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Fan Token Intel

by io.github.BrunoPessoa22

tokenintel_match_odds

Prediction-market odds curve for a single match from the in-play odds tape (odds_ticks): per-market (home/draw/away) implied-probability series with source labels (polymarket = CLOB midpoint, apifootball = de-vigged bookmaker odds), pre-match vs in-play segmentation against kickoff, and open/close/min/max summary stats per market. Settlement wind-down artifacts (ticks after a market first prints prob >= 0.99, or after full-time +15min) are excluded by default and counted via excluded_settlement_ticks. Curves are downsampled to <=300 points per market (labeled). match_id selects a fixture directly; token (+ optional date) resolves the most recent covered fixture. Use tokenintel_odds_coverage to discover which matches have odds data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateNoOptional YYYY-MM-DD; with token, selects that day's fixture instead of the most recent.
tokenNoFan token symbol — resolves its most recent (or date-selected) fixture with odds coverage.
match_idNoThe matches/odds_ticks match_id (e.g. 'apifb_1591866'). Covers fixtures with no fan token too.
include_settlement_ticksNoInclude post-settlement wind-down ticks in the curves (default false).

TDQS

A4.6/5.0
Behavior4/5

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

No annotations provided, so description carries full burden. It discloses default exclusion of settlement wind-down ticks, downsampling to ≤300 points, and counting of excluded ticks. Could mention any rate limits or data freshness, but overall transparent.

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

Conciseness4/5

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

The description is a single, dense paragraph but effectively front-loads the core purpose and key behaviors. It could be slightly more concise, but every sentence adds value.

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?

No output schema exists, so description must explain return values. It thoroughly describes the curve composition: per-market implied probabilities, source labels, pre-match vs in-play segmentation, summary stats, and downsampling. Also covers excluded ticks and discovery via sibling tool.

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?

Schema coverage is 100% with parameter descriptions. The description adds value by explaining the token resolution logic (most recent or date-selected fixture) and the effect of include_settlement_ticks. This supplements the schema well.

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 specifies the tool provides a prediction-market odds curve for a single match, detailing the data sources, preprocessing, and summary stats. It distinguishes from sibling tokenintel_odds_coverage by stating it covers a single match and directing users to that tool for discovery.

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?

Explicitly explains two modes of selection (match_id vs token+date) and advises to use tokenintel_odds_coverage to find matches with odds data. Provides clear context for when to use this tool versus alternatives.

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

All Fan Token Intel toolsInspect
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TDQS

A4.2/5.0
Disambiguation5/5

Every tool targets a distinct aspect of fan token intelligence (e.g., briefing, DEX depth, whale flows, event reactions). Detailed descriptions and usage notes (e.g., 'USE THIS for ...') clearly differentiate overlapping areas like token_context vs briefing.

Naming Consistency5/5

All tools follow a consistent 'tokenintel_<descriptive_name>' snake_case pattern. The prefix is uniform, and names like 'tokenintel_goal_direction_asymmetry' or 'tokenintel_dex_liquidity' are predictable and clear.

Tool Count4/5

22 tools is on the higher side but justifiable given the broad scope (market, sports, DEX, social, whale flows, meta-tools). The server covers many complementary functions without feeling bloated, though a few tools could potentially be merged.

Completeness5/5

The tool set covers the full lifecycle of fan token intelligence: overview (briefing), deep dive (token_context), prices, DEX analysis, whale flows, sports event reactions, social sentiment, health metrics, capital rotation, macro context, and even meta-tools (discover, describe, invoke). No obvious gaps for the stated purpose.

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