Swarms API Documentation - Build AI Agents & Multi-Agent Systems

Overview

The Swarms TypeScript MCP Server bridges MCP-compatible agents (Claude Desktop, Cursor IDE, OpenAI Agents, and other MCP clients) with the Swarms AI agent platform. Built with Stainless and following the Model Context Protocol standard, this server exposes Swarms’ API endpoints as discoverable tools that agents can invoke directly—enabling agents to execute single agents, orchestrate multi-agent swarms, run reasoning workflows, and process batch operations without custom API integration code.

Key Capabilities

Single Agent Execution: Run individual Swarms agents with custom tasks, model selection, and configuration parameters
Multi-Agent Orchestration: Coordinate swarms using architectural patterns including sequential workflows, concurrent execution, hierarchical structures, graph-based routing, and majority voting
Reasoning Agents: Execute chain-of-thought reasoning agents for step-by-step problem analysis and complex decision-making
Batch Processing: Process multiple agent tasks or swarm operations in parallel using thread pools for high-throughput workloads
Dynamic Discovery: Use runtime endpoint discovery to explore available API operations, or configure static tool exposure for better performance and type safety
Client Optimization: Automatically adapts tool schemas and capabilities to match your MCP client (Claude Desktop, Cursor IDE, OpenAI Agents, Claude Code, and custom clients)

Why Use This Server?

The Swarms MCP Server eliminates the need to write custom API integration code when building agent-powered applications. Instead of manually constructing HTTP requests, handling authentication, and parsing responses, agents can directly invoke Swarms operations through standardized MCP tools. The server provides production-ready error handling, rate limit monitoring, health checks, and flexible configuration options—allowing you to expose only the tools your agents need while maintaining full compatibility with the MCP ecosystem.

Installation

Prerequisites

Node.js (version 14 or higher recommended)
A valid Swarms API key
An MCP-compatible client application

Method 1: Direct Invocation via npx

For quick testing or one-time usage, you can run the MCP server directly without installation:

export SWARMS_API_KEY="your_api_key_here"
npx -y swarms-ts-mcp@latest

Method 2: Installation via npm

For persistent usage or integration into projects:

npm install swarms-ts-mcp

Method 3: MCP Client Configuration

Most MCP clients support configuration through a JSON file. Add the following configuration to your client’s MCP server settings:

{
  "mcpServers": {
    "swarms_ts_api": {
      "command": "npx",
      "args": ["-y", "swarms-ts-mcp", "--client=claude", "--tools=all"],
      "env": {
        "SWARMS_API_KEY": "your_api_key_here"
      }
    }
  }
}

Replace your_api_key_here with your actual Swarms API key.

Configuration

Environment Variables

The server requires the following environment variable:

SWARMS_API_KEY: Your Swarms platform API key (required)

Command-Line Arguments

The MCP server supports extensive command-line configuration:

Tool Selection

--tools=all: Expose all available tools
--tools=dynamic: Enable dynamic tool discovery mode
--tool=<name>: Include a specific tool by name
--no-tool=<name>: Exclude a specific tool by name

Resource Filtering

--resource=<name>: Include all tools under a specific resource
--resource=<pattern>*: Include resources matching a wildcard pattern
--no-resource=<name>: Exclude a specific resource

Operation Filtering

--operation=read: Include only read operations (GET/LIST)
--operation=write: Include only write operations (POST/PUT/DELETE)

Client Configuration

--client=<type>: Configure for a specific MCP client
- Valid values: openai-agents, claude, claude-code, cursor

Capability Settings

--capability=<name>: Enable specific client capabilities
- top-level-unions: Support for top-level union types
- valid-json: JSON string parsing for arguments
- refs: Support for $ref pointers in schemas
- unions: Support for union types (anyOf)
- formats: Support for format validations
- tool-name-length=N: Maximum tool name length

Utility Commands

--list: Display all available tools
--help: Show help information

Configuration Examples

Filter for read-only operations on a specific resource:

npx swarms-ts-mcp --resource=agent --operation=read

Exclude specific tools while including others:

npx swarms-ts-mcp --resource=swarms --no-tool=run_swarms_batch

Configure for Cursor with custom capabilities:

npx swarms-ts-mcp --client=cursor --capability=tool-name-length=40

Complex multi-criteria filtering:

npx swarms-ts-mcp --resource=agent,swarms --operation=read --no-tool=get_logs_swarms

Tool Exposure Strategies

The Swarms MCP server offers two primary strategies for exposing API endpoints as tools:

Static Tool Exposure

In this mode, each API endpoint is exposed as a dedicated tool with a fully-defined schema. This approach provides:

Better autocomplete and type safety in client applications
More accurate parameter validation
Clearer documentation for each endpoint
Optimal performance for smaller API surfaces

Recommendation: Use static exposure when working with a focused subset of the API or when the client has sufficient context window capacity.

