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

Annotations already declare readOnlyHint and openWorldHint, so the safety profile is covered. The description adds substantial value by enumerating the return contents (score, label, sandbox actions, findings, last safe version), which is important behavioral context since no output schema exists. It omits error behavior and any rate limits.

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?

Three sentences with zero waste: purpose and timing first, then return details, then a parameter tip. Every sentence earns its place and the most critical information is front-loaded.

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 read-only lookup tool with full schema descriptions and annotations covering safety, the description is complete. It explains what is returned (crucial since no output schema) and provides a concrete name-format example. No essential information is missing.

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 100%, setting a baseline of 3. The description adds meaningful guidance: it clarifies that package names should be used for npm/PyPI servers with a concrete example ('@modelcontextprotocol/server-github' with registry 'npm'), which goes beyond the schema's field descriptions.

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 ('Look up') and resource ('security verdict for an agent skill, MCP server or plugin'), making the tool's function clear. However, it does not explicitly distinguish itself from the sibling tool search_skills, which an agent might confuse for discovery vs. verification.

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?

Explicitly says to use it 'BEFORE installing' — a clear timing condition. It does not name alternatives or provide exclusions, but the context is unambiguous for a pre-install check.

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

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

Annotations already declare read-only and open-world, and the description adds real value beyond them: results are ranked by install count and include sandbox verdicts, which tells the agent what kind of evidence to expect. It omits result-count limits or pagination, keeping it below a 5.

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?

Two tightly packed sentences with the purpose front-loaded and the payoff second. Minor marketing flavor ('detonated in a sandbox') costs a little, but nothing is redundant.

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 no output schema, the description does carry the return-value burden and does so adequately by explaining that matches come with verdicts ranked by installs. Only edge details like result caps or empty-result behavior are unaddressed.

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% and the single query parameter is documented with examples. The description only repeats 'by keyword', adding essentially nothing beyond the schema, so baseline 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 and resource: search the KillerSkills index by keyword, returning most-installed matches with verdicts. It is clearly distinct in spirit from a single-skill check, though it never names the check_skill sibling explicitly.

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?

Gives a clear selection rationale — use this to pick a verified option rather than an unvetted one — which tells the agent when it's appropriate. It stops short of naming alternatives or stating exclusions, so it's a notch below explicit routing.

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