extract_customer_info

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

Without annotations, the description carries the burden of disclosure. It states the tool uses AI to read all messages and extract info, implying read-only behavior. It does not mention permissions, side effects, or what happens if extraction fails. This is adequate but not rich.

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?

Two sentences: first defines action, second explains method and use case. Every word earns its place with no redundancy or filler.

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 presence of an output schema (not shown), the description adequately covers the tool's purpose and parameters. It could mention potential failure modes (e.g., no conversation history) but overall is thorough for a simple extraction tool.

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%, so baseline is 3. The description does not add any extra information about parameter usage beyond what the schema already provides (customer_id required, org_id optional). No additional semantic value.

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 verb 'extract' and the resource 'contact information from conversation history', listing specific fields (name, phone, email, address). It distinguishes from siblings like get_customer (which retrieves stored data) and create_customer by focusing on AI extraction from messages.

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 clear context via 'reads all messages with this customer' and suggests a use case ('Great for auto-filling CRM records'). However, it does not explicitly guide when not to use this tool or mention alternatives like get_customer for already-extracted data.

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