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by delimit-ai
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Python
Hybrid

Delimit Deploy Verify

delimit_deploy_verify

Probe a freshly deployed revision's health to confirm the new SHA is healthy before finalizing the deploy. If unhealthy, proceed with rollback.

Instructions

Probe a freshly-deployed revision's health — experimental (Pro).

When to use: immediately after delimit_deploy_publish has rolled out a new revision, to confirm the new SHA is actually healthy before declaring the deploy done and closing out the chain (delimit_deploy_verify -> delimit_evidence_collect -> delimit_ledger_done -> delimit_notify). If this returns unhealthy, the next step is delimit_deploy_rollback. When NOT to use: for steady-state runtime health checks (use delimit_obs_status / delimit_obs_metrics), to read deploy-system metadata only (delimit_deploy_status), or for a smoke test before deploy (delimit_test_smoke).

Sibling contrast: delimit_deploy_status reads deploy-system metadata only; this actively probes the running deployment. delimit_obs_status is the steady-state observability surface; this is post-deploy-only.

Side effects: gated by require_premium — unlicensed callers receive a license payload and no probe runs. On a licensed call, invokes backends.deploy_bridge.verify which performs network health checks against the deployed app (HTTP probes, container inspection, dependency reachability). No write. Marked EXPERIMENTAL — health logic may return partial results on backends without health endpoints; do not treat as authoritative for runtime SLOs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
appNoApplication identity; selects only this app's targets.
envNoTarget environment ("staging" or "production").
git_refNoOptional git ref the deploy targets.
repo_pathNoExplicit Git worktree root containing deploy target configuration.
target_urlsNoOptional app-specific HTTPS targets; never expands to the global fleet.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed3 schema fields changedv4.13.2
    • changedInput schema / properties / app / description
      Previous value: -"Application name."New value: +"Application identity; selects only this app's targets."
    • addedInput schema / properties / repo_path
      Added value: +{
+  "default": "",
+  "description": "Explicit Git worktree root containing deploy target configuration.",
+  "type": "string"
+}
    • addedInput schema / properties / target_urls
      Added value: +{
+  "anyOf": [
+    {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "description": "Optional app-specific HTTPS targets; never expands to the global fleet."
+}
  2. Changed3 schema fields changedv4.7.9
    • addedInput schema / properties / app / description
      Added value: +"Application name."
    • addedInput schema / properties / env / description
      Added value: +"Target environment (\"staging\" or \"production\")."
    • addedInput schema / properties / git_ref / description
      Added value: +"Optional git ref the deploy targets."
  3. Addedv4.5.5

TDQS

A4.6/5.0
Behavior5/5

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

The description goes well beyond the annotations by disclosing license gating via require_premium, the backend invocation, the network checks performed, the absence of writes, and the experimental nature with possible partial results. The readOnlyHint=false is consistent with the side-effecting license gating and backend probe, while 'No write' adds useful precision about state mutation.

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

Conciseness4/5

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

The description is longer than average but well-organized with labeled sections and a front-loaded purpose. Some redundancy exists—'experimental' appears twice and the sibling contrast partially repeats the when-not guidance—but the structure makes it easy for an agent to extract the relevant decision information quickly.

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

Completeness5/5

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

Given the output schema exists and input parameters are fully covered by the schema, the description supplies the missing operational context: workflow position, rollback path, license gating, backend behavior, and experimental caveats. Nothing an agent needs to decide when and how to invoke this tool is absent.

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

Parameters3/5

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

Schema coverage is 100%, so the schema already documents all five parameters. The description adds no parameter-specific meaning beyond naming the verification context, and does not clarify which optional parameters are typically expected for a verification call. Baseline 3 is appropriate because the schema carries the parameter burden.

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 opens with a specific verb and resource: 'Probe a freshly-deployed revision's health.' It clearly distinguishes this tool from delimit_deploy_status, delimit_obs_status, and delimit_test_smoke, and places it within the deploy workflow. An agent can immediately understand what this tool does and how it differs from close siblings.

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

Usage Guidelines5/5

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

The description gives explicit when-to-use guidance ('immediately after delimit_deploy_publish'), names the downstream chain, and specifies the rollback path if health is unhealthy. It also provides a dedicated 'When NOT to use' section with concrete alternatives for steady-state checks, metadata reads, and pre-deploy smoke tests.

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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