Paper Distill MCP Server

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 effectively describes key behaviors: it generates a prompt (not performs summarization), returns a dict with specific fields, and includes delegation logic to save token costs. It covers output structure and performance considerations, though it lacks details on error handling or rate limits.

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 well-structured and concise, with three focused paragraphs: purpose, output details, and delegation guidance. Every sentence adds value—no redundancy or fluff—and it's front-loaded with the core function, making it efficient for an agent to parse.

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 moderate complexity, no annotations, and the presence of an output schema (which handles return values), the description is complete. It covers purpose, usage context, behavioral aspects like delegation, and output semantics, leaving no significant gaps for an agent to operate effectively.

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 input schema has 100% description coverage for its single parameter ('custom_focus'), so the baseline is 3. The description adds value by implying the parameter's role in the context of 'today's batch' and summarization, though it doesn't explicitly mention 'custom_focus'. This compensates slightly, raising the score to 4, as it enhances understanding beyond the schema's technical details.

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: 'Generate a summarization prompt for unsummarized papers in today's batch.' It specifies the verb ('Generate'), resource ('summarization prompt'), and scope ('unsummarized papers in today's batch'). However, it doesn't explicitly differentiate from sibling tools like 'generate_digest' or 'prepare_review', which might have overlapping functions in a research workflow.

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 on when to use this tool: for generating prompts for unsummarized papers in today's batch. It also includes guidance on delegation ('If summarizer is NOT "self"...'), which helps distinguish usage scenarios. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools, such as 'generate_digest' for direct summarization.

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