find_dataDescribe the data you need in plain language (e.g. 'Apple risk factors 2023', 'is this token a honeypot', 'is this email deliverable', 'read this page'). Searches this server's datasets first, then the whole Professor Sausages catalog, and returns matching endpoints with method, URL, price, and how to call them. Free.
| Name | Required | Description | Default |
|---|---|---|---|
| task | Yes | What you're trying to find or do, in your own words |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the search order (server datasets first, then catalog), the return payload (method, URL, price, how to call), and that it is free. It does not cover failure modes or pagination, but for a non-destructive search tool this is adequate transparency.
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 three sentences, front-loaded with the core instruction, and contains no redundant information. Every sentence adds value: what to describe, the search scope, and the output format/pricing.
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?
For a simple tool with one parameter and no output schema, the description covers the core behavior, return type, and scope well. It could mention what happens if no matches are found, but the tool is simple enough that this is not a major gap.
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 schema has a single parameter 'task' with a basic description, but the tool description enriches it significantly by explaining how to phrase requests ('in plain language') and providing varied examples. This adds meaning beyond the schema, despite the schema's 100% coverage.
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 the tool's function: it takes a natural language description of data needs, searches the server's datasets and the Professor Sausages catalog, and returns matching endpoints with method, URL, price, and call instructions. This distinguishes it from the sibling tools, which are specific data endpoints, by positioning it as a discovery meta-tool.
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 provides clear context with multiple examples ('Apple risk factors 2023', 'is this token a honeypot') and implies use when you need to find the right data source, but it does not explicitly state when not to use it or name alternative tools. This is clear context without exclusions.
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.
Each tool has a clear, distinct purpose: address screening, chain info, ENS resolution, data discovery, gas routing, gas pricing, NFT scam checking, pricing metadata, data requests, spot prices, swap quotes, and token safety. Although gas_compare and gas_price both involve gas, one ranks chains while the other gives fees for a single chain; token_safety and nft_scam_check target different asset types. No two tools are likely to be confused.
Names are all lowercase snake_case but follow no single convention. Some are verb-first (find_data, request_data, swap_quote), some noun-first with a verb (address_screen, ens_resolve, gas_compare), and several are noun-noun compounds (chain_info, gas_price, spot_price, token_safety). This mixed pattern makes the naming feel inconsistent, even though each name is readable.
With 12 tools, the server sits comfortably in the well-scoped range for a blockchain data/utility provider. Each tool addresses a distinct need, and the count is not excessive for the breadth of on-chain operations covered. Every tool appears to earn its place without redundancy.
The tool surface covers a broad set of on-chain needs: screening (address, token, NFT), pricing (spot, swap, gas), identity (ENS), chain metadata, and meta-tools for discovery and requests. Minor gaps exist, such as no direct transaction/block inspection or portfolio-level functions, but these are not core to the server's apparent purpose and agents can work around them using find_data to locate additional endpoints.