB`	Yes	Id or slug for side B.
`entityType`	No	team (default), fighter (UFC), or player (tennis).	team

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

TDQS

A4.7/5.0

Behavior5/5

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

Annotations already indicate read-only and non-destructive behavior; the description adds meaningful context: server-side filtering instead of a native endpoint, weaker Dota filters with meta.warnings, parallel-safety, and upstream cost. These go beyond the structured annotations and inform safe invocation.

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 front-loaded purpose, clear usage sections, a caveat, and an example. Every sentence adds value, and the length is justified given the number of parameters, siblings, and the need to disambiguate from related tools.

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?

Given the rich input schema, annotations, output schema, and explicit routing to sibling tools, the description covers all essential context an agent needs. It includes limitations, parallel-safety, cost, and a concrete invocation example, so no critical operational gap remains.

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?

Schema description coverage is 100%, so the schema already documents all parameters. The description's example clarifies expected slug-style values but adds no new semantic meaning beyond what the schema provides, 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.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the resource: a composed head-to-head record between two teams, UFC fighters, or tennis players. It also differentiates itself by noting that no first-class REST H2H exists and by explicitly routing single-side questions to team_profile or player_profile.

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, a 'prefer over' instruction, and a direct 'do not use when' rule with named sibling alternatives. This leaves little ambiguity about when an agent should select this tool.

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

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