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 of behavioral disclosure. It adds useful context about the tool's output ('Returns weekly/monthly sentiment scores with notable shifts and likely causes'), but it does not cover aspects like rate limits, authentication needs, or error handling, leaving gaps for a tool with no annotation support.

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 usage context, output details, and parameter explanations in a structured 'Args' section. Every sentence adds value without redundancy.

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 (time-series analysis with three parameters) and the presence of an output schema (which reduces the need to explain return values), the description is largely complete. It covers purpose, usage, parameters, and output behavior, though it could benefit from more behavioral details like error cases or performance considerations.

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 0%, so the description must compensate fully. It explicitly lists and explains all three parameters (sources, since, granularity), including formats, defaults, and options, adding significant meaning beyond the bare schema, which lacks any descriptions.

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 with specific verbs ('Get time-series sentiment analysis') and resources ('across feedback sources'), distinguishing it from siblings like get_pain_points and search_feedback by focusing on temporal trends rather than static analysis or searching.

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 the tool ('tracking the impact of releases, bug fixes, or feature launches'), but it does not explicitly state when not to use it or name alternatives among the sibling tools, such as synthesize_feedback for aggregated insights.

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