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. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / need / examplesAdded value: +[
+ "live electricity spot prices by ZIP"
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden. It discloses that the request is free, that requests are catalogued, that nothing identifying is stored, and that the response includes availability and an MCP endpoint. This is transparent for a simple query tool, though it omits details like rate limits or what happens if the service is not available.
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 (three sentences) and front-loads the key value proposition ('FREE, no payment'). Each sentence adds distinct value: cost, purpose, and privacy/behavior. No redundant phrasing.
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, no output schema, and no annotations, the description covers the purpose, expected input, and a reasonable description of the response (availability and endpoint). It doesn't detail the exact response structure, but that is acceptable given the absence of an output schema and the tool's simplicity.
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?
Schema coverage is 100% and the single parameter 'need' is described as 'What you are looking for.' The description adds a free-form context with an example, but does not introduce additional semantics beyond what the schema already conveys. Baseline of 3 is appropriate given the high schema 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 purpose: users describe what data they want, and it returns whether the service provides it, with an endpoint if available. It distinguishes itself from siblings by explicitly covering 'anything, not just on-chain data,' which differentiates it from the specific lookup tools like gas_now or token_info.
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 implies this tool is for general or unspecified data requests, contrasting with the more targeted sibling tools. It says 'anything, not just on-chain data,' which suggests using it when the specific tool is not obvious, but it does not explicitly name alternatives or exclusion conditions.
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.