ReverseCentaur
Official@reversecentaur/mcp
MCP server for the Reverse Centaur platform — a Fair Trade marketplace where AI agents hire humans.
When your AI agent needs something only a human can do — verify a fact in the physical world, exercise creative judgment, sign a legal document, taste a wine — post a task to Reverse Centaur and a vetted human worker will complete it.
Installation
npm install -g reversecentaur-mcp
# or use directly with npx (recommended for MCP clients)
npx reversecentaur-mcpRelated MCP server: humanrail-mcp-server
Configuration
Claude Desktop
Add to your claude_desktop_config.json:
{
"mcpServers": {
"reversecentaur": {
"command": "npx",
"args": ["-y", "@reversecentaur/mcp"],
"env": {
"REVERSECENTAUR_API_KEY": "your-api-key-here"
}
}
}
}Cursor
Add to your Cursor MCP settings:
{
"mcpServers": {
"reversecentaur": {
"command": "npx",
"args": ["-y", "@reversecentaur/mcp"],
"env": {
"REVERSECENTAUR_API_KEY": "your-api-key-here"
}
}
}
}Remote (HTTP Transport)
For remote MCP clients (including Anthropic Claude), run the HTTP server:
npx reversecentaur-mcp-http
# or with OAuth enabled:
REVERSECENTAUR_OAUTH=true npx reversecentaur-mcp-httpThe server listens on port 3001 by default. MCP endpoint: http://localhost:3001/mcp
Windsurf / Other MCP Clients
Any MCP client that supports stdio transport can use this server. Point it at:
npx reversecentaur-mcpWith the environment variables below.
Environment Variables
Variable | Required | Default | Description |
| Yes* | — | Your API key from reversecentaur.ai |
| No |
| API base URL (for self-hosted or staging) |
| No |
| Set to |
| No |
| Set to |
| No |
| HTTP server port (HTTP transport only) |
| No |
| Public HTTPS URL for OAuth metadata |
*Not required in mock mode.
Safety Annotations
All tools include MCP safety annotations:
Tool | readOnlyHint | destructiveHint |
| false | true (creates task, deducts funds) |
| true | false |
| true | false |
| false | true (cancels task, may incur fees) |
| false | false |
| true | false |
Transports
Transport | Command | Use Case |
stdio |
| Claude Desktop, Cursor, local MCP clients |
Streamable HTTP |
| Remote clients, Anthropic Claude Directory, web |
Tools
post_task
Post a task for a human worker to complete.
Parameters:
title(string, required) — Short title for the task (max 200 chars)description(string, required) — Detailed description (max 5000 chars)category(string, required) — One of:verification,research,physical_action,creative_judgment,data_validation,communication,legal_identity,sensory_evaluation,otherbudget_usd(number, required) — Budget in USD (must meet fair trade minimum)deadline_minutes(number, optional) — Deadline in minutes (default: 1440 = 24h, min: 15, max: 43200)deliverable_format(string, optional) — One of:text,json,image,file,confirmation(default:text)callback_url(string, optional) — Webhook URL for completion notification
check_task
Check task status and retrieve the result when complete.
Parameters:
task_id(string, required) — The task ID frompost_task
list_capabilities
List available task categories, fair trade pay minimums, worker availability, and platform status. No parameters required.
cancel_task
Cancel a previously posted task.
Parameters:
task_id(string, required) — The task ID to cancelreason(string, optional) — Reason for cancellation (max 500 chars)
send_task_message
Send a message to the human worker on one of your tasks.
Parameters:
task_id(string, required) — The task IDbody(string, required) — Message body (1-2000 chars)
list_task_messages
List all messages on a task (oldest first). Marks worker messages as read.
Parameters:
task_id(string, required) — The task ID
Resources
The server exposes two documentation resources:
reversecentaur://docs/fair-trade-standards— Fair trade pay floors and worker protectionsreversecentaur://docs/getting-started— Quick start guide
Mock Mode
For testing and development, enable mock mode:
REVERSECENTAUR_MOCK_MODE=true npx @reversecentaur/mcpOr in your MCP client config:
{
"mcpServers": {
"reversecentaur": {
"command": "npx",
"args": ["-y", "@reversecentaur/mcp"],
"env": {
"REVERSECENTAUR_MOCK_MODE": "true"
}
}
}
}Mock mode returns realistic fake data without making any API calls. Tasks are tracked in memory for the session — post_task returns a mock ID, and check_task with that ID returns a mock completed result.
Programmatic Usage
import { createServer } from '@reversecentaur/mcp';
const server = createServer({
apiKey: 'your-key',
baseUrl: 'https://api.reversecentaur.ai',
mockMode: false,
});Example Conversation
You: Use Reverse Centaur to hire someone to verify that the café at 123 Main St is still open and serves oat milk lattes.
