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 and does well by disclosing key behavioral traits: the multi-pass LLM pipeline process, execution time (10-60 seconds), output format (up to 10 ranked pain clusters with impact scores, evidence links, suggested actions), and data collection limits (max items per source). It does not mention rate limits or authentication needs.

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 and front-loaded, starting with the core purpose, followed by details on sources, process, output, timing, and parameters. Every sentence earns its place by adding essential information without redundancy, structured in logical paragraphs.

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 the tool's complexity (multi-source synthesis with LLM pipeline), no annotations, 0% schema coverage, but with an output schema present, the description is complete enough. It covers purpose, usage, behavior, parameters, and output details, compensating for gaps in structured data and leveraging the output schema for return values.

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 fully. It successfully adds meaning beyond the schema by explaining all 4 parameters: 'sources' with examples and types, 'max_items_per_source' with default and purpose, 'since' with format and filtering role, and 'focus' with options and default. This provides complete parameter semantics.

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 specific action ('synthesize customer feedback from multiple sources into ranked pain clusters'), identifies the resources (GitHub Issues, Hacker News, App Store Reviews), and distinguishes from siblings by emphasizing multi-source synthesis versus single-source retrieval (get_pain_points, search_feedback) or sentiment analysis (get_sentiment_trends).

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 (collecting feedback from multiple sources for synthesis and ranking) and implies alternatives through sibling tool names, but does not explicitly state when not to use it or directly compare to siblings like 'get_pain_points' for single-source analysis.

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