demand_reportFREE, no payment. The aggregate of what agents have told us they are looking for, most-requested first, with the categories nothing available yet serves.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Window in days, default 30, max 90. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the report's content, ordering ('most-requested first'), and that it surfaces currently unmet categories. However, it does not state whether the operation is read-only, mention rate limits, freshness, or output format, leaving some ambiguity for a no-annotation tool.
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 a single compact sentence that leads with the most important information (free/no payment) and then states what the report contains. Minor redundancy with the title ('FREE, no payment') and the awkward phrase 'with the categories nothing available yet serves' keep it from being perfect.
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 report with one optional parameter and no output schema, the description explains the return value's nature, ordering, and coverage of unmet categories. The schema covers the days parameter, so an agent has enough to call the tool, though clearer sibling differentiation would improve completeness.
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 fully describes the single 'days' parameter, so the schema carries the semantic weight. The description does not mention the parameter or how it affects results, but because schema coverage is 100%, the baseline of 3 is appropriate.
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 identifies the tool as a free aggregate report of what agents are looking for, ordered by demand, and notes categories where nothing available exists. This goes beyond a mere restatement of the title and distinguishes the tool's role from the sibling feed/request tools, though it never explicitly names them.
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?
There is no guidance on when to choose this tool over alternatives such as discover_feeds or request_data. The phrase 'FREE, no payment' implies a cost-related consideration, but no explicit when-to-use or when-not-to-use context is provided.
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 role: discover_feeds locates feed URLs, read_feed consumes and normalizes them, demand_report surfaces aggregate market demand, and request_data handles individual data-looking requests. Even the two marketplace-related tools are separated by scale and purpose.
All tool names follow the same verb_noun snake_case pattern: discover_feeds, read_feed, request_data, demand_report. The style is uniform and each name accurately suggests its action.
Four tools is a well-scoped count for this server's purpose: two feed operations and two data-marketplace operations. Each tool earns its place without redundancy or bloat.
The core feed workflow is complete: discover where a feed lives, then read and normalize it with optional since-based polling. The demand-report and request-data tools round out the marketplace side without leaving obvious dead ends.