MCP Server with OpenAI Integration
Provides connectivity to OpenAI's API for making LLM calls, with support for custom base URLs (Azure/OpenAI proxies), configurable timeouts, and token usage metrics.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP Server with OpenAI Integration search for recent developments in quantum computing"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP Server with OpenAI Integration
A production-ready Model Context Protocol server implemented in TypeScript. The server provides:
OpenAI connectivity demo – prove the API key works end-to-end via
npm run demo:openai.MCP tool demo – spawn the server and call tools through an MCP client using
npm run demo:tool.Extensibility demo – hot-load third-party tools from disk via
npm run demo:extorMCP_TOOL_MODULES.Browser UI demo – launch an interactive web page that exercises the OpenAI call and knowledge-search tool with
npm run demo:ui.
The codebase focuses on clean abstractions, schema validation, and commercial readiness (logging, config safety, tests).
Requirements
Node.js 18+ (Node 20 recommended to avoid optional engine warnings).
npm 9+.
A valid
OPENAI_API_KEYwith access to the desired models.
Related MCP server: PMCP - Perfect Model Context Protocol Server
Quick start
npm install
cp .env.example .env # fill in OPENAI_API_KEY
npm run build
npm start # runs the compiled MCP server on stdioTo run the TypeScript entry directly during development:
npm run devEnvironment variables
Variable | Description |
| Required. API key for OpenAI. |
| Override base URL for Azure/OpenAI proxies. |
| Timeout (ms) applied to OpenAI API calls. Defaults to |
| Name advertised to MCP clients. |
|
|
| Comma-separated absolute paths to extra tool modules (see extensibility demo). |
| Reserved for future transports; defaults to |
| Optional port for the browser UI demo. Defaults to |
Demo workflows
1. OpenAI connectivity
Verifies credentials and model access:
npm run demo:openaiOutputs the model reply plus token usage metrics via Pino logs.
2. MCP tool invocation
Spawns the compiled MCP server (node dist/index.js) and connects with the official MCP client SDK:
npm run build
npm run demo:toolSet MCP_DEMO_SERVER_COMMAND / MCP_DEMO_SERVER_ARGS if you want the client to launch a different command (for example npx tsx src/index.ts). The script lists tools and invokes knowledge_search end-to-end.
3. Extensibility via plugins
Ships with src/examples/plugins/stockQuoteTool.ts. After npm run build the compiled module lives at dist/examples/plugins/stockQuoteTool.js.
Load it either through the demo script:
npm run build
npm run demo:extor by setting an environment variable before starting the server:
export MCP_TOOL_MODULES=$(pwd)/dist/examples/plugins/stockQuoteTool.js
npm startThe server automatically registers every tool exported from the referenced module(s).
4. Browser UI walkthrough
Launch a lightweight HTTP server that serves public/ui-demo.html:
npm run demo:uiVisit http://localhost:4399 (or UI_DEMO_PORT) to:
Send prompts directly to OpenAI using the configured API key.
Call the built-in
knowledge_searchtool through a REST façade.
Responses render inline so you can validate both flows without leaving the browser.
Tooling
TypeScript strict mode with
tscfor builds.Vitest for unit testing (
npm test).ESLint + Prettier for linting/formatting (
npm run lint,npm run format).Pino structured logging with pretty printing in development.
Test & quality gates
npm run lint
npm testCoverage reports are emitted under coverage/ via V8 instrumentation.
Project structure
src/config/env.ts– centralized, validated environment loading.src/clients/openaiClient.ts– resilient OpenAI wrapper implementing theLLMProvidercontract.src/mcp/registry.ts– tool lifecycle management + dynamic module loading.src/mcp/server.ts– MCP server wiring, tool adapters, and plugin APIs.src/demos/*– runnable scripts covering the three required scenarios.src/examples/plugins/*– sample plugin(s) for extensibility demos.tests/*– Vitest coverage for critical units.
For a deeper architectural overview, read docs/architecture.md.
Available Tools
2 toolsknowledge_searchC
Searches curated knowledge base snippets stored as markdown files.
Inputs: query (required).
text_summarizerC
Summarizes long text into concise bullet points using the configured LLM.
Inputs: text (required), model.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 2 tools
The two tools have clearly distinct purposes: knowledge_search retrieves information from a knowledge base, while text_summarizer condenses provided text. There is no overlap in functionality, and an agent would easily differentiate between them.
Both tools use snake_case naming, which is consistent. However, knowledge_search follows a noun_verb pattern (knowledge_search), while text_summarizer uses a noun_verb pattern with a suffix (text_summarizer), showing a minor deviation in naming style.
With only 2 tools, the server feels thin for an 'OpenAI Integration' purpose, which typically implies broader capabilities like text generation, translation, or analysis. This limited set may not adequately cover the expected scope.
Given the server's name suggests OpenAI integration, there are significant gaps: no tools for text generation, translation, sentiment analysis, or other common LLM tasks. The surface is incomplete, likely causing agent failures for broader use cases.
Related MCP Connectors
MCP server for OpenAI API (chat completions, image generation, embeddings) via AceDataCloud
MCP server for AI dialogue using various LLM models via AceDataCloud
MCP server unifying ERPs, CRMs, APIs and knowledge base for Claude, ChatGPT and Gemini.
MCP server for your apps' tools and custom tools, plus hosted AI agents and approval-gated workflows
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