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Glama

sportiq-mcp

cricket_get_pitch_report

Read-onlyIdempotent

Summarise pitch characteristics for a venue.

Args: venue: Venue key (e.g. wankhede), official name, or city.

Returns: data: {batting_friendly 0..1, expected_first_inn, recommendation, venue, pitch_type}. meta.source: which adapter served the venue record.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
venueYesVenue key (e.g. ``wankhede``), official name, or city.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataNo
metaNo
errorNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral detail about the return shape: batting_friendly score scale 0..1, expected_first_inn, recommendation, venue, pitch_type, and meta.source. This goes beyond the annotations without contradicting them.

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 compact and well-structured with a one-sentence summary followed by clear Args and Returns sections. Every line earns its place, and the most important information is front-loaded. There is no unnecessary prose.

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 simple single-parameter read-only reporting tool, the description is complete: it explains the input, the output fields, and the meaning of score ranges. The output schema presence plus the documented return structure leaves little ambiguity for an agent selecting or invoking this tool. No critical information 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 description coverage is 100% and the single parameter 'venue' is described identically in both the schema and tool description. The description adds no meaning beyond what the schema already provides, such as disambiguation rules or formatting constraints. Baseline 3 is appropriate given the high schema coverage.

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 states a specific verb ('Summarise') and resource ('pitch characteristics for a venue'), making the tool's function immediately clear. It is clearly distinct from sibling cricket tools like scores, squads, or odds. The one-line summary is not a tautology and accurately names the domain.

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

Usage Guidelines3/5

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

The description implies the tool should be used when pitch-related context for a venue is needed, and the venue parameter guidance is helpful. However, it does not explicitly state when to prefer this tool over alternatives or mention any exclusions or sibling tools. Usage is inferred rather than stated.

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

A3.6/5.0
Disambiguation3/5

Sport prefixes make the three domains easy to separate, and most tools have clear purposes. However, several tools overlap: football_match_predictor vs football_xg_model, f1_head_to_head_pace vs f1_race_pace_compare, and the cricket fantasy tools (build_dream11, captain_recommendation, differential_picks) all require careful reading to avoid misselection.

Naming Consistency4/5

The sport prefix + snake_case pattern is consistent and retrieval tools uniformly use get_, which creates predictability. The main deviation is that many analysis/model tools are noun phrases rather than verb_noun (cricket_head_to_head, football_knockout_path, f1_tyre_degradation), but they remain readable and scoped.

Tool Count2/5

44 tools is well above the 25+ threshold for a single server and will strain agent context and tool-selection quality. Each sport block is individually reasonable at 13-15 tools, but combining three sports plus cross-sport and health utilities makes the overall surface too large.

Completeness3/5

Core workflows are broadly covered: live data, schedule/standings, match prediction, tournament simulation, fantasy help, and strategy analysis exist for each sport. However, there are notable dead ends: cricket_player_form_index needs a player_id no exposed tool returns, football_get_match_stats requires an API-Football numeric ID not mapped anywhere, and f1_get_lap_times references a stints endpoint that is not exposed as a tool.