get_events

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 describes the polling behavior and optional filtering, which is useful, but it doesn't cover aspects like rate limits, authentication needs, error handling, or what the return format looks like (e.g., list of events, pagination). For a polling tool with zero annotation coverage, this leaves gaps in understanding its full behavior.

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 concise and well-structured, consisting of two sentences that efficiently convey the tool's purpose and parameter usage. Every sentence earns its place: the first states the core function, and the second explains key parameters. There is no wasted text, making it easy to parse and understand quickly.

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?

Given the tool's moderate complexity (polling with filters), no annotations, and no output schema, the description is somewhat complete but has gaps. It covers the basic purpose and parameter guidance, but it doesn't explain the return values (e.g., event structure, error responses) or behavioral details like polling intervals. For a tool with no output schema, more context on what to expect would improve completeness.

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 has 100% description coverage, so the schema already documents all parameters ('consultation_id', 'since_id', 'event_type') with clear descriptions. The description adds marginal value by reinforcing the use of 'since_id' for new events and 'event_type' for filtering, but it doesn't provide additional syntax, format details, or examples beyond what the schema states. Baseline 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 clearly states the tool's purpose: 'Poll consultation events with optional filters.' It specifies the verb ('poll') and resource ('consultation events'), distinguishing it from siblings like 'emit_event' (which creates events) or 'consultation_report' (which generates reports). However, it doesn't explicitly contrast with all siblings, such as 'read_state', which might also retrieve event-related data.

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 clear context on when to use certain parameters ('Use since_id to get only new events since a previous poll. Use event_type to filter by type.'), but it doesn't explicitly state when to use this tool versus alternatives like 'read_state' or 'consultation_report'. It implies usage for polling events with filters, yet lacks explicit exclusions or comparisons to sibling tools.

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