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. Dates show when Glama detected each change.
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.
Add one secure layer between your agents and this server.
With 94 tools spanning overlapping concepts (multiple readiness/audit/grade tools, many status checkers, deprecated aliases like trust_* vs observation_*), agents will frequently struggle to pick the right one. While each tool is individually distinct, the sheer volume and conceptual overlap (e.g., audit_mcp, readiness_report, verify_mcp_ready, lookup_readiness, recommend_mcp, subscribe_grade_changes) create high misselection risk.
Most tools use snake_case with underscores, but the pattern is inconsistent: some are verb-first (audit_mcp, verify_mcp_ready, claim_start, check_engagement) while others are noun-first (receipt_issue, meter_open, work_order_open, agent_invoice_status). Deprecated aliases like trust_compare vs observation_compare further break consistency, though the majority remain readable.
94 tools is far beyond any reasonable scope for a single server, even one with broad ambitions like 'observatory + town'. The calibration notes 50+ as extreme mismatch; this server far exceeds that. Many tools are highly specific (e.g., factory_resolve_dead_letter, visit_touch_status, start_here) and could be consolidated or split into separate servers.
The server covers a wide range of domains (auditing, claiming, receipts, meters, escrow, work orders, gold rush, town, analytics) and offers many CRUD-like operations, but several lifecycle gaps exist: no cancel/close for work orders (only open/accept/deliver/accept_delivery), escrow (only open/attest/status), or meters (only open/charge/status). Given the massive scope, important operations are missing, though core workflows are present.