extract_with_llm

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

Annotations declare readOnlyHint=true and destructiveHint=false. The description adds context by explaining the default local Ollama model, the need to list models, and API key requirements for cloud providers. It does not cover failure modes or cost implications, but the added transparency is valuable.

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 only three sentences, front-loaded with the purpose, and each sentence adds essential information without waste. It is concise and well-structured.

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?

Given the tool's complexity (7 parameters, multiple providers, no output schema) and supportive annotations, the description covers key behavioral aspects. However, it does not explain the output format (e.g., structured JSON), which might be inferred but is not explicit. Still, it is largely complete.

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%, but the description adds significant usage context beyond the schema descriptions. For example, it explains default model names and the need to call list_ollama_models for Ollama, and specifies default models for each provider. This helps agents use the parameters correctly.

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 starts with a clear verb+resource: 'Extract structured data from a URL or text using a natural-language prompt.' It distinguishes itself from siblings like 'extract_structured' by emphasizing LLM-based extraction and provider flexibility (local Ollama vs cloud models).

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 provides explicit usage guidance: 'call list_ollama_models first to see what's installed and pass the name via the model parameter' and 'Pass provider: "openai" or "anthropic" with the matching API key.' It also notes that one of url/content is required. However, it lacks explicit when-not-to-use or alternative tool recommendations.

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