Dynamic Tool Discovery

When configured with --tools=dynamic, the server exposes three meta-tools that enable runtime endpoint discovery:

`list_api_endpoints`

Discovers available endpoints with optional search filtering. Parameters:

search (optional): Query string to filter endpoints

Use case: Finding relevant endpoints without loading all schemas into context.

`get_api_endpoint_schema`

Retrieves detailed schema information for a specific endpoint. Parameters:

endpoint: The name or identifier of the endpoint

Use case: Obtaining parameter requirements before invoking an endpoint.

`invoke_api_endpoint`

Executes any endpoint with appropriate parameters. Parameters:

endpoint: The endpoint to invoke
parameters: JSON object containing endpoint-specific parameters

Use case: Making API calls after discovering and understanding the schema. Recommendation: Use dynamic tools when:

Working with a large API surface
Context window limitations are a concern
Runtime endpoint discovery is required
The full API schema exceeds available context

Hybrid Approach

You can combine both strategies by specifying explicit tools alongside dynamic tools:

npx swarms-ts-mcp --tool=run_agent --tools=dynamic

This provides immediate access to frequently-used tools while maintaining the flexibility of dynamic discovery.

Client Compatibility

Different MCP clients have varying capabilities for handling tool schemas and complex types. The Swarms MCP server automatically adapts its output based on the specified client.

Supported Clients

Claude (`--client=claude`)

Optimized for Claude AI assistant with full schema support.

Claude Code (`--client=claude-code`)

Configured for the Claude Code command-line environment.

Cursor (`--client=cursor`)

Adapted for the Cursor IDE with appropriate schema simplifications.

OpenAI Agents (`--client=openai-agents`)

Compatible with OpenAI’s agent framework.

Manual Capability Configuration

For clients not listed above or to override defaults:

npx swarms-ts-mcp --capability=top-level-unions,refs,unions,formats,tool-name-length=50

Available capabilities:

top-level-unions: Enables union types at the schema root
valid-json: Enables JSON string parsing for complex arguments
refs: Enables JSON Schema $ref pointers
unions: Enables anyOf union types
formats: Enables format validators (date-time, email, etc.)
tool-name-length=N: Sets maximum tool name length

Available Tools and Resources

The Swarms MCP server exposes 12 tools by default when using --tools=all. These tools are organized by resource and operation type, providing comprehensive access to the Swarms API functionality.

Complete Tools Reference Table

Tool Name	Resource	Operation Type	Description	Primary Use Case
`get_root_client`	$client	Read	Root client information	API connectivity verification and health checks
`check_health`	health	Read	Health status of the Swarms API	Monitoring API uptime and status
`run_agent`	agent	Write	Execute a single agent with a specified task	Running individual AI agents for specific tasks
`run_agent_batch`	agent.batch	Write	Execute multiple agents concurrently using thread pool	Parallel processing of multiple independent agent tasks
`list_available_models`	models	Read	Retrieve all available AI models	Model discovery and selection for agent configuration
`check_available_swarms`	swarms	Read	Query available swarm types and configurations	Determining which swarm architecture to use
`get_logs_swarms`	swarms	Read	Retrieve API request logs for associated API keys	Auditing, debugging, and usage analysis
`run_swarms`	swarms	Write	Execute a swarm with a specified task	Orchestrating multiple agents in coordinated patterns
`run_swarms_batch`	swarms.batch	Write	Execute multiple swarms concurrently using thread pool	Large-scale parallel swarm processing
`create_completion_reasoning_agents`	reasoning_agents	Write	Execute a reasoning agent with chain-of-thought	Complex problem-solving requiring step-by-step analysis
`list_types_reasoning_agents`	reasoning_agents	Read	List available reasoning agent types	Selecting appropriate reasoning strategies
`get_limits_client_rate`	client.rate	Read	Query rate limits and current usage statistics	Monitoring API quota and preventing rate limit violations

Dynamic Discovery Tools

When using --tools=dynamic, these additional meta-tools become available:

