tool_activity

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explicitly states 'read-only aggregation' which reassures the agent that this is a safe, non-mutating operation. It also discloses the error condition ('Analytics failed' when the response store is unreachable) and the underlying data store (Qdrant response store), which adds meaningful context beyond the basic function.

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 highly efficient and well-structured, using labeled sections ('Use when', 'Behavior', 'Returns', 'Errors') to front-load key information. Every sentence contributes value, with no repetition of schema details or fluff. The entire text is compact while covering purpose, usage, behavior, return format, and error handling.

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?

Despite having an output schema (which lowers the burden for explaining return values), the description still describes the return format: 'a text dashboard grouped by tool_name or user_email, with sample point IDs per group.' It also covers the error case and the data source. For a low-complexity read-only analytics tool, this is complete and self-contained.

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 three parameters with descriptions (limit, group_by, user_google_email), achieving 100% coverage. The description adds no new parametric details beyond what the schema already provides, such as mentioning group_by values ('tool_name' or 'user_email') which are already in the schema. Baseline of 3 is appropriate when the schema does the heavy lifting.

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: 'Show usage analytics for this server's tools: call counts, error rates, last-used times.' This clearly states what the tool does and its scope. It distinguishes itself from sibling tools like fetch_document (reading a single document) and search (discovering tools) by focusing on aggregate usage analytics.

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 an explicit 'Use when' clause: 'answering what has been used or failing lately.' It also names alternatives: 'To read an individual response, pass a sample point ID to fetch_document; to discover tools to call, use search instead.' This gives clear context for when to use this tool versus its siblings.

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