Get current market conditions — BTC trend, CHZ momentum, fear/greed index, and the platform's market regime classification. Useful for filtering or adjusting signal confidence based on macro conditions.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It describes what data is returned but doesn't disclose behavioral traits like read-only nature, authentication needs, or rate limits. For a stateless query tool with no parameters, the description is adequate but could be more explicit about being non-destructive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loads the essential purpose, and adds a use case. Every word contributes value, with no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no parameters, no output schema, and no annotations, the description fully covers the tool's purpose and usage context. It answers what the tool does and when to use it, leaving no critical gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so schema coverage is 100% by default. The description doesn't need to add parameter info. Baseline 4 is appropriate since no parameters exist to elaborate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it retrieves current market conditions, listing specific data points (BTC trend, CHZ momentum, fear/greed index, market regime classification). It also provides a concrete use case for filtering signal confidence, distinguishing it from sibling tools like tokenintel_macro_context or tokenintel_social_sentiment.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit guidance on when to use this tool ('for filtering or adjusting signal confidence based on macro conditions'). It doesn't mention when not to use it or compare directly to alternatives, but the context is clear enough for an agent to decide.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.