analyze_document

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

With no annotations, the description is the sole source of behavioral info. It mentions parallel processing and multiple instances, which is useful but does not cover potential side effects like cost, latency, or result aggregation. It adds value beyond the schema but remains incomplete.

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 a single, front-loaded sentence that conveys the core functionality without extraneous words. Every element serves a purpose, achieving maximum conciseness.

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 tool with 9 parameters and no output schema, the description provides only a high-level overview. It lacks detail on output format, error handling, or prerequisites, leaving gaps that could hinder correct invocation in complex scenarios.

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?

Schema coverage is 100% with parameter descriptions, setting a baseline of 3. The tool description adds cohesive context by explaining the parallel chunking approach, which gives meaning to parameters like chunk_size, overlap, and parallel_instances, elevating understanding beyond individual schema entries.

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 clearly states the action (analyze), the resource (large documents), and the method (parallel processing with multiple model instances). This precisely identifies the tool's function and distinguishes it from sibling tools like chat_with_model, compare_models, etc.

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 does not indicate when to use this tool versus alternatives or when not to use it. It lacks any usage context or prerequisites, leaving the agent to infer applicability without guidance.

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