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extract_structured

Extract structured, cited evidence briefs from your PDF library for systematic review scaffolding. Returns a markdown table with citations per field.

Instructions

Extract a structured, cited evidence brief on a topic from the PDF library.

Elicit-style structured extraction: runs a fixed question set over the indexed
library (PaperQA2) and assembles a markdown table — one row per field, each
answer carrying its citations. Useful for systematic-review scaffolding.

Args:
    topic: The subject to extract on (e.g. "HAT passive screening sensitivity").
    fields: Optional comma-separated fields to extract. Default set covers
            population, design, sample size, outcome, finding, limitations.
    scope: Which index to query ("default" or "ph_library"). Build it first
           with index_library_pdfs().

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicYes
fieldsNo
scopeNodefault

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations, the description carries the full burden. It explains the tool runs a fixed question set and produces a markdown table with citations, but does not explicitly state that it is read-only or disclose any limitations, rate limits, or side effects.

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 (approximately 100 words), front-loaded with the main purpose, and well-structured with sections for use case and arguments. No redundant information.

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

Completeness4/5

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

The description covers arguments, use case, and prerequisite, but does not elaborate on the output format beyond 'markdown table'. Given an output schema exists, this is a minor gap; overall, it is sufficiently complete for its complexity.

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

Parameters5/5

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

The schema has 0% description coverage, but the description compensates fully by explaining each parameter: topic with an example, fields with default values listed, and scope with valid options and a prerequisite reference. This adds substantial value beyond the schema names.

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 tool extracts a structured, cited evidence brief from the PDF library, specifying the action (extract), resource (structured brief), and differentiation from siblings like search_library by mentioning Elicit-style fixed question set.

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 advises that it is useful for systematic review scaffolding and mentions a prerequisite (index the library first with index_library_pdfs()), providing clear context. However, it does not explicitly state when not to use it, which would improve guidance.

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

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