Paperlib MCP

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 adds some context: it mentions deterministic generation without LLM, writes to a database, and has a rebuild option. However, it doesn't cover important aspects like permissions needed, rate limits, error conditions, or what '写入数据库' (writes to database) entails operationally. The description doesn't contradict annotations (none exist), but it's incomplete for a mutation tool.

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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by a structured Args section. There's no wasted text, though the Returns section could be integrated more smoothly. Every sentence earns its place by adding value, making it efficient for an AI 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 a mutation tool with 4 parameters, 0% schema coverage, no annotations, but an output schema exists, the description is moderately complete. It covers parameters well and states the return values, but lacks behavioral context like side effects, idempotency, or error handling. The output schema reduces the need to explain returns, but more operational guidance would help for a database-writing tool.

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?

Given 0% schema description coverage, the description compensates well by explaining all 4 parameters in the Args section. It clarifies that 'topic' and 'comm_ids' are mutually exclusive ('二选一' - choose one), describes 'outline_style' with a default, and explains 'rebuild' as whether to rebuild existing outlines. This adds meaningful semantics beyond the bare schema, though it doesn't detail possible values for 'outline_style' beyond the default.

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: '生成综述大纲（确定性，无 LLM）从 topic 或 comm_ids 生成可复现的综述大纲结构，写入数据库.' It specifies the verb ('生成' - generate), resource ('综述大纲' - review outline), and key constraints ('确定性，无 LLM' - deterministic, no LLM). However, it doesn't explicitly differentiate from sibling tools like 'draft_lit_review_v1' or 'get_outline_templates', which might have overlapping functionality.

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 through its parameter explanations (e.g., 'topic 或 comm_ids 二选一' - topic or comm_ids choose one), suggesting when to use this tool based on available input. However, it lacks explicit guidance on when to choose this tool over alternatives like 'draft_lit_review_v1' or 'get_outline_templates', and doesn't mention prerequisites or exclusions beyond the parameter constraints.

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