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livetennisapi-mcp

by io.github.livetennisapi

Archive career aggregates

get_archive_career
Read-onlyIdempotent

One player's whole career over the results archive (1968–2022): W-L record overall and by surface/level/year, titles, and summed serve statistics with honest coverage — the corpus records serve stats from 1991 only, so matches_with_stats states how many matches the serve block covers. The name must resolve to one person; an ambiguous fragment returns the candidate list to choose from. Requires the BASIC plan or any History plan.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesPlayer name fragment, min 3 chars — must resolve to exactly one person.

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.
spanNoCareer span inside the archive.
serveNoSummed serve stats + derived ratios. matches_with_stats states the coverage; ratios are null where the denominator is zero.
recordNoThe W-L record.
by_yearNoPer-season W-L.
messageYesHuman-readable summary. Identical to the text content, so either half can be used alone.
player_nameNoThe resolved player.

TDQS

A4.5/5.0
Behavior4/5

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

Beyond the annotations (readOnly, openWorld, idempotent), the description discloses an important data caveat: serve stats are only recorded from 1991 and matches_with_stats indicates coverage. It also explains the ambiguity resolution behavior. This is valuable added context, though not exhaustive in terms of pagination or rate limits.

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 concise and well-structured, with a clear front-loaded purpose followed by necessary caveats and requirements. Every clause contributes useful information with no wasted words.

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 a single parameter, rich annotations, and an output schema, the description fully covers key usage rules, data limitations, and disambiguation behavior. It does not need to explain return values because the output schema is present. 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 already fully documents the 'name' parameter with type, minLength, and a description. The tool description reinforces the resolution requirement and explains that an ambiguous fragment returns a candidate list, providing context beyond the schema. This justifies a score above the baseline of 3.

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 tool returns a single player's entire career over the results archive (1968–2022), including W-L record, titles, and serve statistics. This scope distinguishes it from sibling tools like get_archive_match or get_player, making the purpose highly specific.

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?

It provides clear context on when to use the tool (for full career archive aggregates) and includes explicit requirements: the name must resolve to exactly one person, and a plan is required. However, it does not explicitly name alternative tools or state when not to use this tool, so it stops short of a 5.

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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