request_dataFREE, no payment. Describe in your own words the data you are trying to buy — anything, not just packages — 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 fully carries behavioral disclosure. It states the operation is free, that requests are catalogued, that repeatedly-requested data may get built, and that no identifying information is stored. These are meaningful side-effect and privacy disclosures beyond the schema.
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, front-loads the most important fact (free), and every sentence contributes meaning. There is minimal redundancy and no filler.
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 single-parameter tool with no output schema and no annotations, the description covers the request format, the response behavior, the privacy implications, and the relationship to package tools. Nothing essential 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?
Schema coverage is 100%, so the baseline is 3. The description adds value by instructing the agent that 'need' should be a free-form, natural-language description of the desired data, and that it can be about anything, not just predefined packages.
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 a specific action: describe the data you want to buy and receive whether the service can supply it, with the MCP endpoint if available. It explicitly distinguishes itself from package-focused siblings by saying 'anything, not just packages.'
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 is the tool for custom or unlisted data requests, contrasting with the package-oriented sibling tools. It does not name alternatives explicitly or give a precise when-to-use/when-not-to-use rule, but the 'not just packages' phrasing provides clear context.
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 clearly distinct purpose: demand_report and request_data handle market demand and data procurement, while package_info, package_vulnerabilities, and package_audit cover package details, known advisories, and a combined safety verdict. The audit tool's relationship to the other two is explicitly clarified by recommending it over separate calls, removing any ambiguity.
All tool names follow the same snake_case pattern of a subject noun followed by a focus noun (demand_report, package_audit, package_info, package_vulnerabilities, request_data). This consistent convention makes the set predictable and easy to navigate.
With 5 tools, the server is well-scoped for its dual purpose of package intelligence (info, vulnerabilities, audit) and data market signals (demand report, data request). Each tool serves a distinct need without excess or deficiency, falling within the ideal 3–15 range.
The surface covers the full workflow for package safety checks (look up info, check vulnerabilities, get a combined verdict) and data procurement (see market demand, request specific data). No obvious gaps exist for the stated purpose of providing dev package intelligence and a data marketplace.