ingest_pdf

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

With no annotations provided, the description carries the burden of behavioral disclosure. It transparently outlines the multi-step process: extracting concepts/equations/parameters, matching against existing knowledge, reasoning about relationships, and committing changes. This reveals that the tool modifies the knowledge graph, which is crucial behavioral information.

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 well-structured with a one-sentence summary, a detailed process description, and an Args/Returns section. It is not overly verbose for a tool with multiple steps and parameters, and the most critical information (purpose, parameters) is front-loaded.

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 complex tool involving extraction, matching, reasoning, and committing changes, the description provides a solid overview of the workflow and output. It benefits from an output schema (stated as present), so return value details are not needed. Minor gaps include lack of prerequisites like file accessibility or idempotency, but the description is largely complete for an agent to understand the tool's role.

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?

Despite 0% schema description coverage, the description includes an Args section that explains each parameter: file_path as an absolute path, extraction_model and reasoning_model as optional model names. This adds meaningful context beyond the bare string types and defaults, helping the agent understand what values to provide.

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 opens with a specific verb 'Ingest a PDF document into the knowledge graph,' clearly identifying the resource and action. It further details the steps (extracts concepts, equations, parameters; reasons about relationships) that distinguish it from sibling tools like analyze_circuit_image or query_knowledge.

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 clearly implies this tool is for ingesting PDF documents into the knowledge graph, providing a clear context for when to use it. It lacks explicit exclusions or mention of alternative tools for other input types, but the PDF-specific scope prevents confusion with image-analysis or query tools.

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