Get DEX depth and slippage curves for fan token pools on Chiliz Chain. Computes constant-product (x*y=k) price impact at trade sizes [1%, 5%, 10%, 25%] of pool reserves. Useful for agents evaluating execution costs before trading. Data from latest on-chain liquidity snapshots.
| Name | Required | Description | Default |
|---|---|---|---|
| token | No | Token symbol (optional). If omitted, returns all CHZ pairs sorted by TVL. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses that the tool computes constant-product price impact at specific trade sizes (1%, 5%, 10%, 25%) and uses 'latest on-chain liquidity snapshots'. This gives a good understanding of behavior and data source, though it could explicitly state that it is read-only and has no side effects.
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?
Three sentences with no fluff. First sentence states purpose, second explains computation, third gives use case and data source. Every sentence earns its place.
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?
The description covers purpose, computation, and data source but does not describe the output format. For a tool that returns 'depth and slippage curves', it would be helpful to specify the structure (e.g., arrays of price impact vs. amount). Given no output schema, the description could be more complete about what the agent receives.
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 input schema has one optional parameter 'token' with description. The description adds value by specifying the default behavior when omitted: 'If omitted, returns all CHZ pairs sorted by TVL.' This clarifies the parameter's semantics beyond the schema's minimal description.
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 uses a specific verb 'Get' and clearly identifies the resource 'DEX depth and slippage curves' with context (fan token pools on Chiliz Chain). It distinguishes from siblings by specifying the computation of price impact at fixed trade sizes, unlike other tools like tokenintel_dex_liquidity which likely provide raw liquidity data.
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 states 'Useful for agents evaluating execution costs before trading', providing clear when-to-use guidance. It does not explicitly state when not to use or mention alternatives, but the context and sibling tool names imply that other tools exist for different aspects (e.g., liquidity, prices). This is sufficient but not exhaustive.
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.