polymcp

lue code between models and tools. Instead of manually wiring everything together for each new setup, PolyMCP offers a structured way to: • Register tools as MCP servers • Attach them to one or more agents • Explicitly manage execution flow and state • Inspect and debug interactions It’s MIT licensed and aimed at developers building production-grade automation, internal copilots, or multi-tool assistants. Repository: https://github.com/poly-mcp/PolyMCP

pute totals, then summarize.") print(answer) Or TypeScript, combining HTTP and stdio-based MCP tools: import { UnifiedPolyAgent, OpenAIProvider } from 'polymcp-ts'; const agent = new UnifiedPolyAgent({ llmProvider: new OpenAIProvider({ apiKey: process.env.OPENAI_API_KEY!, model: 'gpt-4o-mini', }), mcpServers: ['http://localhost:3000/mcp'], stdioServers: [{ command: 'npx', args: ['@playwright/mcp@latest'] }], verbose: true, }); await agent.start(); const answer = await agent.run('Collect data and summarize.'); console.log(answer); Use cases: • Aggregate data from multiple internal services and scripts • Build AI copilots that span different tools and languages • Automate multi-step operational workflows • Prototype agents that interact with production systems safely Works with OpenAI, Anthropic, and Ollama models, including local deployments. GitHub links: • Core & Agent: https://github.com/poly-mcp/PolyMCP • Inspector: https://github.com/poly-mcp/PolyMCP-Inspector • SDK Apps: https://github.com/poly-mcp/PolyMCP-MCP-SDK-Apps I’d love feedback from anyone exploring agent orchestration or building multi-tool AI pipelines.

y: multiple tools in one WASM bundle • Ideal for interactive demos, edge AI apps, or browser-based automation Repo: https://github.com/poly-mcp/Polymcp

the painful part. Most solutions require rewriting logic around a specific SDK or heavily annotating functions. PolyMCP focuses on: • Minimal intrusion into existing code • Clean separation between business logic and AI tooling • Easy debugging via a visual inspector Example use cases • Expose internal APIs or legacy scripts to LLM agents • Automate operational workflows • Build internal copilots over real systems • Prototype AI agents that interact with production services Works with OpenAI, Anthropic, and Ollama (including local models). It’s still evolving and I’m actively iterating. I’d really appreciate feedback — especially from people building agents or experimenting with MCP in production environments. GitHub: • Core: https://github.com/poly-mcp/PolyMCP • Inspector: https://github.com/poly-mcp/PolyMCP-Inspector • SDK Apps: https://github.com/poly-mcp/PolyMCP-MCP-SDK-Apps Happy to answer technical questions.

corators, no schema files, no agent-specific SDKs. ⸻ 2) Real APIs, not toy examples Existing API code works as-is: import requests from polymcp import expose_tools_http def get_weather(city: str): """Return current weather data for a city""" response = requests.get( f"https://api.weatherapi.com/v1/current.json?q={city}" ) return response.json() app = expose_tools_http([get_weather], title="Weather Tools") Agents can now call: get_weather("London") and receive real-time data. ⸻ 3) Business & internal workflows Example: internal reporting logic reused directly by agents. import pandas as pd from polymcp import expose_tools_http def calculate_commissions(sales_data: list[dict]): """Calculate sales commissions from sales data""" df = pd.DataFrame(sales_data) df["commission"] = df["sales_amount"] * 0.05 return df.to_dict(orient="records") app = expose_tools_http([calculate_commissions], title="Business Tools") No rewriting legacy logic. 4) PolyMCP Inspector (visual debugging) To make MCP development usable in practice, I added PolyMCP Inspector: • Visual UI to browse tools, prompts, and resources • Call MCP tools interactively • Inspect schemas, inputs, outputs, and errors • Multi-server support (HTTP + stdio) • Built-in chat playground (OpenAI / Anthropic / Ollama) Think “Postman + DevTools” for MCP servers. Repo: https://github.com/poly-mcp/PolyMCP-Inspector ⸻ 5) MCP SDK Apps (tools + UI) The latest addition is PolyMCP MCP SDK Apps: • Build MCP apps, not just tools • Expose: • tools • UI resources (HTML/JS dashboards) • app-level workflows • Let agents interact with both tools and UIs This is useful for: • internal copilots • ops dashboards • support tools • enterprise AI frontends Repo: https://github.com/poly-mcp/PolyMCP-MCP-SDK-Apps ⸻ Why this matters (especially for companies) • Reuse existing code immediately (scripts, APIs, internal libs) • Standard MCP interface instead of vendor-specific agent SDKs • Multiple tools, one server • Agent-driven orchestration, not hardcoded flows • Faster AI adoption without refactoring everything PolyMCP treats AI agents as clients of your software, not magic wrappers around it. ⸻ Repos • Core framework: https://github.com/poly-mcp/PolyMCP • Inspector UI: https://github.com/poly-mcp/PolyMCP-Inspector • MCP SDK Apps: https://github.com/poly-mcp/PolyMCP-MCP-SDK-Apps Happy to hear feedback from people building MCP servers, agents, or internal AI tools.