read_paper

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

With no annotations, the description takes on the disclosure burden. It reveals useful behavioral traits: chunked return, source fallback priority, and the need for at least one identifier. It does not cover errors, concurrency limits, or exact output format, but the core behavior is transparent.

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 compact and well-structured: purpose in the first line, then source precedence, then an Args list. Every sentence contributes useful information without filler.

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?

For a tool with five optional-looking parameters and no output schema, the description covers identifiers, source precedence, and chunk control. It does not address edge cases like conflicting identifiers or error behavior, but the essential call contract is present.

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 0%, but the description compensates fully by explaining every parameter: doi, arxiv_id, pmid, pmcid, and max_chunks. It also gives realistic examples for identifiers, making parameter usage unambiguous.

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 verb (获取/resad) and resource (paper full text, chunked), and the purpose of deep reading. It does not explicitly name sibling tools it differs from, so it stops just short of full differentiation.

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 gives a usable precondition: at least one identifier must be provided. It also lists the source priority order, which helps the agent reason about behavior. However, it does not explicitly say when to prefer this over siblings like search_literature or test_fulltext.

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