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. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / days / examplesAdded value: +[
+ "30"
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the disclosure burden. It usefully discloses that the tool is free, returns an aggregate rather than individual requests, sorts by demand, and includes categories with nothing available yet. It does not explicitly state read-only behavior, but for a report tool this is a minor gap.
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 short but not fully polished: 'FREE, no payment' partly repeats the title's '(free)', and the phrase 'categories nothing available yet serves' is awkward. It is still compact, but the wording could be clearer.
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 one-optional-parameter report with no output schema, the description provides enough context: it explains the free aggregate output, the ordering, and the inclusion of unserved categories. The remaining detail about the days parameter is covered by the schema.
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 already fully documents the only parameter: days window, default 30, max 90. The description itself adds no parameter-level meaning, so the baseline of 3 applies.
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 identifies the resource as an aggregate of what agents are looking for, states the ordering ('most-requested first'), and mentions unserved categories. This distinguishes it from the package_* siblings, though it does not explicitly differentiate from request_data.
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?
It implies when the tool is useful: get a free aggregate view of agent demand. However, it gives no explicit when-to-use/when-not-to-use guidance and names no alternatives, leaving routing to inference.
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.
package_audit overlaps with package_info and package_vulnerabilities because it bundles their data, but the descriptions clearly frame audit as the combined decision-oriented call and the others as individual fact or advisory lookups. demand_report and request_data are also distinct: one is aggregate market demand, the other is a single request submission.
Three tools share the package_ prefix with descriptive suffixes (audit, info, vulnerabilities), while demand_report and request_data cover the demand-side features without the prefix. All names are snake_case and readable, so the deviation is minor rather than chaotic.
Five tools is a well-scoped size for a package intelligence server. Each tool maps to a meaningful workflow: package facts, vulnerabilities, a composite audit, individual data requests, and an aggregate demand report.
The package workflow is covered end-to-end: check package info, check advisories, and get a combined audit verdict before adding or pinning a dependency. The demand side also feels complete with both individual request_data and the aggregated demand_report.