Feedback Synthesis MCP

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 behavioral traits like 'single LLM pass' (implying computational approach), 'faster and cheaper' (performance/cost), and 'returns... with frequency counts and sample evidence URLs' (output format). However, it lacks details on rate limits, authentication needs, or error handling, which are important for a tool with data collection.

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 concise: first sentence states purpose, second compares to sibling, third describes output, and the 'Args' section lists parameters clearly. Every sentence adds value without waste, and it's front-loaded with key information.

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 3 parameters with 0% schema coverage, no annotations, and an output schema (which reduces need to explain returns), the description is mostly complete. It covers purpose, usage, parameters, and output hints, but could improve by mentioning potential limitations (e.g., source compatibility) or error cases for better agent guidance.

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 0%, so the description must compensate. It adds meaning by explaining each parameter: 'source' includes types and an example, 'max_items' and 'top_n' have defaults and purposes. This clarifies semantics beyond the bare schema, though it could detail 'source' constraints more (e.g., valid 'target' formats).

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's purpose: 'extract top pain points from a single feedback source.' It specifies the verb ('extract'), resource ('pain points'), and scope ('single feedback source'), and distinguishes it from sibling 'synthesize_feedback' by noting it's 'faster and cheaper' with 'single LLM pass, one source.'

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 provides usage guidance: 'Quickly extract...' implies speed, and it directly compares to 'synthesize_feedback' as an alternative for when you need a faster, cheaper option with a single source. This gives clear context on when to use this tool versus alternatives.

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