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fleet_status

Read-only

Compare the canonical fleet roster file against the fleet Postgres database to surface drift diagnostics. Identify every registered agent and its declared state, noting where the database diverges from the file.

Instructions

Read the canonical fleet roster (fleet.json) — every registered agent and its declared state — annotated with drift diagnostics wherever the fleet Postgres disagrees with the file. Read-only; requires Postgres. Use fleet_health for liveness signals rather than roster membership.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
app_idYes
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description need not repeat that. It adds valuable context beyond annotations: the drift-diagnostics behavior (reconciling against Postgres), the Postgres requirement, and the roster-scope limitation. This meaningfully extends what the annotations alone convey. It doesn't contradict annotations in any way.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two dense, information-rich sentences with zero filler. Every clause adds value: the scope ('canonical fleet roster'), the content ('every registered agent and its declared state'), the enhancement ('drift diagnostics'), the prerequisite ('requires Postgres'), and the sibling routing. This is effectively front-loaded and concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only diagnostic tool with one parameter and no output schema, the description covers the essential context: what it reads, what it adds, the dependency, and how to route to the correct sibling. Minor gaps: no description of the return format or the shape of 'drift diagnostics,' but these are not critical for this tool's function. The sibling routing compensates for most missing context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description does not explain what 'app_id' means, how it filters the roster (e.g., per-app scope?), or any format constraints. With a single required parameter and zero schema documentation, the description bears full responsibility but gives no guidance on this parameter. This is a clear gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Read'), a precise resource ('canonical fleet roster (fleet.json)'), and its scope ('every registered agent and its declared state'). It explicitly names what it is not (fleet_health) and what it adds ('drift diagnostics'), which clearly distinguishes it from siblings in the same family.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly names the alternative 'fleet_health' and the condition that selects it ('for liveness signals rather than roster membership'). However, it doesn't explicitly state when NOT to use this tool beyond the fleet_health contrast, nor does it provide prerequisites beyond 'requires Postgres.' This is clear but could be stronger with explicit exclusions.

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

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