get_trust_signals

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

Annotations declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds useful behavioral context: it runs 4 backend endpoints in parallel and returns fields that may be null if a backend call fails. No contradiction.

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 concise and well-structured, using 'USE WHEN' and 'RETURNS' to organize usage and output. Every sentence adds value without redundancy.

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 no output schema, the description fully explains the return structure (maintainer, scorecard, quality, provenance) and notes potential nulls. It addresses the tool's complexity and backend parallelism, making it complete for an AI agent.

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 coverage is 0%, so the description carries the burden. It mentions 'ecosystem' and 'package' as inputs but does not explain them beyond what the schema provides (ecosystem has enum in schema). The description lacks additional parameter details like format or examples, so it compensates only partially.

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 states the tool aggregates all non-CVE supply-chain trust signals, listing four categories (maintainer trust, OpenSSF Scorecard, quality, SLSA/Sigstore provenance). It distinguishes itself from sibling tools like get_vulnerabilities or check_package by focusing on trust signals beyond CVEs.

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 explicitly states 'USE WHEN: deep-vetting a package beyond CVEs' with specific scenarios (hardened/regulated env, SBOM/compliance, etc.). It does not provide when-not to use or explicitly name alternatives, but the context is clear.

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