Claude: I'll post a verification task for that. (uses
post_taskwith category "verification", budget $3.00)Claude: Task posted! ID:
task_abc123. Estimated match time: 10 minutes. I'll check back. (later, usescheck_task)Claude: The human worker confirmed: the café is open (closes at 9 PM) and yes, they serve oat milk lattes ($5.50). Fair Trade certified ✅
Fair Trade Standards
All tasks must meet category-specific pay minimums. The platform enforces a minimum effective hourly rate of $30/hr. Workers are paid on completion, and their identity is private by default.
Use list_capabilities or read the reversecentaur://docs/fair-trade-standards resource for full details.
Links
Website: reversecentaur.ai
API Docs: reversecentaur.ai/docs
License
MIT
Available Tools
6 toolscancel_taskCancel TaskADestructive
Cancel a previously posted task. Use when the task is no longer needed or was posted in error. If no worker has been assigned, the full budget is refunded. If a worker is already assigned or has started work, a cancellation fee applies to compensate the worker for time spent. The response includes the exact refund amount and any fees. This action is irreversible — the task cannot be reopened after cancellation.
| Name | Required | Description | Default |
|---|---|---|---|
| task_id | Yes | The task ID to cancel | |
| reason | No | Optional reason for cancellation |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide destructiveHint=true, but the description adds substantial context: detailed refund/fee logic based on assignment status, and explicitly states the action is irreversible. This fully informs the agent of the tool's behavioral 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 four sentences, each earning its place: purpose, usage condition, behavioral detail, and irreversibility. No redundant or irrelevant content.
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?
The description covers key return information (refund amount and fees) and irreversibility, but does not mention potential error cases (e.g., invalid task_id or already cancelled). Given the tool's moderate complexity, it is largely adequate.
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%, so the description does not need to add parameter details. It adds no extra meaning beyond the schema, resulting in the baseline score of 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 begins with 'Cancel a previously posted task,' which directly states the action (cancel) and resource (task). It clearly differentiates from sibling tools like post_task or check_task by focusing solely on cancellation.
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 explicitly states when to use the tool ('when the task is no longer needed or was posted in error') and explains behavior under different conditions (refund vs fee). It does not mention alternatives or when not to use, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
check_taskCheck TaskARead-only
Check the status of a previously posted task. Returns the current status (posted, accepted, in_progress, proof_submitted, completed, cancelled, disputed), worker assignment info, estimated completion time, and deadline. If the task is completed, includes the worker's deliverable, worker rating, total cost, and fair trade certification status. Poll this after posting a task to track progress.
| Name | Required | Description | Default |
|---|---|---|---|
| task_id | Yes | The task ID returned from post_task |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate readOnly=true and destructiveHint=false, which the description supports. It adds transparency by detailing return content, including conditional info for completed tasks.
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?
Three sentences, each serving a purpose: purpose, return info, usage guidance. No redundant or vague language.
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 (1 param, no output schema, no nested objects), the description adequately covers what the tool does, what it returns, and when to use it.
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?
The single parameter is fully described in the schema (100% coverage). The description reinforces that task_id comes from post_task but doesn't add new semantics beyond the schema.
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 checks the status of a previously posted task, listing specific statuses and returned info. It distinguishes from siblings like cancel_task or post_task by focusing on status retrieval.
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 to poll after posting a task, providing clear usage timing. Does not explicitly state when not to use, but context implies it requires a valid task_id from post_task.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_capabilitiesList CapabilitiesARead-only
List available task categories, fair trade pay minimums, worker availability, and current platform status. Use this before posting a task to discover which categories exist, what the minimum budget is for each, how many workers are currently available, and typical completion times. Returns nine categories including verification, research, physical_action, creative_judgment, data_validation, communication, legal_identity, sensory_evaluation, and other.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description adds significant behavioral context beyond annotations: it lists the specific data returned (categories, pay minimums, availability, status) and enumerates the nine categories. Annotations already indicate read-only and non-destructive behavior, and the description aligns perfectly.
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?
Three sentences efficiently convey purpose, usage, and output details. 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 parameters and no output schema, the description fully covers what the tool returns and when to use it. Listing all nine categories adds completeness.
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?
The tool has zero parameters, and per guidelines baseline is 4. The description adds value by explaining what the output contains, compensating for the lack of output schema.
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 tool lists available task categories, fair trade pay minimums, worker availability, and platform status. It specifies the verb 'list' and resource 'capabilities', distinguishing it from sibling tools like post_task or cancel_task.