Tool Name	Description	Parameters
`list_api_endpoints`	Discover available endpoints with optional filtering	`search` (optional): Query string to filter endpoints
`get_api_endpoint_schema`	Retrieve detailed schema for a specific endpoint	`endpoint`: Endpoint name or identifier
`invoke_api_endpoint`	Execute any endpoint with appropriate parameters	`endpoint`: Endpoint to invoke `parameters`: JSON object with endpoint-specific parameters

Resource: $client

get_root_client (read)

Description: Root client information
Use case: Health checks and API connectivity verification

Resource: health

check_health (read)

Description: Health status of the Swarms API
Returns: API health metrics and status
Use case: Monitoring and uptime verification

Resource: agent

run_agent (write)

Description: Execute a single agent with a specified task
Parameters:
- task: The task description for the agent
- Additional agent configuration parameters
Returns: Agent execution results
Use case: Running individual AI agents for specific tasks

Resource: agent.batch

run_agent_batch (write)

Description: Execute multiple agents concurrently using a thread pool
Parameters:
- tasks: Array of task descriptions
- Batch configuration options
Returns: Aggregated results from all agents
Use case: Parallel processing of multiple independent tasks

Resource: models

list_available_models (read)

Description: Retrieve all available AI models
Returns: List of model identifiers and capabilities
Use case: Model discovery and selection

Resource: swarms

check_available_swarms (read)

Description: Query available swarm types and configurations
Returns: List of swarm architectures and their capabilities
Use case: Determining which swarm pattern to use

get_logs_swarms (read)

Description: Retrieve API request logs for all associated API keys
Returns: Log entries (excluding client IP information)
Use case: Auditing, debugging, and usage analysis

run_swarms (write)

Description: Execute a swarm with a specified task
Parameters:
- task: The task for the swarm to execute
- swarm_type: The type of swarm architecture
- Additional swarm configuration
Returns: Swarm execution results
Use case: Orchestrating multiple agents in coordinated patterns

Resource: swarms.batch

run_swarms_batch (write)

Description: Execute multiple swarms concurrently using a thread pool
Parameters:
- tasks: Array of swarm tasks
- Batch and swarm configuration
Returns: Aggregated swarm results
Use case: Large-scale parallel swarm processing

Resource: reasoning_agents

create_completion_reasoning_agents (write)

Description: Execute a reasoning agent with chain-of-thought capabilities
Parameters:
- task: The reasoning task
- Reasoning configuration options
Returns: Reasoning output with intermediate steps
Use case: Complex problem-solving requiring step-by-step analysis

list_types_reasoning_agents (read)

Description: List available reasoning agent types
Returns: Reasoning agent architectures and their characteristics
Use case: Selecting appropriate reasoning strategies

Resource: client.rate

get_limits_client_rate (read)

Description: Query rate limits and current usage
Returns: Rate limit information and usage statistics
Use case: Monitoring API quota and preventing rate limit violations

Tools by Operation Type

The 12 default tools are divided into read and write operations:

Read Operations (7 tools)

Read operations are safe, idempotent operations that retrieve information without modifying state:

get_root_client
check_health
list_available_models
check_available_swarms
get_logs_swarms
list_types_reasoning_agents
get_limits_client_rate

Plus dynamic tools (when enabled):

list_api_endpoints
get_api_endpoint_schema

Write Operations (5 tools)

Write operations execute actions and may modify state or consume resources:

run_agent
run_agent_batch
run_swarms
run_swarms_batch
create_completion_reasoning_agents

Plus dynamic tool (when enabled):

invoke_api_endpoint

Total: 12 static tools (7 read + 5 write)

Advanced Usage

Programmatic Integration

For custom integrations, you can import and configure the server programmatically:

import { server, endpoints, init } from "swarms-ts-mcp/server";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";

// Initialize the server with all endpoints
init({ server, endpoints });

// Start the server
const transport = new StdioServerTransport();
await server.connect(transport);

Importing Specific Tools

import getRootClient from "swarms-ts-mcp/tools/top-level/get-root-client";
import { init } from "swarms-ts-mcp/server";

// Initialize with only specific tools
init({ 
  server: myServer, 
  endpoints: [getRootClient] 
});

Creating Custom Endpoints

import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { zodToJsonSchema } from "zod-to-json-schema";
import { z } from "zod";

const myServer = new McpServer({
  name: "custom-swarms-server",
  version: "1.0.0"
});

const myCustomEndpoint = {
  tool: {
    name: 'my_custom_tool',
    description: 'Custom tool for specialized operations',
    inputSchema: zodToJsonSchema(z.object({ 
      query: z.string(),
      options: z.object({
        verbose: z.boolean().optional()
      }).optional()
    })),
  },
  handler: async (client, args) => {
    // Custom implementation
    return { 
      result: "Custom operation completed",
      data: args 
    };
  }
};

init({ 
  server: myServer, 
  endpoints: [myCustomEndpoint] 
});

