Paperlib MCP

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

No annotations are provided, so the description carries full burden. It mentions 'assigning precomputed features' but doesn't disclose behavioral traits such as whether this is a read-only or mutating operation, permission requirements, side effects (e.g., overwriting existing features), rate limits, or error handling. The description is minimal and lacks critical operational context for a tool that likely modifies data.

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 a single, efficient sentence in Chinese that directly states the tool's purpose without unnecessary words. It's appropriately sized for a simple tool, though it could be more structured (e.g., by explicitly listing features). No fluff or redundancy is present.

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 likely involves data mutation (assigning features), no annotations, and an output schema exists (which may cover return values), the description is incomplete. It lacks behavioral details (e.g., mutation effects, error cases) and parameter explanations, but the presence of an output schema reduces the need to describe returns. The description is minimal but not entirely inadequate for a basic understanding.

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 1 parameter ('scope') with 0% description coverage, and the tool description adds no information about parameters. It doesn't explain what 'scope' means, its possible values (beyond the default 'all'), or how it affects feature assignment. With low schema coverage, the description fails to compensate, leaving parameters undocumented.

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 action ('分配' meaning 'assign') and the resource ('claims' with '预计算特征' meaning 'precomputed features'), specifying what gets assigned (primary_topic, outcome/treatment family, etc.). It distinguishes from many siblings by focusing on feature assignment rather than building, exporting, or managing documents/evidence. However, it doesn't explicitly differentiate from tools like 'compute_topic_df_cache' which might involve similar computations.

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?

No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites (e.g., whether claims must exist or features be precomputed), exclusions, or comparisons to sibling tools like 'compute_topic_df_cache' or 'build_claim_groups_v1_2'. Usage is implied only by the action of assigning features to claims.

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