JSON Placeholder Posts MCP
Fetches and filters posts from JSON Placeholder API by userId, with support for elicitation when userId is omitted.
README
JSON Placeholder Posts MCP
A small UV-managed FastMCP server that calls:
https://json-placeholder.mock.beeceptor.com/posts
It exposes two MCP tools:
get_posts_by_user, which returns matching posts whenuserIdis provided. IfuserIdis missing, it creates a durable pending workflow instead of using native MCP elicitation.continue_workflow, which accepts the missing input and completes a pending workflow from any pod.
The tools emit MCP server notifications with log messages and progress updates while they resolve input, fetch posts, and filter results.
Setup
uv sync
Run
For MCP clients that use stdio:
uv run json-placeholder-posts-mcp
For local HTTP testing:
uv run json-placeholder-posts-mcp --transport streamable-http
By default the HTTP server listens at:
http://127.0.0.1:8000/mcp
You can override the host and port with environment variables:
HOST=127.0.0.1 PORT=9000 uv run json-placeholder-posts-mcp --transport streamable-http
Workflow continuation uses Mongo. By default, the server connects to
mongodb://localhost:27017:
uv run json-placeholder-posts-mcp --transport streamable-http
For deployed environments, override the Mongo connection details:
MONGO_URI=mongodb://mongo:27017 \
MONGO_DATABASE=json_placeholder_posts_mcp \
MONGO_WORKFLOW_COLLECTION=elicitation_workflows \
uv run json-placeholder-posts-mcp --transport streamable-http
Technical Design
See docs/mcp-workflow-design.md for the MCP creation flow, workflow continuation pattern, elicitation decision, and multi-pod workflow ID storage design.
Tool
get_posts_by_user
Fetches all posts from the Beeceptor API and returns only posts matching the
provided userId. When userId is omitted, the server creates a pending
workflow that any pod can continue with the returned workflowId.
Input:
{
"userId": 1
}
Output:
{
"status": "completed",
"userId": 1,
"count": 2,
"posts": [],
"source": "https://json-placeholder.mock.beeceptor.com/posts"
}
Pending workflow input:
{}
Pending workflow output:
{
"status": "input_required",
"workflowId": "abc123",
"message": "Enter the userId to fetch posts for.",
"requiredInput": {
"field": "userId",
"type": "integer"
},
"expiresAt": "2026-06-18T10:15:00+00:00"
}
continue_workflow
Completes a pending workflow and clears its stored details after success or failure.
Input:
{
"workflowId": "abc123",
"userId": 1
}
Development
uv run pytest
uv run ruff check .
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。