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ask_library

Answer questions from your personal PDF library with synthesized, cited summaries using PaperQA2.

Instructions

Answer a question using the user's indexed PDF library via PaperQA2.

Searches the pre-built index (see index_library_pdfs()) and returns
a synthesised answer with citations from the source papers.

Args:
    question: Natural language question to answer from the library.
    top_k: Number of source passages to retrieve before synthesis (default 5).
    scope: Which index to query. "default" = full library. "ph_library" = PH background only.
           Build the index first with index_library_pdfs(scope=<scope>).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
questionYes
top_kNo
scopeNodefault

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

The description explains that the tool searches a pre-built index and returns a synthesized answer with citations. It implies read-only behavior, but since no annotations are provided, the description could explicitly state that no modifications occur. Nonetheless, it offers sufficient behavioral context.

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

Conciseness4/5

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

The description is well-structured with a brief overview followed by an Args section. It is mostly concise, though the repetition of 'see index_library_pdfs()' could be tightened. Overall, it is efficient and front-loaded.

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

Completeness5/5

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

Given the presence of an output schema (not shown but indicated), the description covers prerequisites (index must be built), parameter details, and the core behavior. It is complete for a query tool without requiring additional context.

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?

With schema description coverage at 0%, the description fully compensates by explaining each parameter in natural language: question ('Natural language question to answer from the library'), top_k (purpose and default), and scope (enumerated values with a note about building the index). This adds substantial meaning beyond the schema.

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's purpose: 'Answer a question using the user's indexed PDF library via PaperQA2.' It specifies the verb (Answer), resource (indexed PDF library), and method (PaperQA2). This distinguishes it from sibling tools like search_library, which likely performs raw searches without synthesis.

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 provides guidance on when to use the tool: after building the index via index_library_pdfs(). It mentions the index must be pre-built and references the appropriate sibling tool. However, it does not explicitly state when not to use it or compare to alternatives like search_library.

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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