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, and it excels. It details that the tool re-fetches the verdict shape, computes a delta, returns an enumerated thesis_status, includes a narrative, and returns 404 on expiry (90-day TTL) or missing records. This is unusually thorough and leaves very little hidden 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?

Every sentence in the description contributes meaningful information: purpose, input source, behavior, output enum, narrative, error case, TTL, and a concrete example use case. It is front-loaded with the core action and remains focused without any filler or redundant restatement.

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 has only one parameter and no output schema, the description fully covers the needed context: how to obtain the input, what the output looks like (thesis_status and narrative), and failure modes (404 for expiry or missing record). The inclusion of the 90-day TTL and the example callback shape makes this complete for an agent to invoke and interpret results.

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 already documents the only parameter, decision_id, at 100% coverage. The description adds valuable provenance by telling the agent to pass the decision_id returned by decision_track, which goes beyond the schema's generic 'Path parameter (required)' note. This extra context helps the agent correctly source the value.

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 'REVIEW A TRACKED DECISION,' a specific verb+resource combination that clearly distinguishes this tool from sibling tools like tengu_copilot_score_ticker or tengu_copilot_ticker_full. It further clarifies the purpose by explaining it computes a structured DELTA against the original snapshot and returns thesis_status, leaving no ambiguity about what the tool does.

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 gives an explicit prerequisite: 'Pass the decision_id returned by decision_track,' and a concrete use case: the 'your AAPL position you opened Tuesday is up X% — thesis is tracking' callback shape. It does not explicitly list alternatives or when-not-to-use scenarios, but the context is clear enough for an agent to choose this tool appropriately.

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