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livetennisapi-mcp

by io.github.livetennisapi
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Model analysis

get_match_analysis
Read-onlyIdempotent

Model analysis for a match: predicted win probability, the model's thesis and the main factors behind it. Requires the ULTRA plan. Match rows carry has_analysis, so check it first. A match with no thesis or profile has nothing to return.

Input Schema

NameRequiredDescriptionDefault
match_idYesMatch id, as returned by get_live_matches, get_upcoming_matches or get_recent_results.

Output Schema

NameRequiredDescriptionDefault
okYesTrue when the request succeeded, including when the answer is an empty list. False for a missing or rejected key, a plan that does not include this data, an unknown id, a rate limit or a network error. The message then says what to do. check_api_status is the exception: it is true whenever the API answered, and reports API key problems in the message.
thesisNoNarrative view.
messageYesHuman-readable summary of the result, safe to relay to the user verbatim.
profileNoQuantitative view.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedOutput schema / properties / message / description
      Previous value: -"Human-readable summary. Identical to the text content, so either half can be used alone."New value: +"Human-readable summary of the result, safe to relay to the user verbatim."
    • changedOutput schema / properties / ok / description
      Previous value: -"True 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."New value: +"True when the request succeeded, including when the answer is an empty list. False for a missing or rejected key, a plan that does not include this data, an unknown id, a rate limit or a network error. The message then says what to do. check_api_status is the exception: it is true whenever the API answered, and reports API key problems in the message."
  2. First observed

TDQS

A4.5/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, it discloses a hard subscription gate (requires the ULTRA plan) and an empty-result condition tied to the has_analysis flag. These are exactly the operational facts an agent needs and that annotations cannot express.

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?

Four tight sentences, front-loaded with what the tool returns, then plan requirement, then the gating check. No redundant restatement of the name or title.

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?

With an output schema present, the description needn't describe return shapes, and it still sketches them. Auth gating and the has_analysis precondition close the remaining gaps for a single-parameter read tool.

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 coverage is 100% and the single match_id parameter already documents its provenance (get_live_matches, get_upcoming_matches, get_recent_results). The description adds no further parameter detail, so baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

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

States a specific verb+resource (model analysis for a match) and enumerates the payload: predicted win probability, the model's thesis, and the main factors. This clearly separates it from statistics/odds siblings, though no sibling is named explicitly.

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?

Gives a concrete pre-check ('Match rows carry has_analysis, so check it first') and an explicit when-not condition ('A match with no thesis or profile has nothing to return'). The prerequisite is stated up front, leaving nothing to inference.

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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