Generate fake company data for testing, including localized company names and configurable quantities. Use it to populate test datasets and simulate business records without relying on real companies.
Generate fake company data for testing.
| Name | Required | Description | Default |
|---|---|---|---|
| locale | No | Locale code (default: en_US). | |
| quantity | No | Number of companies (max 100, default 5). |
Changes observed during successful MCP inspections.
v0.1.11Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden, and it discloses almost nothing beyond the one-line purpose. It does not state whether output is random/non-deterministic, whether it is safe and side-effect free, or what a returned company record looks like.
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?
A single front-loaded sentence with zero waste. Nothing redundant or padded.
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?
The tool is simple with only two fully documented optional params, but with no output schema the description should ideally hint at the shape of the generated company data and its randomness. It is adequate but leaves the return contract to inference.
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 100%, with locale and quantity fully documented in the schema. The description adds no parameter meaning beyond that, 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?
States a specific verb+resource ('Generate fake company data') and adds use-case intent ('for testing'). It implicitly differs from siblings like faker_persons and faker_texts via the 'company' resource, but does not explicitly differentiate 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?
The phrase 'for testing' implies the context of use, but there is no explicit guidance on when to choose this over faker_persons, faker_texts, or lorem generators, and no exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.