ActiveAgentic Ai APIs
The ultimate collection of APIs for building autonomous AI agents — 2,036 production-ready APIs across Agents, AI Models, and MCP Servers. Stop wasting weeks building infrastructure. Plug these in and ship your agent today.
Quick Answer
- 1. Agentic Ai APIs gives AI agents ai & ml capabilities with 351 GitHub stars and a quality score of 77/99.
- 2. Install: Run
npx -y agentic-ai-apisor add the config to your MCP client. - 3. Runtime: npm — no authentication required.
- 4. Health: Actively maintained — last checked 7/29/2026.
Overview
Category: AI & ML
Runtime: npm
Install: npx -y agentic-ai-apis
Auth: No auth required
Source: community
Stars: 351
Setup time: ~30 sec
What You Can Build
With the Agentic Ai APIs MCP server, your AI assistant can:
- Access ai & ml functionality through natural language
- Use the server immediately with no API key setup
- Combine with other MCP servers for cross-tool automation workflows
- Run with npm for reliable, consistent deployments
Health Status
ActiveLast commit: 7/19/2026
Checked: 7/29/2026
Page updated: June 13, 2026
One-Click Install
Copy the snippet for your MCP client and paste it in.
Claude Desktop
Add to claude_desktop_config.json
{
"mcpServers": {
"agentic-ai-apis": {
"command": "npx",
"args": [
"-y",
"agentic-ai-apis"
],
"env": {}
}
}
}Cursor
Settings → MCP Servers → Add
{
"mcpServers": {
"agentic-ai-apis": {
"command": "npx -y agentic-ai-apis"
}
}
}Saves as .cursor/mcp.json in the download. Move it to your project root.
VS Code
Use with MCP extension
{
"mcpServers": {
"agentic-ai-apis": {
"command": "npx -y agentic-ai-apis"
}
}
}SDK & Code Integration Examples
Connect to Agentic Ai APIs programmatically using TypeScript or Python MCP SDKs.
import { Client } from "@modelcontextprotocol/sdk/client/index.js";
import { StdioClientTransport } from "@modelcontextprotocol/sdk/client/stdio.js";
// Initialize Agentic Ai APIs MCP client transport
const transport = new StdioClientTransport({
command: "npx",
args: ["-y","agentic-ai-apis"],
});
const client = new Client(
{ name: "mcp-app-client", version: "1.0.0" },
{ capabilities: { tools: {} } }
);
async function connectAndRun() {
await client.connect(transport);
const tools = await client.listTools();
console.log("Connected to Agentic Ai APIs. Available tools:", tools);
}
connectAndRun().catch(console.error);MCP Server Configuration
Add this to your claude_desktop_config.json
{
"mcpServers": {
"agentic-ai-apis": {
"command": "npx",
"args": ["-y","agentic-ai-apis"],
"env": {}
}
}
}Topics
Error Resolution & Troubleshooting Matrix
Common JSON-RPC protocol error codes and environment troubleshooting for Agentic Ai APIs.
-32600 (Invalid Request)Cause: Malformed JSON-RPC payload sent to server
Resolution: Verify MCP client payload adheres to JSON-RPC 2.0 specs.
-32601 (Method Not Found)Cause: Requested tool or resource method does not exist
Resolution: Call list_tools() to inspect supported tool names.
-32602 (Invalid Params)Cause: Missing or invalid tool arguments
Resolution: Check argument schema types against tool definition.
-32603 (Internal Error)Cause: Unhandled execution exception inside server process
Resolution: Inspect process stderr logs or check API key credentials.
AUTH_KEY_MISSINGCause: Required environment variable not set in MCP config
Resolution: Define required API key under 'env' object in claude_desktop_config.json.
STDIO_PIPE_CLOSEDCause: Server process crashed on startup
Resolution: Run install command manually in terminal to inspect error output.
Quality Score
Automated quality ranking of maintaining source repository.
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