Error Handling

When integrating with the Swarms MCP server, implement appropriate error handling:

Common Error Scenarios

Authentication Errors: Invalid or missing API key
Rate Limiting: Exceeded API quota
Invalid Parameters: Malformed requests or missing required fields
Network Issues: Connectivity problems or timeouts
Resource Unavailability: Requested models or swarm types not available

Best Practices for Error Management

Always validate the presence of the SWARMS_API_KEY environment variable
Implement retry logic with exponential backoff for transient failures
Monitor rate limits using get_limits_client_rate before heavy operations
Log errors with sufficient context for debugging
Provide meaningful error messages to end users

Best Practices

Best Practice Category	Recommendation	Description/Details
Batch Operations	Use batch endpoints	When processing multiple tasks, prefer batch endpoints to reduce overhead.
Tool Exposure	Filter unnecessary tools	Minimize context window usage by exposing only the tools required for your workflow.
Rate Limit Monitoring	Monitor rate limits	Regularly check your usage with `get_limits_client_rate` to avoid hitting rate limits.
Model List Optimization	Cache model lists	Store the output of `list_available_models` to prevent repeated, redundant API calls.
API Key Security	Secure API key storage	Never commit API keys to version control; use secure secrets management.
Environment Management	Use environment variables	Manage sensitive information via secure environment variable configuration.
Access Control	Limit tool exposure by permission	Restrict tool access based on user permissions when possible.
Audit Logging	Utilize audit logging	Use `get_logs_swarms` for security auditing and tracking usage.
Tool Strategy (Exploration)	Start with dynamic tools	Use `--tools=dynamic` during early exploration and development to discover available endpoints.
Tool Strategy (Production)	Transition to static tool exposure	Once requirements are clear, switch to static tool exposure for performance and predictability.
Tool Filter Testing	Test tool/resource/operation filters	Verify your configurations (`--tool`, `--resource`, `--operation`) to ensure only expected endpoints are exposed.
Client Compatibility	Test with target client	Always verify integration and tool compatibility with your specific client setup.
Version Management	Pin production versions	Specify the exact package version (e.g., `swarms-ts-mcp@0.1.0-alpha.10`) in production deployments.
Health Monitoring	Implement health checks	Regularly call `check_health` to ensure server and API availability.
API Reliability	Handle API unavailability gracefully	Implement graceful degradation strategies for scenarios when the API is down.
Internal Documentation	Maintain documentation of exposed tools	Keep internal documentation up-to-date on which tools/endpoints are available and why they’re exposed.

Client Setup Examples

Claude Code

Claude Code is a command-line tool that enables Claude to interact with your codebase and execute operations. Here’s how to configure the Swarms MCP server for Claude Code.

Configuration

Add the Swarms MCP server to your Claude Code configuration file (typically located at ~/.config/claude-code/mcp.json or similar):

{
  "mcpServers": {
    "swarms_api": {
      "command": "npx",
      "args": ["-y", "swarms-ts-mcp@latest", "--client=claude-code", "--tools=all"],
      "env": {
        "SWARMS_API_KEY": "your_api_key_here"
      }
    }
  }
}

Recommended Configuration for Claude Code

For optimal performance with Claude Code, consider using a focused set of tools:

{
  "mcpServers": {
    "swarms_api": {
      "command": "npx",
      "args": [
        "-y",
        "swarms-ts-mcp@latest",
        "--client=claude-code",
        "--resource=agent,swarms,reasoning_agents",
        "--capability=tool-name-length=50"
      ],
      "env": {
        "SWARMS_API_KEY": "your_api_key_here"
      }
    }
  }
}

Usage Example: Running an Agent

Once configured, you can ask Claude Code to execute Swarms agents directly:

# In Claude Code, you can now use natural language:
# "Run a Swarms agent to analyze this codebase and generate a summary"

Claude Code will automatically invoke the run_agent tool:

{
  "tool": "run_agent",
  "parameters": {
    "task": "Analyze the codebase structure and generate a comprehensive summary of the architecture, key components, and dependencies.",
    "model": "gpt-4",
    "max_tokens": 2000
  }
}

Usage Example: Batch Processing

For processing multiple files or tasks:

