factory_stats
Counts in the public factory directory: total, by capability and by country, optionally for a...
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| q | No | ||
| city | No | ||
| text | No | ||
| country | No | ||
| material | No | ||
| capability | No |
Counts in the public factory directory: total, by capability and by country, optionally for a...
| Name | Required | Description | Default |
|---|---|---|---|
| q | No | ||
| city | No | ||
| text | No | ||
| country | No | ||
| material | No | ||
| capability | No |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint and destructiveHint=false, so the safety profile is covered structurally. The description contributes the useful facts that the dataset is public and the output is an aggregate count, but adds nothing on cost, result shape, or how empty filters behave.
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?
It is short and front-loads the resource, but the single sentence terminates mid-thought, which is a structural defect rather than conciseness. The intent is efficient; the execution is incomplete.
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?
With six undocumented parameters, no output schema, and a truncated description, an agent lacks what it needs to know which of q/text/city/material narrow versus group the counts. For a parameterless-required aggregation tool this leaves real ambiguity.
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 description coverage is 0% and none of the six parameters (q, city, text, country, material, capability) are described in the schema. The description only obliquely suggests that capability and country act as grouping axes; q, text, city and material receive no meaning anywhere, so the agent cannot confidently filter.
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 specific verb and resource ('Counts in the public factory directory') plus the aggregation axes (total, by capability, by country), which is meaningfully more than the name alone. It does not, however, distinguish itself from siblings like factories_search or factory_get, so an agent must infer that this is the aggregate-only variant.
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 trailing clause 'optionally for a...' is truncated, so the conditional scoping guidance is literally cut off mid-sentence. Beyond implying aggregation rather than listing, there is no explicit when-to-use or when-not-to-use-versus-factories_search guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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