Discourse MCP

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

Annotations already cover key behavioral traits (readOnlyHint: true, destructiveHint: false, etc.), so the bar is lower. The description adds useful context about visibility ('visible to the current user') and return content ('category names and topic counts'), but does not disclose additional behaviors like rate limits or authentication needs beyond what annotations imply.

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 front-loaded with the core purpose in the first sentence, followed by a brief utility note. Every sentence earns its place without redundancy, making it appropriately sized and efficient for an AI agent to parse quickly.

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 low complexity (0 parameters, no output schema) and rich annotations, the description is mostly complete. It covers purpose, usage context, and output details, but lacks information on potential limitations (e.g., pagination or sorting) that could be relevant for a list operation, preventing a perfect score.

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?

With 0 parameters and 100% schema description coverage, the baseline is high. The description adds no parameter-specific information, which is fine since there are none. It effectively compensates by clarifying the output semantics, earning a score above the minimum viable.

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 verb ('List') and resource ('categories visible to the current user'), specifying scope and distinguishing it from siblings like discourse_list_tags or discourse_list_user_posts. It explicitly mentions what is returned ('category names and topic counts'), making the purpose specific and unambiguous.

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 ('Useful for discovering where to post or search'), which helps guide usage. However, it does not explicitly state when not to use it or name alternatives (e.g., discourse_search for broader queries), so it falls short of a perfect score.

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