MeatSpace
Server Details
Human-in-the-loop for AI agents. Submit choices, get a human decision.
- Status
- Healthy
- Uptime
- 100.0% over the last 24 hours
- Last Tested
- Transport
- Streamable HTTP · MCP 2024-11-05
- URL
- Repository
- zmarten/meatspace
- GitHub Stars
- 0
- Server Listing
- MeatSpace
- ChatGPT
- –
- Claude
- –
TDQS
Scored across 3 tools
Each tool covers a distinct concern: ask_human performs the actual human interaction, get_service_status determines availability, and provision_api_key handles authentication setup. There is no overlap in their roles.
All tool names follow a consistent verb_noun snake_case pattern: ask_human, get_service_status, provision_api_key. The convention is uniform and predictable.
Three tools is appropriate for the narrow, well-defined purpose of enabling human-in-the-loop decisions. Each tool is necessary and the scope feels complete rather than thin.
The core workflow is covered: provision a key, check service status, and ask a human. Minor gaps like revoking keys or listing pending asks exist but do not block the primary use case.
Available Tools
3 toolsask_humanAInspect
Present content to a human and ask them to choose between options. Use this for subjective judgment, approval, preference, or tie-breaks. Avoid using it for deterministic checks or reversible low-stakes choices. The tool waits briefly for a result, then returns pending if the human has not responded yet.
Inputs: title (required), choices (required), agent_name (required), run_id, +10 more.
get_service_statusAInspect
Check whether MeatSpace is available and when to use a human. Call this when deciding whether to escalate a subjective or high-consequence choice.
Inputs: no named parameters.
provision_api_keyAInspect
Create a MeatSpace API key instantly. No authentication required. Returns a Bearer token for use with the ask_human tool. Max 5 active keys per email address.
Inputs: name (required), email (required).
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Related MCP Connectors
Give any AI agent a way to ask a person — for approval, a decision, an answer or a review.
Lets AI agents use a real human as a tool: visual checks, taste, phone calls, unblocking, approvals
Human-as-a-Service for AI agents. Delegate tasks that need a real human, get results via API.
Let your AI agents ask you for decisions and get your answers back, from your phone or the web.
Related MCP Servers
- AlicenseNot gradedqualityDmaintenanceProvides a human-in-the-loop dashboard for coordinating multiple AI agents, enabling real-time approval, feedback, and intervention on critical decisions.Mozilla Public 2.0
- AlicenseNot gradedqualityDmaintenanceEnables AI agents to request human approvals with customizable forms, webhooks, and team features.91 npmMIT
- AlicenseAqualityDmaintenanceHuman-in-the-loop approval gate for AI agents. Your agent calls submit_approval before any irreversible action; a human reviews on a branded page; a signed webhook fires back with the decision.11MIT
- AlicenseNot gradedqualityAmaintenanceLets AI agents submit work to real human reviewers, receive pinned feedback and a verdict, and iterate until the result is approved.38 npmMIT
Glama MCP Gateway
Add one secure layer between your agents and this server.