list_user_queries

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

With no annotations, the description carries full behavioral burden. It discloses that it returns 'extracted query text with metadata' and mentions the exclude_internal filter, which is useful. But it omits core behavioral traits like read-only status, pagination, default limit, or what metadata precisely appears. The lack of side-effect disclosure is acceptable for a list tool, but the description could add more operational context.

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 well-structured: a one-sentence purpose, a block of example questions, then parameter-specific notes. It is not overly verbose, and every section earns its place. The example list could be trimmed slightly, but overall it is efficient and scannable.

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 11 parameters and no schema descriptions, the description is incomplete. It provides good usage context but omits explanations for over half the parameters, making it hard to understand the full range of filtering and grouping options. The output schema helps define return shape, not parameter semantics.

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 description coverage is 0%, so the description must compensate. It explains domain, group_by, and exclude_internal, but leaves 8 other parameters (name, tags, limit, project, user_id, end_date, start_date, time_range) with no explanation. The description adds meaning to only a subset of the parameters, failing to fully compensate for the empty schema.

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 opens with a specific verb and resource: 'List user queries extracted from trace inputs.' This clearly states what the tool does and distinguishes it from sibling tools like list_users (which lists users) or fetch_sessions (which lists sessions). Concrete example questions ('What are merchants asking?') reinforce the intended purpose.

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?

It provides clear usage context with example queries and explicit guidance on using the domain parameter: 'Use this when the user asks about a company/org by name instead of a specific user email.' This helps with parameter choice. However, it does not explicitly name alternative tools or exclusionary conditions, so it's not a perfect 5.

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