intelligence_summarize_channel

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

Discloses that the server has no API keys and relies on MCP sampling from the client, plus a fallback returning raw messages when sampling is unsupported. This notably expands on the readOnly/idempotent annotations by exposing a dependency on client capabilities.

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?

Uses bold section headers to separate Purpose, When to use, Returns, and Fallback. Every sentence conveys meaningful information 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?

Covers purpose, usage triggers, return shape, and critical fallback behavior for clients lacking sampling. This is especially valuable given the tool's dependency on client-side LLM capabilities.

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 already describes all three parameters (limit, style, channel_id) with defaults and constraints; the description adds no per-parameter semantics. Baseline score due to full schema coverage.

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?

Clearly states the action ('Summarize') and resource ('recent messages in a Discord channel'), with the additional qualifier of using the client's LLM. This distinguishes it from sibling intelligence tools and directly matches the tool name.

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?

Provides concrete example user queries ('what was discussed in #X?', 'catch me up', 'TL;DR') that signal when to invoke. Does not explicitly exclude cases or name alternatives, but the use-case framing is clear.

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