request_dataFREE, no payment. Describe in your own words the data you are trying to buy — anything, not just on-chain data — and get back whether we operate a service that supplies it, with the MCP endpoint if so. Every request is catalogued so repeatedly-requested data gets built. Nothing identifying is stored, only the words of the request.
| Name | Required | Description | Default |
|---|---|---|---|
| need | Yes | What you are looking for. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and discloses meaningful behaviors: it is free, every request is catalogued for future building, and nothing identifying is stored — only the request text. It does not detail response format or failure behavior, but the main side effects and privacy posture are transparent.
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 compact and front-loaded with the most important fact ('FREE, no payment'). Each sentence adds functional or privacy-related value; it is slightly wordy but not padded.
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 one-parameter tool with no output schema or annotations, the description adequately explains the input, the expected output (whether a service exists, with the MCP endpoint if so), the lack of cost, and the privacy behavior. It could mention result timing or examples, but nothing critical is missing.
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 already covers the single 'need' parameter at 100%, so the baseline is 3. The description adds value by specifying that the input is free-form natural language covering 'anything, not just on-chain data', and clarifies that the words themselves are what get processed and catalogued.
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?
States a clear action — describe your data need in your own words and receive whether a matching service exists, including the MCP endpoint if it does. The 'anything, not just on-chain data' wording clearly distinguishes it from the sibling on-chain-specific tools.
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?
Communicates a clear use case: a free-form discovery/cataloguing tool for any data request, not limited to on-chain data. It does not explicitly name sibling alternatives or state when not to use it, but the domain contrast with the specialized sibling tools is implied strongly enough.
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 targets a clearly different resource or moment: address state, token contract metadata, gas price, transaction status, and the two sides of the data-request marketplace. demand_report and request_data are related but cleanly split between aggregate supply/demand and a specific inquiry. No pair is likely to cause an agent to pick the wrong tool.
All names are lowercase snake_case and mostly follow a descriptive noun-noun pattern like address_info, token_info, and tx_status. request_data is the one imperative-style outlier, and gas_now deviates from the noun-info pattern, but neither is confusing. The naming is broadly predictable with only minor deviations.
Six tools is a well-scoped set for a focused on-chain data and utility MCP. Each tool has a distinct job and there is no redundancy or padding. The set feels neither too thin nor too heavy.
The paid tools cover the main pre-transaction checks (address, token, gas) and post-transaction confirmation, while the free tools form a closed loop for discovering and requesting additional data. Minor optional additions like chain metadata or transaction history are workable gaps and do not create dead ends. The surface is complete for its apparent read-heavy purpose.