stl_rt_health

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

The annotations already declare readOnly and idempotent behavior. The description adds that it operates on 'locally stored realtime feeds,' giving further context on data locality. No contradictions with annotations.

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 concise, two sentences, with the main purpose first and parameter details second. No fluff or redundancy.

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 description provides sufficient context for what the tool returns (staleness, counts, agreement) and the optional parameter. Since an output schema exists separately, it does not need to detail return structure.

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 single parameter 'entity' is clearly explained with allowed values ('trip_updates', 'vehicle_positions', 'alerts') and the behavior when omitted. This fully covers the schema's lack of description.

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 specifies the tool's purpose: providing staleness, entity counts, and header timestamp agreement for realtime feeds. It distinguishes itself from other realtime tools by focusing on health metrics rather than decoding or wire 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 implies its use as a health check for realtime feeds, but it does not explicitly contrast with sibling tools like stl_rt_schema_census or stl_rt_reference. However, the purpose is straightforward enough that an agent can infer when to use it.

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