Get the full input schema for a specific tool. Returns the JSON Schema (parameters, types, required fields, descriptions) needed to call the tool via tokenintel_invoke. Use tokenintel_discover first to find tool names.
| Name | Required | Description | Default |
|---|---|---|---|
| tool_name | Yes | The tool name to describe (e.g. 'tokenintel_whale_flows'). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must disclose behavior. It clearly states it returns JSON Schema for the tool, and implies it is a read-only introspection. Adequate for this simple tool.
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?
Two sentences, no wasted words. Front-loaded with the purpose, then usage guidance. Efficient and clear.
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 simplicity of the tool (single parameter, no output schema needed, read-only), the description fully covers what the agent needs to know, including the suggested order of tools.
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 single parameter 'tool_name' is already described in the schema with a clear example. The description does not add additional meaning beyond what the schema provides. Schema coverage is 100%.
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?
Describes a specific verb ('Get') and resource ('full input schema for a specific tool'). Clearly distinguishes from siblings like tokenintel_discover and tokenintel_invoke by stating its role.
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?
Tells users to use tokenintel_discover first to find tool names, guiding the correct workflow. Does not explicitly state when not to use, but the context is clear.
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.