# "Process all Python files in this directory using a batch of agents"

Claude Code will use the batch endpoint:

{
  "tool": "run_agent_batch",
  "parameters": {
    "tasks": [
      "Analyze file1.py and extract function signatures",
      "Analyze file2.py and identify class definitions",
      "Analyze file3.py and document module exports"
    ],
    "model": "gpt-4"
  }
}

Usage Example: Multi-Agent Swarm

For complex tasks requiring multiple agents:

# "Use a sequential swarm to: 1) analyze the code, 2) generate tests, 3) create documentation"

{
  "tool": "run_swarms",
  "parameters": {
    "task": "Analyze the codebase, generate comprehensive tests, and create API documentation",
    "swarm_type": "sequential_workflow",
    "model": "gpt-4"
  }
}

Usage Example: Reasoning Agent

For complex problem-solving tasks:

# "Use a reasoning agent to debug this error and provide a step-by-step solution"

{
  "tool": "create_completion_reasoning_agents",
  "parameters": {
    "task": "Debug the following error: [error message]. Provide a step-by-step analysis and solution.",
    "reasoning_type": "chain_of_thought"
  }
}

Dynamic Tool Discovery

If you prefer runtime discovery, configure with dynamic tools:

{
  "mcpServers": {
    "swarms_api": {
      "command": "npx",
      "args": [
        "-y",
        "swarms-ts-mcp@latest",
        "--client=claude-code",
        "--tools=dynamic"
      ],
      "env": {
        "SWARMS_API_KEY": "your_api_key_here"
      }
    }
  }
}

Then use the discovery tools:

{
  "tool": "list_api_endpoints",
  "parameters": {
    "search": "agent"
  }
}

Claude Chat (Claude Desktop)

Claude Chat (Claude Desktop) provides a conversational interface where Claude can use MCP tools to interact with the Swarms API.

Configuration

Add the Swarms MCP server to your Claude Desktop configuration file. On macOS, this is typically located at ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "swarms_api": {
      "command": "npx",
      "args": ["-y", "swarms-ts-mcp@latest", "--client=claude", "--tools=all"],
      "env": {
        "SWARMS_API_KEY": "your_api_key_here"
      }
    }
  }
}

Recommended Configuration for Claude Chat

For Claude Chat, you may want to expose all tools for maximum flexibility:

{
  "mcpServers": {
    "swarms_api": {
      "command": "npx",
      "args": [
        "-y",
        "swarms-ts-mcp@latest",
        "--client=claude",
        "--tools=all"
      ],
      "env": {
        "SWARMS_API_KEY": "your_api_key_here"
      }
    }
  }
}

Usage Example: Health Check

Start by verifying the connection:

You: "Check if the Swarms API is healthy"

Claude will automatically use the health check tool:

{
  "tool": "check_health",
  "parameters": {}
}

Usage Example: Agent Execution

Execute agents through natural conversation:

You: "Run a Swarms agent to summarize the key points from this document"

Claude will invoke the agent:

{
  "tool": "run_agent",
  "parameters": {
    "task": "Summarize the key points from the provided document",
    "model": "gpt-4",
    "max_tokens": 1000
  }
}

Usage Example: Model Discovery

Discover available models before running agents:

You: "What models are available in Swarms?"

{
  "tool": "list_available_models",
  "parameters": {}
}

Usage Example: Swarm Orchestration

Use multi-agent swarms for complex workflows:

You: "Use a concurrent swarm to analyze these three documents in parallel and provide a comparative analysis"

{
  "tool": "run_swarms",
  "parameters": {
    "task": "Analyze three documents in parallel and provide a comparative analysis",
    "swarm_type": "concurrent_workflow",
    "model": "gpt-4"
  }
}

Usage Example: Rate Limit Monitoring

Check your API usage:

You: "What's my current rate limit status?"

{
  "tool": "get_limits_client_rate",
  "parameters": {}
}

Filtered Configuration Example

If you want to limit Claude Chat to read-only operations:

{
  "mcpServers": {
    "swarms_api": {
      "command": "npx",
      "args": [
        "-y",
        "swarms-ts-mcp@latest",
        "--client=claude",
        "--operation=read"
      ],
      "env": {
        "SWARMS_API_KEY": "your_api_key_here"
      }
    }
  }
}

This configuration exposes only read operations like check_health, list_available_models, check_available_swarms, and get_limits_client_rate, preventing accidental execution of agents or swarms.

