meeting_prep_prepare_meeting_contacts

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

No annotations are provided, so the description carries full burden. It does not disclose whether the tool is read-only or has side effects, nor does it mention required permissions, potential LLM calls (e.g., via the use_llm_for_company_name parameter), or the nature of formatting.

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 extremely concise—two sentences that front-load the core purpose. Every word adds value, and there is 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?

The tool is part of a multi-step meeting prep workflow, but the description does not explain prerequisites, expected outputs, or how it fits with other steps. Without output schema or behavioral details, an AI agent may lack sufficient context to use it correctly.

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 already defines each parameter with descriptions, achieving 100% coverage. The description adds no further meaning beyond the schema, so a baseline score of 3 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: it identifies the target company and primary contact, and formats contacts for research. The reference to combining Steps 21-22 from the original workflow distinguishes it from sibling tools that handle other parts of the meeting prep pipeline.

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 provide explicit guidance on when to use this tool versus its many meeting_prep siblings. It mentions combining specific workflow steps, but that assumes prior knowledge and does not give an AI agent clear selection criteria or alternatives.

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