deploy-mcp

check_deployment_status

Check the latest deployment status for your project on Vercel, Netlify, or Cloudflare Pages. Get details of recent deployments to monitor success or failure.

Instructions

Check the latest deployment status for a project on a platform

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "platform": "<platform>",
      "project": "<project>"
    },
    "name": "check_deployment_status"
  }
}

Input Schema

Name	Required	Description
`limit`	No	Number of recent deployments to return (default: 1, max: 20)
`token`	No	API token for authentication (optional if set in environment)
`project`	Yes	The project name or ID
`platform`	Yes	The deployment platform

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.6.5Aug 7, 2026

TDQS

C2.9/5.0

Behavior2/5

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

With no annotations, the description carries the full burden for behavioral disclosure. 'Check' conveys a read-only operation, but it does not mention authentication requirements, rate limits, pagination, or the response structure beyond 'status'. This is minimal disclosure for a tool with no annotation safety net.

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

Conciseness5/5

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

The description is a single front-loaded sentence with no filler or redundant repetition of the tool name. Every word contributes meaning ('Check', 'latest', 'deployment status', 'project', 'platform').

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

Completeness2/5

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

There is no output schema and no annotations, yet the description does not explain the return format, how multiple deployments are returned, or authentication behavior. Given the tool has four parameters and sits alongside related siblings, one sentence is insufficient for an agent to fully predict invocation results.

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 description coverage is 100%, so the parameters are already well documented (platform enum, project name/ID, optional token, limit with default/max). The description adds only the 'latest' scope, which aligns with the limit default of 1 but does not provide additional semantics beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Check') and resource ('deployment status') with scope ('latest', 'for a project on a platform'), clearly distinguishing it from log or comparison tools. It loses one point because 'latest' singular conflicts with the `limit` parameter allowing up to 20 results, making the scope slightly ambiguous.

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

Usage Guidelines2/5

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

No explicit guidance is given about when to use this tool versus siblings like watch_deployment or get_deployment_logs. The description implies usage through its verb but provides no prerequisites, exclusions, or alternative recommendations.

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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