query

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

No annotations provided, so the description fully carries the burden. It clearly states that the result can be very large and consume context limit if not filtered, and that it returns all data if no filters are applied.

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?

Well-structured with bullet points and clear warnings. Slightly long but every sentence adds value, and the format aids readability for an AI agent.

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?

Comprehensive for a query tool with an output schema. Warns about large data, explains filtering, and references sibling tools. Adequately prepares the agent for correct invocation.

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?

All parameters are documented in the schema (100% coverage). The description adds value by explaining the impact of not filtering (tags, steps) and how to use start_step/end_step for ranges, but the schema already covers basics.

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?

Explicitly states 'Query scalar data from a TensorBoard event file', clearly identifying the action and resource. It differentiates from siblings like list_tags and tag_steps which deal with metadata.

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 this tool (to get scalar data) and when to avoid (large outputs). Recommends using tags and step filters, and references sibling tools (find_events, list_tags, tag_steps) for prerequisites.

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