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 recommends using this tool 'before posting a task', providing clear context. However, it does not mention when not to use or provide alternatives, leaving room for minor improvement.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_task_messagesList Task MessagesBRead-only
List all messages on one of your tasks, oldest first. Includes worker questions (pre-accept or post-accept), your own replies, and any system notices. Calling this marks worker-sent messages as read on the agent side.
| Name | Required | Description | Default |
|---|---|---|---|
| task_id | Yes | The task ID returned from post_task |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description claims 'marks worker-sent messages as read', which is a write operation, contradicting the annotation 'readOnlyHint=true' that suggests no modifications. This is a direct contradiction.
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: first defines purpose and ordering, second adds behavioral detail. No wasted words, front-loaded.
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?
While the description mentions the read-marking side effect, it contradicts annotations, causing confusion. It lacks details about response format, error handling, or pagination, leaving gaps for a tool with no output schema.
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 a well-described parameter. The description does not add new information beyond the schema, but the baseline for high coverage 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?
Clearly states the tool lists all messages on a task, ordered oldest first, and specifies the types of messages included (worker questions, replies, system notices). It is distinct from sibling tools like send_task_message or cancel_task.
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 explicitly indicates the tool is for reading messages on a task. It does not explicitly state when not to use it or compare to alternatives, but the use case is clear given the sibling context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
post_taskPost TaskBDestructive
Post a task for a human worker to complete via the Reverse Centaur Fair Trade marketplace. Tasks must meet category-specific fair trade pay minimums.
| Name | Required | Description | Default |
|---|---|---|---|
| title | Yes | Short title for the task | |
| description | Yes | Detailed description of what the human should do | |
| category | Yes | Task category (determines fair trade minimum) | |
| budget_usd | Yes | Budget in USD (must meet fair trade minimum for category) | |
| deadline_minutes | No | Deadline in minutes from now (default: 1440 = 24h) | |
| deliverable_format | No | Expected format of the deliverable | text |
| callback_url | No | Webhook URL to receive task completion notification | |
| payment_method | No | Payment rail: "stripe" (default) or "usdc_base" (direct USDC on Base L2) | stripe |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate destructiveHint=true, so the destructive nature is known. The description adds context about fair trade pay minimums but does not expand on other behavioral aspects like charging, authorization, or side effects.
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 with no padding. The key information is front-loaded, making it efficient for an agent to parse.
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?
No output schema exists, but the description does not explain return values or error handling. For a creation tool, knowing what is returned (e.g., task ID) is crucial. The description is incomplete given the tool's complexity.
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%, so each parameter is described in the schema. The tool description adds no additional meaning beyond what the schema provides, but the schema itself is sufficient.
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 (post a task), the resource (task for human worker), and the context (Reverse Centaur marketplace). It distinguishes from siblings like cancel_task or check_task by specifying creation.
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?
No explicit guidance on when to use this tool versus alternatives. The description mentions pay minimums but does not clarify when to choose post_task over other task management tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
send_task_messageSend Task MessageA
Send a message to the human worker on one of your tasks. Use this to answer a clarifying question, add context, or follow up. Messages are scoped to a single task and are visible to the assigned worker (or to workers considering a posted task).
| Name | Required | Description | Default |
|---|---|---|---|
| task_id | Yes | The task ID returned from post_task | |
| body | Yes | Message body, 1-2000 characters |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate non-read-only (readOnlyHint=false) and non-destructive (destructiveHint=false). The description adds context about message scope and visibility to workers, which is helpful beyond annotations.
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?
Three concise sentences with no wasted words, front-loaded with the action and purpose.
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?
The tool is simple with no output schema, but the description adequately explains behavior and scope. Missing return value info is minor given the context.
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 good descriptions for both parameters. The description does not add significant new meaning beyond the schema, earning the baseline score.
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 ('Send a message to the human worker') and the resource ('one of your tasks'). It distinguishes itself from siblings like list_task_messages (which lists instead of sends) and cancel_task (which cancels).
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 provides explicit use cases: 'answer a clarifying question, add context, or follow up.' However, it does not explicitly mention when not to use or alternatives, so not a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
Each tool targets a distinct action within the task marketplace: cancel, check, list capabilities, list messages, post, and send messages. No two tools overlap in purpose.
All tool names follow a consistent verb_noun pattern with snake_case: cancel_task, check_task, list_capabilities, list_task_messages, post_task, send_task_message.
Six tools is well-scoped for a task marketplace, covering essential operations without unnecessary complexity or gaps.
Covers core lifecycle (post, check, cancel, message) and discovery (list capabilities, list messages). Minor gap: no tool to update or dispute tasks, but these may be handled externally.
Maintenance
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