Fan Token Intel

tokenintel_social_sentiment

Social sentiment for a fan token. The only live social source is the LunarCrush aggregated feed, and on the current plan it provides galaxy_score and alt_rank ONLY — sentiment and social_volume come back null (see lunarcrush.fields_unavailable); they are unavailable, not zero. Native X/Twitter ingestion was retired 2026-03-01 and native Reddit/YouTube ingestion has never run in production, so those blocks read 0 — data_sources labels each pipeline (active / no_recent_data / inactive_since_ / never_active). overall_sentiment is computed only from sources that actually reported activity and is null when none did. Descriptive community-mood data, not a recommendation.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`hours`	No	Lookback window in hours (default: 24, max: 168)
`token`	Yes	Token symbol (e.g., ASR, BAR, PSG). Required.

TDQS

A4.3/5.0

Behavior5/5

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

In the absence of annotations, the description thoroughly discloses behavioral traits: data source limitations ('only live social source is LunarCrush'), field availability ('sentiment and social_volume come back null'), historical ingestion status, and how data_sources labels indicate pipeline states. It also clarifies the tool's purpose is descriptive data, not a recommendation.

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 dense with necessary detail and front-loads the purpose. While every sentence adds value, the paragraph is slightly long but remains clear and effective.

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

Completeness4/5

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

Given the lack of an output schema, the description explains return behaviors well (galaxy_score, alt_rank, null fields, data_sources labels). It provides enough context for the agent to understand the tool's output and limitations.

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 schema already documents both parameters. The description does not add further detail beyond the schema, which is sufficient. 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?

The description clearly states the tool provides social sentiment for fan tokens, using a specific verb ('Social sentiment') and resource ('fan token'). It differentiates from siblings by focusing solely on sentiment analysis among many token intelligence tools.

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 on data availability (LunarCrush feed only, retired ingestion channels) and states it is descriptive, not a recommendation. However, it does not explicitly mention when to use or avoid this tool relative to siblings.

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

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.

Resources

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