livetennisapi-mcp

Model analysis

get_match_analysis

Read-onlyIdempotent

Fetch model analysis for a tennis match, including predicted win probability, thesis, and key factors. Requires ULTRA plan; verify the match's has_analysis flag first.

Instructions

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

Input Schema

TableJSON Schema

Name	Required	Description	Default
`match_id`	Yes	Match id, as returned by get_live_matches, get_upcoming_matches or get_recent_results.

Output Schema

TableJSON Schema

Name	Required	Description
`ok`	Yes	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.
`thesis`	No	Narrative view.
`message`	Yes	Human-readable summary. Identical to the text content, so either half can be used alone.
`profile`	No	Quantitative view.

Schema Changelog

Changes observed during successful MCP inspections.

Addedv1.3.0Aug 5, 2026
Removedv1.2.1Jul 22, 2026

Changed2 schema fields changedv1.2.0Jul 22, 2026

addedInput schema / properties / match_id / description

Added value: +"Match id, as returned by get_live_matches, get_upcoming_matches or get_recent_results."

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "additionalProperties": false,
+  "properties": {
+    "message": {
+      "description": "Human-readable summary. Identical to the text content, so either half can be used alone.",
+      "type": "string"
+    },
+    "ok": {
+      "description": "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.",
+      "type": "boolean"
+    },
+    "profile": {
+      "additionalProperties": false,
+      "description": "Quantitative view.",
+      "properties": {
+        "expected_closeness": {
+          "description": "How close the model expects the match to be.",
+          "type": [
+            "number",
+            "null"
+          ]
+        },
+        "key_factors": {
+          "description": "Drivers behind the model view.",
+          "items": {
+            "type": "string"
+          },
+          "type": "array"
+        },
+        "volatility_rating": {
+          "description": "Expected swing in the match state.",
+          "type": [
+            "string",
+            "null"
+          ]
+        },
+        "win_probability_p1": {
+          "description": "Model probability player 1 wins, 0-1.",
+          "type": [
+            "number",
+            "null"
+          ]
+        }
+      },
+      "required": [
+        "win_probability_p1",
+        "expected_closeness",
+        "volatility_rating",
+        "key_factors"
+      ],
+      "type": "object"
+    },
+    "thesis": {
+      "additionalProperties": false,
+      "description": "Narrative view.",
+      "properties": {
+        "confidence": {
+          "description": "Model confidence, 0-1.",
+          "type": [
+            "number",
+            "null"
+          ]
+        },
+        "pick_side": {
+          "description": "Player the model favours, 1 or 2.",
+          "type": [
+            "number",
+            "null"
+          ]
+        },
+        "reasoning": {
+          "description": "Narrative reasoning.",
+          "type": [
+            "string",
+            "null"
+          ]
+        },
+        "state": {
+          "description": "Current state of the thesis.",
+          "type": [
+            "string",
+            "null"
+          ]
+        }
+      },
+      "required": [
+        "pick_side",
+        "confidence",
+        "state",
+        "reasoning"
+      ],
+      "type": "object"
+    }
+  },
+  "required": [
+    "ok",
+    "message"
+  ],
+  "type": "object"
+}

First observedv1.0.4Jul 22, 2026

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already cover read-only and idempotent behavior. The description adds meaningful behavioral context beyond the annotations: the ULTRA plan requirement and the empty-result condition when has_analysis is false. This helps the agent avoid unnecessary calls.

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?

Two sentences with no filler. The core purpose is front-loaded, followed by access requirements and an actionable precondition. Every sentence earns its place.

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?

For a read-only, single-parameter tool with a full input schema and an output schema, the description covers all non-obvious context: plan requirement, the has_analysis prerequisite, and the empty-result case. Nothing critical is missing.

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 only parameter, match_id, is already fully described with source endpoints. The description adds no new parameter-level detail, so the baseline score 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?

Description states a specific verb/resource ('Model analysis for a match') and lists concrete contents: predicted win probability, model's thesis, and key factors. This clearly distinguishes it from sibling tools like get_match_odds or get_match_statistics, even without naming them.

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 provides clear context and a strong precondition: 'Requires the ULTRA plan' and 'Match rows carry has_analysis — check it first.' It also implies when not to call ('a match with no thesis or profile has nothing to return'). It does not explicitly name alternative tools, but the usage boundary is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.