search_aws_knowledge

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

No annotations exist, so the description carries the full disclosure burden, and it delivers: parallel source querying, degraded single-source errors becoming entries in `errors` rather than failing the call, intentionally short snippets, the Builders' Library redirect quirk, and the caveat that Builder Center results carry no article body. These are exactly the behaviors an agent needs to calibrate expectations.

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 long but each paragraph earns its place: purpose, in-scope uses, exclusions, a redirect quirk callout, fail-degrade behavior, and a follow-up routing to read_aws_content. It is front-loaded with the purpose statement and organized in clear sections. There is minor redundancy (the Builders' Library is mentioned in the in-scope list and again in the callout), but the callout adds vital context, so the verbosity is largely justified.

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?

This is a search tool with only 3 parameters and an output schema, and the description covers everything an agent needs: what to search, when to search, when not to search, what each source maps to, failure behavior, and what to do with the results (pass URLs to read_aws_content). Combined with schema + output schema coverage, nothing material 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%, so the baseline is 3. The description adds real value: it maps source values to intent ('repost' → troubleshooting threads; 'kiro' → steering/hooks/specs/MCP config; 'builder' → Builders' Library and community write-ups), explains why `builder` is non‑obvious in the redirect paragraph, and gives advice on how to proceed from the (short) snippets. It doesn't add much for `limit`, but the source semantics are significantly enriched.

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 opening sentence explains the specific action and scope: 'Search AWS knowledge sources that the official AWS Knowledge MCP Server does not cover.' It then enumerates the concrete resource categories ('repost', 'kiro', 'builder'), and contrasts itself with both the official server and its sibling read_aws_content, leaving no ambiguity about what this tool is and is not.

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 is exemplary: it gives a 'Use this for:' list with concrete examples (error threads, Kiro config, Builders' Library), then a 'Do NOT use this for:' list (service docs, API references, AWS blogs, What's New, etc.) with the explicit alternative recommendation: 'the official AWS Knowledge MCP Server ... is the better answer for them.' It also routes follow-on behavior to read_aws_content. Nothing is left to inference.

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