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raposa-mcp

raposa-mcp

Official
by agentlabbusiness
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

request_human_approval

Pause a high-stakes action and wait for an authorized human to approve or reject it. Returns a clear approved flag; proceed only on explicit approval.

Instructions

Ask an authorised human to approve an action and wait for the answer. Returns {approved: bool, status, decided_by, ...}. Proceed ONLY if approved is true — a timeout, an expiry or a rejection means do not proceed. risk: low | medium | high.

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": {
      "action": "<action>",
      "context": "<context>",
      "risk": "<risk>"
    },
    "name": "request_human_approval"
  }
}

Input Schema

NameRequiredDescriptionDefault
riskYes
actionYes
contextYes
wait_secNo
requested_byNomcp-agent
expires_in_secNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

A4/5.0
Behavior5/5

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 does this well by explaining that the tool waits, returns a structured result, and that only approved=true means proceed, while timeout, expiry, or rejection means do not proceed. It also enumerates acceptable risk values, giving the agent actionable safety semantics.

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 compact and front-loaded, with every sentence providing necessary information: the action, the return shape, the proceed condition, and the risk vocabulary. There is no filler or repetition.

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

Completeness3/5

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

The description gives strong core semantics and the output schema likely covers return values, but it leaves gaps around parameter meanings and does not explain when to prefer this over the sibling create_approval/get_approval tools. For a 6-parameter tool with no input schema descriptions and no annotations, more guidance is needed.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate for missing parameter documentation. It only clarifies the risk parameter by listing low | medium | high. The important parameters action, context, wait_sec, requested_by, and expires_in_sec are left to be inferred from their names, which is insufficient for reliable invocation.

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?

Description clearly states the action: 'Ask an authorised human to approve an action and wait for the answer.' It identifies both the verb and the resource, and the waiting/returning behavior distinguishes it from siblings like create_approval and get_approval without needing to read their schemas.

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

Usage Guidelines3/5

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

The description implies when to use the tool: when human approval is needed and you can wait for the decision. However, it does not explicitly contrast with the sibling tools create_approval or get_approval, so the agent is left to infer which one to pick for asynchronous creation or later retrieval.

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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