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memorize_pdf_file

Chunks a PDF file into meaningful segments and stores them in memory for semantic retrieval based on meaning, not keywords.

Instructions

Chunk the contents of a PDF file into meaningful segments and store them in memory for later retrieval based on relevance in meaning, not just keywords.

Args:
    ctx (Context): The context of the request.
    file_path (str): The path to the PDF file.
    page (int, optional): The starting page number to read from the PDF file. Defaults to 0.
    metadata (dict, optional): Metadata to associate with the memorized content.

Returns:
    str: A message indicating success or failure of the operation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNo
metadataNo
file_pathYes
Behavior3/5

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

No annotations are provided, so the description must disclose behavior. It explains the chunking and storage action, the return value (success/failure), and mentions 'meaningful segments' but lacks details on side effects (e.g., overwriting, memory limits) or required permissions.

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 fairly concise with a one-line summary and a docstring. The Args section repeats schema info but is well-structured. Could be slightly more compact.

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?

Given the lack of output schema and annotations, the description covers basic purpose and parameters but omits details on chunking algorithm, error handling, and memory storage behavior. Sibling tool differentiation is absent.

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

Parameters4/5

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

The description adds meaning to all three parameters: clarifies 'page' is starting page (default 0) and 'metadata' is for association. However, it does not mention the default value for metadata ({'topic': 'memory'}) or further specify its structure.

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 action ('Chunk... and store in memory'), the resource ('PDF file'), and the purpose ('based on relevance in meaning, not just keywords'), distinguishing it from simple keyword-based retrieval.

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

Usage Guidelines3/5

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

The description hints at semantic memory use but does not explicitly compare to sibling tools like 'memorize_text' or 'remember_similar_texts', nor does it state when to use this tool over alternatives.

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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