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livetennisapi-mcp

by io.github.livetennisapi

Charting: player career profile

get_charting_player
Read-onlyIdempotent

Career shot-level profile from the Match Charting Project: serve placement (deuce/ad × wide/body/T), return depth and outcomes, net play, clutch break/game/set-point serving, winners and errors by wing, rally-length tendencies — summed over the player's charted matches. COVERAGE IS CURATED (11,646 charted matches back to the 1960s, concentrated on the majors), not full-slate. An ambiguous name returns the candidates to choose from. Requires the ULTRA plan.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesPlayer name fragment, min 3 chars — must resolve to one charted person.
genderNoDisambiguates a name charted on both tours.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okYesTrue when the call returned data. False for a tier wall, a missing or rejected key, or an empty result — all of which are normal states with a clear remedy, not failures.
playerNoThe resolved charted player.
messageYesHuman-readable summary. Identical to the text content, so either half can be used alone.
coverageNoA reminder that charting coverage is curated, not full-slate.
familiesNoPer-family summed numeric columns — raw sums over the player's charted Total rows.
matches_chartedNoThe sample every summed field covers.

TDQS

A4.7/5.0
Behavior5/5

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

Beyond annotations, the description discloses that coverage is curated (11,646 matches, concentrated on majors) and that ambiguous names return candidate choices. It also notes the ULTRA plan requirement. These are non-obvious behavioral traits that help set expectations.

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 information-dense but well-organized, front-loaded with the core purpose. The metric list is exhaustive yet each item adds value, and the coverage and plan notes are clearly highlighted. No fluff or redundancy.

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 tool's complexity and that an output schema exists, the description covers the data source, coverage scope, ambiguity handling, and plan requirement. It gives enough context for an agent to know when to expect partial data and what inputs are needed. No significant gaps remain.

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 descriptions already cover both parameters well (100% coverage). The description adds the behavior that an ambiguous name returns candidates, clarifying how the name parameter resolution works beyond the schema's 'must resolve to one charted person'.

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 it provides a 'career shot-level profile from the Match Charting Project' listing specific metrics (serve placement, return depth, net play, etc.), which distinguishes it from siblings like get_charting_match (match-level) and get_player (general profile). The scope is explicit: summed over charted matches.

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

Usage Guidelines4/5

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

The description implies when to use this tool (for aggregated career charting stats) and notes prerequisites like 'Requires the ULTRA plan' and the ambiguous-name behavior. However, it does not explicitly mention alternatives or exclusions, such as 'for a single match use get_charting_match'.

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

A3.9/5.0
Disambiguation4/5

The tool set is largely distinct with clear resource/action pairs. Potential confusion exists between get_match and get_match_score (both return match information), and get_fixtures vs get_upcoming_matches (both list upcoming matches), but descriptions clarify the specific use cases for each.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern using either 'get_' or 'search_'. This makes the API highly predictable and easy to navigate, with no stylistic deviations.

Tool Count3/5

With 24 tools, the server is on the heavy end of the typical range. The broad scope (live, archive, charting, rankings, tournaments) justifies the count, but it borders on overwhelming and requires careful categorization.

Completeness4/5

The server covers the core lifecycle of a tennis information API: searching players/tournaments, retrieving profiles/rankings, live scores and detailed match analysis, historical results, and head-to-head records. Minor gaps like tournament draws or standings are missing, but the primary use cases are well covered.

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