Poll a job by id. For paid checkout jobs this is also the settlement trigger: once the buyer has paid, polling captures the charge strictly against the anchored proof and returns the deliverable.
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It reveals that for paid checkout jobs, polling acts as a settlement trigger that captures the charge and returns the deliverable. This is a significant behavioral trait beyond basic polling.
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 wasted words. The first sentence clearly states the core action, and the second provides critical context without unnecessary elaboration.
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 no output schema and one parameter, the description covers the basic purpose and a key behavioral aspect. However, it does not explain return value format, error conditions, or behavior for non-paid-checkout jobs, leaving some gaps for a poll tool.
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 schema coverage is 0% and the description only says 'by id' for the single parameter job_id. It does not explain the format, constraints, or usage of job_id beyond being an identifier. The description adds minimal value over the schema for parameter understanding.
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 'poll' and resource 'job by id'. It also provides a specific use case (settlement trigger for paid checkout jobs), distinguishing it from sibling tools like fund_opportunity and request_capability.
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 context for when the tool is used (e.g., for paid checkout jobs it triggers settlement), but it does not explicitly state when not to use it or suggest alternatives. The guidance is implied rather than explicit.
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.
Each tool targets a distinct operation—discovery, funding, job polling, receipt minting, capability purchase, confidence resolution, graph reading, and verification. No two tools overlap in purpose, and the detailed descriptions make selection unambiguous.
Most tools follow a verb_noun pattern (discover_*, fund_opportunity, get_job, get_sample_receipt, request_capability, resolve_confidence), but subject_evidence_graph is a noun phrase and verify is a bare verb, creating minor inconsistency.
With 9 tools, the server is well-scoped, covering discovery, funding, execution, and verification without redundancy or bloat.
The tool surface covers the full evidence lifecycle: discover opportunities, fund them, poll jobs, mint receipts, request capabilities, resolve confidence gaps, read evidence graphs, and verify receipts. No obvious operational gaps.
