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lib_read

Read a locally stored paper, optionally a specific section by heading. Without a section, get the abstract and list of headings to navigate content deliberately.

Instructions

Read a paper held locally, optionally one section of it.

section matches a heading case-insensitively, e.g. 'methods', 'surface plasmon', 'results'. With no section, returns the abstract plus a list of available headings, so a long paper can be read deliberately rather than dumped into context.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sectionNo
max_charsNo
identifierYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.1/5.0
Behavior4/5

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

There are no annotations, so the description carries the full burden of behavioral disclosure. It explains non-obvious behavior: section matching is case-insensitive, and omitting section returns the abstract plus available headings. This is valuable transparency. It does not mention the effect of max_chars or error handling, but the core read behavior is well disclosed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and front-loaded: the core purpose is in the first sentence, followed by just enough detail about section matching and the deliberate-reading fallback. Every sentence earns its place, and the examples are practical and clear.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The output schema exists, so return values need not be explained. However, with three parameters and zero schema descriptions, the description should cover all parameters to be complete. It handles identifier and section well, but omits max_chars semantics, which could materially affect how the agent invokes the tool. Overall useful but not fully complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It thoroughly explains the 'section' parameter with examples and fallback behavior, and 'identifier' is clearly implied by 'Read a paper'. However, 'max_chars' is not described at all, leaving its truncation or output-limiting behavior undefined. Partial compensation, with one significant gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb and resource: 'Read a paper held locally', with the optional scope of reading one section. It also distinguishes itself from sibling tools by emphasizing 'locally', which separates it from fetch/search/status tools. The purpose is immediately understandable and specific.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear context that this tool is for locally held papershare, and it explains the recommended usage pattern: starting with no section to get abstract and headings, then reading a specific section. It does not explicitly name alternatives like lit_get or lit_fetch, but 'held locally' provides a useful discriminator.

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