Pre-Check Paper

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

With no annotations, the description carries the full burden. It discloses that the tool is zero-LLM-cost, free, sub-second response, and backed by a local library. This gives the agent confidence that it is a safe read-only operation without side effects, though it does not explicitly state no mutations occur.

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 extremely concise: two sentences front-loading the purpose and usage. Every sentence adds value, with no redundant or vague phrasing.

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 (5 parameters, no output schema, no annotations), the description covers the core functionality: what it returns (tier, field, confidence band) and when to use it. It lacks details on error handling or authentication, but for a simple scoring tool, it is adequately complete.

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?

All 5 parameters have descriptions in the schema (100% coverage), so the schema already provides parameter semantics. The description adds only general context (e.g., returns probabilities, fast) but does not elaborate on parameter-specific details beyond the schema.

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: run a pre-submission scoring of an academic paper, returning predicted tier, field, and confidence band. It uses specific verbs and resources, and the use of 'pre-check' differentiates it from siblings like 'check_duplicate_publication' and 'recommend_journals'.

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 explicitly tells when to use this tool: 'when the user wants a fast sanity-check on whether a paper is ready to submit, or which tier of journal to target.' It does not include when-not-to-use or alternatives, but the context is sufficient for an AI agent to decide.

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