security_fetch_package_maintainer_history

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description adds significant behavioral context beyond annotations: data sources (PyPI JSON API and npm registry), refresh behavior (each call, 1-hour cache), rate limit (60/minute), authentication requirements (no auth), and a detailed list of outputs including the anomaly_score range and maintainer_health values. No contradiction with annotations.

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 paragraph covering purpose, sources, caching, outputs, rate limit, auth, audience, and feedback. It is well-structured and front-loaded with the main purpose. Each sentence adds value, though it could be slightly more concise.

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?

Given the presence of an output schema (mentioned in context signals), the description lists all key return fields and their types (e.g., anomaly_score 0.0–1.0, maintainer_health categorical values). It also explains data freshness, caching, and API usage. The tool is fully specified for its intended use case.

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%, so the input schema already documents both parameters. The description adds minimal value with an example ('e.g. requests') but does not provide additional meaning beyond what the schema offers. Baseline is 3.

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 explicitly states the tool's purpose: 'Analyse ownership and release history for an npm or PyPI package to detect supply-chain risk.' It identifies the verb ('Analyse'), resource (npm or PyPI package), and outcome (detect supply-chain risk). It distinguishes from sibling tools by focusing on maintainer history with specific outputs like anomaly_score and maintainer_health.

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 specifies the target user: 'For security engineers auditing open-source dependencies before inclusion in production builds.' It provides a fallback instruction to report feedback if the tool's response is unsuitable. While it does not explicitly state when not to use it or list alternatives, the context is clear and actionable.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.