Standalone Model Context Protocol server for FinalApproval — gate AI agent actions behind a human approval.
Your agent calls submit_approval; a human reviews on a branded page in your FinalApproval dashboard; the channel's configured webhook fires back when the human decides.
This is the stdio variant intended for local / headless / CI / directory-indexed (Glama) use. If you want the hosted OAuth-gated Streamable-HTTP server instead, point MCP clients at https://www.finalapproval.ai/mcp.
v0.1 exposes one tool:
Tool | Status | Notes |
| ✅ shipped | Creates a pending approval. Webhook fires on resolution. |
| planned | Blocked on public |
| planned | Blocked on public |
| planned | Blocked on public |
For the full tool surface today, use the hosted endpoint at https://www.finalapproval.ai/mcp (OAuth 2.1, scope approvals:write).
Related MCP server: Approval Gate
Sign in at finalapproval.ai and create a channel. Copy its API key (starts with fa_) and configure a webhook URL.
Run the MCP server with the API key in FINALAPPROVAL_API_KEY.
npx -y @finalapproval/mcp-serverConfig snippet (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(Published image coming; you can also build locally: docker build -t finalapproval-mcp ..)
Var | Required | Default | Purpose |
| yes | — | Channel API key (starts with |
| no |
| Override for self-hosted / dev |
submit_approval returns immediately with a pending approval ID. The actual decision arrives via the channel's webhook, not back through MCP. Your host application (the agent's server) must implement a webhook receiver that verifies the HMAC signature and executes the gated action on approved or logs on denied. See the FinalApproval docs for webhook payload schema.
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.
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.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Human-in-the-loop API for AI agents. CAPTCHA, OTP, KYC, and approvals by real humans.
Human-in-the-loop approval for agent actions, with verifiable action-bound receipts.
Human-in-the-loop for AI agents. Submit choices, get a human decision.
Pre-execution governance for AI agents. Deterministic PASS/FAIL/REVIEW verdicts, replayable proof.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/finalapproval/final-approval-mcp'
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