用于 FinalApproval 的独立 Model Context Protocol 服务器 —— 为 AI 智能体的操作设置人工审批网关。
您的智能体调用 submit_approval；人工在您的 FinalApproval 控制台的品牌化页面进行审核；当人工做出决定时，频道配置的 Webhook 会触发回调。
这是 stdio 版本，适用于本地 / 无头 / CI / 目录索引（Glama）使用。如果您需要托管的、支持 OAuth 的 Streamable-HTTP 服务器，请将 MCP 客户端指向 https://www.finalapproval.io/mcp。
v0.1 版本提供一个工具：
工具 | 状态 | 备注 |
| ✅ 已发布 | 创建待审批请求。解析时触发 Webhook。 |
| 计划中 | 依赖于公开的 |
| 计划中 | 依赖于公开的 |
| 计划中 | 依赖于公开的 |
如需使用目前完整的工具集，请使用托管端点 https://www.finalapproval.ai/mcp (OAuth 2.1, 作用域 approvals:write)。
Related MCP server: Approval Gate
登录 finalapproval.ai 并创建一个频道。复制其 API 密钥（以 fa_ 开头）并配置 Webhook URL。
使用 FINALAPPROVAL_API_KEY 环境变量运行 MCP 服务器。
npx -y @finalapproval/mcp-server配置片段 (Claude Code ~/.claude.json):
{
"mcpServers": {
"finalapproval": {
"command": "npx",
"args": ["-y", "@finalapproval/mcp-server"],
"env": {
"FINALAPPROVAL_API_KEY": "fa_..."
}
}
}
}docker run --rm -i \
-e FINALAPPROVAL_API_KEY=fa_xxx \
ghcr.io/finalapproval/final-approval-mcp:latest(已发布镜像即将推出；您也可以在本地构建：docker build -t finalapproval-mcp .。)
变量 | 必需 | 默认值 | 用途 |
| 是 | — | 频道 API 密钥 (以 |
| 否 |
| 用于自托管 / 开发环境的覆盖设置 |
submit_approval 会立即返回一个待处理的审批 ID。实际的决策结果通过频道的 Webhook 返回，而不是通过 MCP 返回。您的宿主应用程序（智能体的服务器）必须实现一个 Webhook 接收器，用于验证 HMAC 签名，并在 approved 时执行受控操作，或在 denied 时记录日志。请参阅 FinalApproval 文档 获取 Webhook 有效载荷架构。
npm install
npm run build
FINALAPPROVAL_API_KEY=fa_... npm startMIT
Create a pending approval in the FinalApproval channel whose API key is configured in this MCP server. The channel's webhook fires when the human approves or denies. Use this before any irreversible agent action (sending email, merging a PR, publishing a post, running a migration, etc).
| Name | Required | Description | Default |
|---|---|---|---|
| title | Yes | Short headline shown in the approval list. | |
| body | Yes | HTML body shown to the human reviewer. Tailwind utility classes are supported. Include the actual content being gated (diff, PR body, email text) — the reviewer decides from this card alone. | |
| data | No | Machine-readable payload attached to the approval. Echoed back to the channel's webhook on resolution — put the structured args your callback will need to execute the gated action. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: the approval is pending until human action, webhook firing on resolution, and the irreversible nature of gated actions. However, it lacks details on error handling, rate limits, or authentication requirements beyond the API key mention.
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?
The description is efficiently structured in two sentences: the first defines the tool's purpose, and the second provides critical usage guidelines. Every phrase adds value, with no redundant or unnecessary information.
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?
For a tool with no annotations and no output schema, the description provides strong contextual completeness by explaining the approval workflow, webhook behavior, and use-case guidance. It adequately compensates for the lack of structured fields, though it could benefit from mentioning response format or error cases.
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 100%, providing full parameter documentation. The description adds minimal parameter semantics beyond the schema—it mentions the channel's API key context but doesn't elaborate on parameter usage or constraints. Baseline 3 is appropriate since the schema does the heavy lifting.
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 action ('Create a pending approval') and the target resource ('in the FinalApproval channel'), with specific context about the channel's API key configuration. It distinguishes this as a submission tool for approval requests, which is unambiguous even without sibling tools.
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 provides explicit guidance on when to use this tool ('before any irreversible agent action') and lists concrete examples of such actions (sending email, merging a PR, etc.). This gives clear context for application without needing sibling tool comparisons.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.1.0submit_approvalScored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool has a clear and distinct purpose for submitting approvals.
The tool name 'submit_approval' follows a clear verb_noun pattern. Since there is only one tool, consistency is inherently perfect with no deviations or mixed conventions.
A single tool is too few for most practical server purposes, as it severely limits functionality and scope. While it might serve a narrow use case, it feels thin and incomplete for typical agent workflows.
The server's domain appears to be approval workflows, but with only a submit tool, there are significant gaps. Missing operations likely include checking approval status, listing pending approvals, or handling approvals programmatically, which could cause agent failures.
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