verify_connectionCheck whether the read-only role for a connection exists yet and mark it VERIFIED if so.
Expect this to fail while the human's CloudFormation stack is still creating —
that's normal, not a misconfiguration. Retry every ~15 seconds for up to ~5
minutes before reporting a problem.
| Name | Required | Description | Default |
|---|---|---|---|
| connection_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses meaningful behavioral traits beyond annotations: transient failure during stack creation is normal, and a specific retry schedule is given. It also clarifies the side effect of marking VERIFIED, consistent with readOnlyHint=false and idempotentHint=true. No contradiction.
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 succinct paragraphs: the first states the core action, the second adds essential retry guidance. Every sentence earns its place, and the main purpose is front-loaded.
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's simplicity (one param, no output schema), the description is complete enough: it explains the check, the VERIFIED update, and the retry behavior. It lacks explicit guidance on when to use this versus get_connection, but the core usage is well covered.
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 single parameter connection_id is self-explanatory by name, but the description does not add details about its provenance (e.g., where to obtain it) or format. With 0% schema coverage, the description could have compensated more, but the parameter is simple.
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?
Clearly states a specific action: 'Check whether the read-only role for a connection exists yet and mark it VERIFIED if so.' This distinguishes it from siblings like get_connection (retrieval) and create_cloud_connection (creation). The verb+resource is explicit.
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?
Provides concrete usage context: expect failure during CloudFormation stack creation, retry every ~15 seconds for up to ~5 minutes. This tells when to use the tool and how to handle transient errors, though it doesn't explicitly name alternatives or exclusions.
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 resource and action: connection lifecycle (create, verify, get, list), job lifecycle (run, get status, get result), and findings (list). Even get_job_result and list_cost_findings are clearly differentiated as raw vs. analyzed data, and whoami/list_tools_available serve metadata purposes.
Most tools follow a consistent verb_noun pattern (create_, get_, list_, run_, verify_). The only outlier is 'whoami', which breaks the pattern but is a recognizable convention for account identification. Overall naming is predictable and readable.
With 10 tools, the set is well-scoped for a cloud cost scanning platform. Each tool serves a clear purpose in the connection-scan-result workflow, with no redundancy or bloat.
The core scan workflow is covered (connect, verify, scan, get job, get findings), but there are notable gaps: no tool to delete/disconnect a cloud account, and no way to list past jobs or retrieve results without a prior job_id. These missing lifecycle/history operations could force agents to rely on external state or fail when context is lost.