MeatSpace
Server Details
Human-in-the-loop for AI agents. Submit choices, get a human decision.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- zmarten/meatspace
- GitHub Stars
- 0
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 4.2/5 across 3 of 3 tools scored.
Each tool has a clearly distinct purpose: ask_human for human interaction, get_service_status for service availability, and provision_api_key for key creation. No overlapping functionality.
All tools use a consistent verb_noun pattern in snake_case: ask_human, get_service_status, provision_api_key. The naming is clear and predictable.
With 3 tools, the server is minimal but covers the core workflow of human interaction. However, additional tools for managing API keys or retrieving ask results would be beneficial.
The tool set lacks essential features: no way to retrieve a pending ask result, no key management (list/revoke), and no tool to cancel or update an ask. Agents will likely fail to complete workflows involving delayed human responses.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| title | Yes | Short title for the request (max 200 chars) | |
| run_id | No | Optional workflow run identifier | |
| choices | Yes | 2-4 choices for the human to pick from | |
| content | No | Content for the human to review (text, markdown, HTML, or image URL). Max 50KB. | |
| metadata | No | Optional metadata passed through to webhook | |
| trace_id | No | Optional trace identifier | |
| agent_name | Yes | Your agent/tool name (max 100 chars) | |
| confidence | No | Agent confidence between 0 and 1 | |
| callback_url | No | Optional HTTPS webhook URL for async notification | |
| content_type | No | How to render the content. Default: text | |
| decision_reason | No | Why the agent is escalating this to a human (max 500 chars) | |
| timeout_seconds | No | Request expiry in seconds (default 3600, max 86400) | |
| recommended_option | No | Optional choice id the agent currently recommends | |
| consequence_of_wrong_choice | No | Why a wrong choice matters (max 500 chars) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description reveals asynchronous behavior (waits briefly, returns pending) and non-destructive nature. Could mention idempotency, but sufficient.
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?
Four sentences, no fluff, front-loaded with purpose and usage guidelines. Every sentence adds value.
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?
Covers purpose, usage, and pending behavior for a complex tool with 14 params. Could describe output more, but description addresses the key aspect.
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 coverage is 100% with detailed descriptions; description does not add parameter-specific meaning, but baseline is 3.
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 it presents content to a human for choice, and specifies use cases like subjective judgment, approval, and tie-breaks, which distinguishes it from 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?
Explicitly tells when to use (subjective judgment, etc.) and when to avoid (deterministic checks, low-stakes choices), plus explains the wait-and-pending behavior.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Fails to specify return format (e.g., boolean, message) or any behavioral details like latency or authentication needs. For a zero-parameter tool, more transparency about output is expected.
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?
Two sentences, front-loaded with purpose and usage. No wasted words; every sentence earns its place.
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?
Describes purpose and usage adequately for a simple status check, but lacks details on return value or how output informs escalation. With no output schema, the description should provide at least the nature of the result.
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?
Zero parameters, so baseline is 4. Description does not add parameter info, but schema coverage is 100% trivially. No need for additional parameter semantics.
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?
Clearly states the tool checks MeatSpace availability and guides when to use a human. Distinguishes from siblings (ask_human, provision_api_key) by focusing on escalation decisions.
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?
Explicitly says 'call this when deciding whether to escalate a subjective or high-consequence choice', providing clear use context. No exclusions or alternatives mentioned, but sibling names imply subsequent actions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Agent or tool name (max 100 chars) | |
| Yes | Owner email address |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description discloses key behaviors: no authentication needed, returns a Bearer token, intended for 'ask_human', and a limit of 5 keys per email. It lacks details on error handling or idempotency, but covers essential traits.
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 a single concise sentence that front-loads the action and includes all critical information without waste. Every word earns its place.
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?
Given the tool's simplicity (2 required params, no output schema), the description covers purpose, constraints, authentication, return type, and integration with a sibling. It omits error scenarios and idempotency but is adequate for a straightforward creation tool.
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%, and the description does not add new parameter details beyond the schema. It only repeats the purpose, so no extra value beyond baseline.
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 verb 'Create', the resource 'MeatSpace API key', and adds 'instantly' for context. It differentiates from siblings 'ask_human' and 'get_service_status' by specifying the key's purpose for use with 'ask_human'.
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?
It explicitly states 'No authentication required' and 'Max 5 active keys per email address', providing clear usage context. However, it does not explicitly mention when not to use it beyond the constraint, nor compare directly to sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
No comments yet. Be the first to start the discussion!