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 explains the ranking formula and data dependencies, which adds useful context beyond basic functionality. However, it lacks details on performance, error handling, or output format, leaving gaps in behavioral understanding for an AI agent.

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 and method, followed by specific details on factors and data sources in two concise sentences. Every sentence adds essential information without redundancy, making it efficient and well-structured for quick comprehension.

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 complexity of a ranking tool with a weighted formula and external data dependencies, the description is mostly complete. It explains the algorithm and inputs, and with an output schema present, return values need not be detailed. However, it could benefit from more guidance on usage scenarios or limitations to fully cover context.

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%, so the schema already documents the parameters well. The description adds value by specifying that 'papers' should come from 'search_papers' and mentioning the use of external files for scoring, which clarifies parameter semantics beyond the schema. No parameters are left unexplained, but the description compensates with contextual insights.

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 specific action ('Score and rank papers') and the method ('using 4-factor weighted formula'), distinguishing it from sibling tools like search_papers or filter_duplicates. It provides concrete details about the ranking algorithm, making the purpose explicit and differentiated.

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 implies usage by mentioning data sources (topic_prefs.json, papers.jsonl) and referencing search_papers as the source for input papers, but it does not explicitly state when to use this tool versus alternatives like filter_duplicates or prepare_review. No exclusions or specific contexts are provided, leaving usage somewhat ambiguous.

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