MCP server
by unifuncs
UniFuncs offers a TypeScript bridge to the google web search api, enabling web search and reading with Express and NPX.
Connects MCP to UniFuncs API for web search, content extraction, and deep research capabilities. Offers both real-time and async processing for complex research tasks.
UniFuncs is an official MCP server published by unifuncs that provides AI assistants with tools and capabilities via the Model Context Protocol. UniFuncs offers a TypeScript bridge to the google web search api, enabling web search and reading with Express and NPX. It is categorized under developer tools.
You can install UniFuncs in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
MIT
UniFuncs is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
We evaluated UniFuncs against two servers with overlapping tools; this profile had the clearer scope statement.
UniFuncs has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
We wired UniFuncs into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
UniFuncs is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
Useful MCP listing: UniFuncs is the kind of server we cite when onboarding engineers to host + tool permissions.
According to our notes, UniFuncs benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
We wired UniFuncs into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
UniFuncs is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
Strong directory entry: UniFuncs surfaces stars and publisher context so we could sanity-check maintenance before adopting.
We evaluated UniFuncs against two servers with overlapping tools; this profile had the clearer scope statement.
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MCP Server for the UniFuncs API - Enhanced with Deep Search and Deep Research capabilities
This MCP server provides access to the following UniFuncs APIs:
web-search)Real-time web search with comprehensive results
web-reader)Extract detailed content from web pages
deep-search-sync)Real-time deep search with immediate results
deep-search-create-task + deep-search-query-task)Asynchronous deep search for complex queries
deep-research-create-task + deep-research-query-task)Comprehensive deep research capabilities
Get a UniFuncs API Key: https://unifuncs.com/account
{
"mcpServers": {
"unifuncs": {
"command": "npx",
"args": [
"-y",
"@unifuncs/ufn-mcp-server"
],
"env": {
"UNIFUNCS_API_KEY": "sk-**********"
}
}
}
}
For SSE transport, set the environment variable:
export UNIFUNCS_SSE_SERVER=true
export UNIFUNCS_SSE_SERVER_PORT=5656 # Optional, default is 5656
Or use the --sse flag:
npx @unifuncs/ufn-mcp-server --sse
Query: Search keywords
Freshness: Day | Week | Month | Year (optional)
Page: Page number, default 1 (optional)
Count: Results per page, 1-50, default 10 (optional)
Format: json | markdown | text, default json (optional)
URL: Page URL to read
Format: markdown (optional)
IncludeImages: boolean (optional)
LinkSummary: boolean (optional)
Model: s3 (default: s3)
Messages: Array of {role: "user"|"assistant"|"system", content: string}
Stream: boolean (default: false)
Model: s3 (default: s3)
Messages: Array of {role: "user"|"assistant"|"system", content: string}
Returns: task_id for querying status
Task_ID: Task ID from create_task
Returns: Task status, progress, and results when completed
Model: u1 | u1-pro (default: u1)
Content: Research question/topic
Introduction: Researcher persona (optional)
Reference_Style: link | number | footnote (default: link)
Generate_Summary: boolean (default: false)
Max_Depth: 1-50 (default: 25, recommended)
Domain_Scope: Comma-separated domains (optional)
Domain_Blacklist: Comma-separated domains to exclude (optional)
Output_Prompt: Custom output template (optional)
Important_URLs: Comma-separated URLs (optional)
Important_Keywords: Comma-separated keywords (optional)
Important_Prompt: Important prompt content (optional)
Push_To_Share: boolean (default: false)
Set_Public: boolean (default: false)
Returns: task_id for querying status
Task_ID: Task ID from create_task
Returns: Task status, progress, and results when completed
{
"query": "OpenClaw AI",
"count": 5,
"format": "json"
}
// Create task
{
"model": "s3",
"messages": [
{ "role": "user", "content": "What are the latest developments in AI?" }
]
}
// Query task (use returned task_id)
{
"task_id": "3aff2a91-7795-4b73-8dab-0593551a27a1"
}
// Create research task
{
"model": "u1",
"content": "Analyze the impact of AI on healthcare",
"max_depth": 25,
"domain_scope": "arxiv.org, nature.com",
"generate_summary": true
}
// Query research task
{
"task_id": "research-task-id-here"
}
For detailed pricing, visit: https://unifuncs.com/pricing
MIT
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.