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

Annotations indicate this is a write operation (readOnlyHint=false) but are otherwise minimal. The description adds meaningful behavioral context beyond the annotations: submissions are reviewed by humans, past duplicate proposals waste reviewer time, and specific report formats are more actionable. This helps the agent understand the real-world impact and expectations of the submission.

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 well-structured and front-loaded with the core purpose, followed by actionable guidance. Each sentence contributes useful information, though the list of past duplicate proposals (IIIF manifests, Content Search, DTS) is slightly detailed. Overall, it is efficient without being overly verbose.

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 simple feedback submission tool with three parameters and no output schema, the description provides sufficient context for an agent to invoke it correctly. It explains the submission target, reviewer expectations, and how to maximize usefulness. The only minor gap is the absence of explicit clarification that name and email are optional, but the schema already encodes that.

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 only 33%, so the description carries some responsibility for parameter meaning. It enriches the message parameter by specifying what content reviewers find valuable, such as noting which docs were checked and including record IDs/reproducing queries. However, it does not clarify the optional name and email parameters, which remain undocumented in both the schema and description.

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 submits feedback, bug reports, or feature requests to the Source Library team, using a specific verb and resource. It is distinct from sibling tools like search_library or propose_collection, so an agent can immediately identify its purpose.

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 gives clear context for when to use the tool: for feedback, bugs, and feature requests. It also provides concrete usage guidance—read the referenced docs before proposing new functionality, state which docs were checked, and prefer actionable bug reports with record IDs and reproducing queries. It does not explicitly contrast with sibling tools, but the context is strong.

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