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Glama

pulsenetwork

by com.theaslangroupllc.pulse

stateedge

StatEdge: Global sports analytics and intelligence API. AI-synthesized injury reports, ATS/spread analysis, matchup predictions, odds analysis, parlay optimization, referee tendency analysis, rest/travel advant

Coverage: Global

Endpoints: • global ($0.10): Global sports intelligence — F1, cricket, rugby, tennis, AFL, golf, boxing, MMA, cycling • ats ($0.10): Against-the-spread trends • parlay ($0.10): Parlay analysis and probability • ref-analysis ($0.10): Referee and official tendencies • rest ($0.08): Rest and schedule advantage analysis • injury-impact ($0.08): Single player injury impact analysis

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
refNoReferee name (optional — analyzes general tendencies if omitted)
langNoResponse language code (en | es | fr | de | zh | hi | ar | pt | ja | ko | etc.)
legsNoComma-separated parlay legs (e.g. Chiefs -3,Over 47.5,Lakers ML)
teamNoTeam to analyze
sportNoSport code. F1 pulls live data from Jolpica API. All others use real-time Tavily synthesis from authoritative sources (formula1.com, ESPNcricinfo, Cricbuzz, worldrugby.org, BBC Sport, etc.)
actionNoF1: race|standings|qualifying|calendar. Cricket: match|series|ipl|standings. Rugby: match|tournament|standings. Tennis: tournament|rankings|draw|match. Others: preview|results|standings|analysis.
detailNoOptional context: tournament name, team name, matchup, series. E.g. 'Six+Nations', 'Wimbledon', 'England+vs+Australia', 'Masters'
playerNoplayer
opponentNoopponent
situationNoThe situation to analyze (e.g. home-underdog, divisional, off-a-loss, primetime)

Schema Changelog

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

  1. Changed3 schema fields changed
    • changedInput schema / properties / sport / description
      Previous value: -"Sport or league code. Global coverage: EPL/LALIGA/BUNDESLIGA/SERIEA/LIGUE1/UCL for European soccer; AFL/NRL/NBL for Australia; SIXNATIONS/NRL for rugby; F1 for Formula 1; CRICKET_IPL/CRICKET_BBL for cricket."New value: +"Sport code. F1 pulls live data from Jolpica API. All others use real-time Tavily synthesis from authoritative sources (formula1.com, ESPNcricinfo, Cricbuzz, worldrugby.org, BBC Sport, etc.)"
    • changedInput schema / properties / team / description
      Previous value: -"Team name (e.g. Lakers, Chiefs, Arsenal, Mumbai Indians)"New value: +"Team to analyze"
    • removedInput schema / properties / week
      Removed value: -{
-  "description": "Week number (NFL/NCAAF only)",
-  "type": "string"
-}
  2. First observed

TDQS

C2.8/5.0
Behavior2/5

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

There are no annotations, so the description carries full burden. It mentions 'AI-synthesized' data and global coverage, which adds some behavioral insight, but it omits details on data freshness, error behavior, rate limits, permissions, or whether operations are read-only. The description is a marketplace-style summary rather than a behavioral disclosure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description uses a structured list of endpoints with pricing, which is scannable, but the opening sentence is a long, truncated run-on ('rest/travel advant...') and 'Coverage: Global' repeats the word 'Global' from the first line. Some content is redundant, and the truncation suggests carelessness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

This is a multi-endpoint API with 10 parameters, no required parameters, no output schema, and no annotations. The description lists endpoints but does not explain how to select an endpoint or how the many parameters (ref, legs, opponent, situation) map to each endpoint. This leaves significant gaps for an agent trying to invoke the tool correctly.

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%, so the baseline is 3. The description's endpoint list indirectly relates to parameters like sport and action but does not add explicit parameter meaning beyond the schema. Therefore, it neither enhances nor diminishes the schema's clarity.

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?

The description clearly identifies the tool as a 'Global sports analytics and intelligence API' and lists its major capabilities (injury reports, ATS analysis, parlay optimization, etc.), giving a solid understanding of purpose. However, it does not explicitly differentiate itself from sibling sports tools like footballpulse or racingpulse, lacking direct comparison.

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

Usage Guidelines2/5

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

The description provides a list of endpoints and pricing but offers no guidance on when to choose this tool over alternatives or any exclusions. It implies usage for sports analytics but does not state prerequisites, scenarios, or alternative tool recommendations.

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

B3.2/5.0
Disambiguation4/5

Each tool has a unique domain prefix (e.g., airdroppulse, alphapulse, arbipulse) making them mostly distinguishable at a glance. A few adjacent verticals like careerpulse vs talentpulse or marketpulse vs dealpulse have overlapping themes, but their descriptions clarify the distinct focus. The utility tools (catalog_search, discover, get_openapi_spec, x402_troubleshoot) are also clearly distinct in role. However, the sheer number of similar 'pulse' names could still cause misselection without reading descriptions.

Naming Consistency4/5

The dominant naming convention is `<domain>pulse` (e.g., climatepulse, cryptopulse, edupulse), which is highly consistent and predictable. Exceptions like catalog_search, discover, get_openapi_spec, x402_troubleshoot, and stateedge break the pattern, but these are few and serve obvious utility purposes. Overall, the convention is clear and easily learnable.

Tool Count2/5

With 80 tools, the server presents an extremely large and potentially overwhelming surface. While each tool represents a distinct intelligence vertical and navigation aids exist (catalog_search, discover, get_openapi_spec), the count far exceeds the typical 3-15 range for coherent agent use and even the 'heavy' 16-25 range. The burden of selecting the correct vertical from 80 options is significant, despite clear naming.

Completeness5/5

The server offers an exceptionally broad and deep coverage of domains, from finance and health to agriculture and gaming. Each vertical includes multiple endpoints that address core operations for its domain, such as search, analysis, comparisons, deterministic checks, and even action-oriented tools like letter generators and physical mail. The presence of free discovery and troubleshooting tools fills potential gaps, leaving no obvious dead ends in the overall tool surface.

Resources