Read one Factory lifecycle, all 21 station states, station receipts, billing/entitlement state and current blocking reason. Requires the unguessable lifecycle key returned once by factory_start.
| Name | Required | Description | Default |
|---|---|---|---|
| lifecycle_id | Yes | ||
| lifecycle_key | Yes |
Changes observed during successful MCP inspections.
Input schema / $schemaPrevious value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the description's 'Read' operation aligns. The description adds value by detailing the scope (all 21 stations, receipts, billing) and the key requirement, which goes beyond annotation signals. No contradictions.
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?
Two sentences with no redundancy. The first sentence enumerates the read scope; the second specifies the key requirement. Every word adds value.
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 complexity (many state components) and absence of output schema, the description provides a useful summary of returned data. It omits error scenarios (e.g., invalid key) but overall gives sufficient context for an agent to understand what to expect.
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?
Schema description coverage is 0%, so the description must explain both parameters. It mentions 'lifecycle key' but not lifecycle_id, leaving the agent to infer that lifecycle_id identifies the lifecycle. This is insufficient for an agent to correctly supply the required inputs without additional knowledge.
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 verb 'Read' and the resource 'one Factory lifecycle', listing specific data elements (21 station states, receipts, billing/entitlement, blocking reason). This distinguishes it from sibling tools like factory_start (which creates) or factory_health (which checks health), providing unambiguous purpose.
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 explicitly requires the 'unguessable lifecycle key returned once by factory_start', indicating this tool is used after starting a factory lifecycle. It does not list exclusions or alternatives, but the context makes it clear that other status tools (e.g., factory_health) exist for different purposes.
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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