Classify Contact

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

Beyond the annotations (readOnly, idempotent, openWorld), the description reveals key behavioral traits: deterministic rule table (no API key, no model, same answer for same title), performance impact of verify_position (adds ~3 seconds and 9 requests), and privacy handling for LLM fallback (only title sent, never name). This is rich context that annotations do not cover.

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 longer than average but each sentence adds substantive information. It is front-loaded with the core purpose and then sequentially covers output, determinism, verification mode, LLM fallback, and limitations. While efficient, it could be slightly trimmed without losing value, so not a perfect 5.

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?

The description fully contextizes the tool for an agent: it explains the output format (department, seniority, rank, rule), prerequisites (APIFY_TOKEN), constraints (not a discovery tool), edge cases (null results without LLM key), and cost implications (credits, requests). No output schema exists, so the description appropriately covers expected return values. This is complete for the tool's complexity.

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?

The input schema covers all 8 parameters with descriptions (100% coverage), giving baseline 3. The description adds relational meaning by noting that full_name and company_domain are only read when verify_position is on, and that job_title is the only required field. It also clarifies the skipCache and llm_model behavior implicitly, but does not fully explain each parameter; schema still carries most detail, so a 4 is appropriate.

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 tool's function with a specific verb and resource: 'One contact in, one classified row out' and 'Give it a job title and it returns the department, the seniority level, a seniority_rank... and classification_rule.' It leaves no ambiguity about what the tool does, and the 'does not discover people' note further distinguishes its scope.

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?

Provides explicit guidance on when to use the tool and what it does not do ('This actor does not discover people: the name and title come from you'). It also clarifies prerequisites (APIFY_TOKEN, credits), optional feature contexts (verify_position needs full_name and company_domain, LLM fallback requires a key), and fallback behavior when no key is set.

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