Resources

Resource	Link
GitHub Repository	swarms-ts GitHub
npm Package	swarms-ts-mcp on npm
Model Context Protocol	modelcontextprotocol.io
License	Apache-2.0

Version Information

Current Version: 0.1.0-alpha.10
Status: Alpha release
Last Updated: 6 months ago

Support

For issues, feature requests, or contributions:

Open an issue on the GitHub repository
Review existing documentation and examples
Check the Model Context Protocol specification for general MCP questions

Python Client Docs MCP and LLMs txt

​Overview

​Key Capabilities

​Why Use This Server?

​Installation

​Prerequisites

​Method 1: Direct Invocation via npx

​Method 2: Installation via npm

​Method 3: MCP Client Configuration

​Configuration

​Environment Variables

​Command-Line Arguments

​Tool Selection

​Resource Filtering

​Operation Filtering

​Client Configuration

​Capability Settings

​Utility Commands

​Configuration Examples

​Tool Exposure Strategies

​Static Tool Exposure

​Dynamic Tool Discovery

​list_api_endpoints

​get_api_endpoint_schema

​invoke_api_endpoint

​Hybrid Approach

​Client Compatibility

​Supported Clients

​Claude (--client=claude)

​Claude Code (--client=claude-code)

​Cursor (--client=cursor)

​OpenAI Agents (--client=openai-agents)

​Manual Capability Configuration

​Available Tools and Resources

​Complete Tools Reference Table

​Dynamic Discovery Tools

​Resource: $client

​Resource: health

​Resource: agent

​Resource: agent.batch

​Resource: models

​Resource: swarms

​Resource: swarms.batch

​Resource: reasoning_agents

​Resource: client.rate

​Tools by Operation Type

​Read Operations (7 tools)

​Write Operations (5 tools)

​Advanced Usage

​Programmatic Integration

​Importing Specific Tools

​Creating Custom Endpoints

​Error Handling

​Common Error Scenarios

​Best Practices for Error Management

​Best Practices

​Client Setup Examples

​Claude Code

​Configuration

​Recommended Configuration for Claude Code

​Usage Example: Running an Agent

​Usage Example: Batch Processing

​Usage Example: Multi-Agent Swarm

​Usage Example: Reasoning Agent

​Dynamic Tool Discovery

​Claude Chat (Claude Desktop)

​Configuration

​Recommended Configuration for Claude Chat

​Usage Example: Health Check

​Usage Example: Agent Execution

​Usage Example: Model Discovery

​Usage Example: Swarm Orchestration

​Usage Example: Rate Limit Monitoring

​Filtered Configuration Example

​Resources

​Version Information

​Support

Overview

Key Capabilities

Why Use This Server?

Installation

Prerequisites

Method 1: Direct Invocation via npx

Method 2: Installation via npm

Method 3: MCP Client Configuration

Configuration

Environment Variables

Command-Line Arguments

Tool Selection

Resource Filtering

Operation Filtering

Client Configuration

Capability Settings

Utility Commands

Configuration Examples

Tool Exposure Strategies

Static Tool Exposure

Dynamic Tool Discovery

`list_api_endpoints`

`get_api_endpoint_schema`

`invoke_api_endpoint`

Hybrid Approach

Client Compatibility

Supported Clients

Claude (`--client=claude`)

Claude Code (`--client=claude-code`)

Cursor (`--client=cursor`)

OpenAI Agents (`--client=openai-agents`)

Manual Capability Configuration

Available Tools and Resources

Complete Tools Reference Table

Dynamic Discovery Tools

Resource: $client

Resource: health

Resource: agent

Resource: agent.batch

Resource: models

Resource: swarms

Resource: swarms.batch

Resource: reasoning_agents

Resource: client.rate

Tools by Operation Type

Read Operations (7 tools)

Write Operations (5 tools)

Advanced Usage

Programmatic Integration

Importing Specific Tools

Creating Custom Endpoints

Error Handling

Common Error Scenarios

Best Practices for Error Management

Best Practices

Client Setup Examples

Claude Code

Configuration

Recommended Configuration for Claude Code

Usage Example: Running an Agent

Usage Example: Batch Processing

Usage Example: Multi-Agent Swarm

Usage Example: Reasoning Agent

Dynamic Tool Discovery

Claude Chat (Claude Desktop)

Configuration

Recommended Configuration for Claude Chat

Usage Example: Health Check

Usage Example: Agent Execution

Usage Example: Model Discovery

Usage Example: Swarm Orchestration

Usage Example: Rate Limit Monitoring

Filtered Configuration Example

Resources

Version Information

Support