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

Beyond the minimal annotations (readOnlyHint=false, destructiveHint=false), the description reveals important behavior: the feedback goes to a Slack channel, it is not a support ticket, nobody will reply, and the tool returns a `sent` field that must be checked. It even instructs the agent on how to handle the false case transparently, which is far beyond what annotations provide.

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 longer than average, but every sentence earns its place: it front-loads the core action, then provides trigger phrasing, exclusions, and a mandatory failure-handling note. The structure is logical and each clause adds necessary information rather than padding.

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?

Since there is no output schema, the description compensates by explicitly describing the returned `sent` field and the required action when it is false. It also covers the no-reply behavior and the distinction from send_feedback. Combined with the schema and annotations, the agent has all necessary information to invoke this tool correctly.

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 100%, and the schema already explains that feedback must be in the entrepreneur's own words. The description adds a little extra semantic context by enumerating accepted content types ('praise, complaint or feature request') and reinforcing the quoting rule. It does not duplicate the entire schema, but adds modest beyond-schema value.

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 states exactly what the tool does: it passes the entrepreneur's feedback, praise, complaint, or feature request to the stub team via Slack. It explicitly differentiates itself from the sibling send_feedback by saying 'This is NOT feedback about the MCP tools', and clarifies that it does not open a support ticket. This leaves no ambiguity about the tool's 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 explicit trigger examples ('send this to the team', 'tell stub that…', 'can you report this') and an offer condition ('when they voice a clear frustration or want something stub cannot currently do'). It also states what the tool is NOT for, naming send_feedback as the alternative for MCP tool feedback. This is comprehensive guidance for when to use the tool.

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