Feedbucket MCP Server

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

With no annotations provided, the description carries the full burden. It discloses that the tool 'automatically optimizes data to prevent overwhelming responses' and mentions screenshot URLs in the 'resource' field, which are useful behavioral insights. However, it doesn't cover important aspects like authentication requirements, rate limits, error conditions, or whether this is a read-only operation (though 'fetch' implies it).

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 appropriately sized with two sentences that each earn their place. The first sentence states the core purpose and key features, while the second provides important usage guidance about sibling tools. It's front-loaded with the main functionality and avoids unnecessary repetition.

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 tool with 8 parameters, no annotations, and no output schema, the description provides adequate but incomplete context. It covers the purpose, filtering capabilities, and relationship to feedback_get, but doesn't address the return format, pagination behavior beyond the offset parameter, error handling, or authentication requirements. The lack of output schema means the description should ideally explain what the response contains.

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?

The schema description coverage is 100%, so the schema already documents all 8 parameters thoroughly. The description adds minimal parameter semantics beyond what's in the schema - it mentions 'intelligent filtering' and 'AI optimization' which relate to the parameters but don't provide additional syntax or format details. The baseline of 3 is appropriate when the schema does the heavy lifting.

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 fetches feedback items with intelligent filtering for AI consumption, distinguishing it from sibling tools like feedback_get (which retrieves full details) and feedback_stats (which provides statistics). However, it doesn't explicitly differentiate from feedback_comment or feedback_resolve beyond the 'list' vs 'comment/resolve' distinction.

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 provides clear context for when to use this tool (fetching all feedback items with filtering) and explicitly mentions using feedback_get for retrieving full details and viewing screenshots. It doesn't specify when NOT to use it or compare with all sibling alternatives like feedback_stats or feedback_comment.

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