UniFuncs▌
by unifuncs
UniFuncs offers a TypeScript bridge to the google web search api, enabling web search and reading with Express and NPX.
Provides a bridge to the UniFuncs API for web search and web reading capabilities through TypeScript implementation with Express and NPX commands.
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
best for
- / Researchers needing comprehensive web analysis
- / Developers building search-powered applications
- / Content creators gathering source material
- / AI assistants requiring web data access
capabilities
- / Search the web with filtering and pagination
- / Extract clean content from web pages
- / Perform deep search with streaming results
- / Create async research tasks with custom parameters
- / Configure research depth and domain scope
- / Export results in multiple formats
what it does
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.
about
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.
how to install
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.
license
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.
readme
UniFuncs MCP Server
MCP Server for the UniFuncs API - Enhanced with Deep Search and Deep Research capabilities
Features
This MCP server provides access to the following UniFuncs APIs:
1. Web Search (web-search)
Real-time web search with comprehensive results
- Search across the internet with keywords
- Filter by freshness (Day/Week/Month/Year)
- Pagination support (1-50 results per page)
- Multiple output formats (JSON/Markdown/Text)
2. Web Reader (web-reader)
Extract detailed content from web pages
- Clean content extraction
- Optional image inclusion
- Link summary support
- Markdown output
3. Deep Search - Sync (deep-search-sync)
Real-time deep search with immediate results
- Model: S3
- Streaming support
- Instant response
4. Deep Search - Async (deep-search-create-task + deep-search-query-task)
Asynchronous deep search for complex queries
- Create task and get task_id immediately
- Poll for task status and results
- Background processing for large queries
5. Deep Research (deep-research-create-task + deep-research-query-task)
Comprehensive deep research capabilities
- Models: U1, U1-Pro
- Customizable research parameters:
- Introduction: Set researcher persona
- Reference style: link/number/footnote
- Max depth: Up to 50 iterations (25 recommended)
- Domain scope: Limit search to specific domains
- Domain blacklist: Exclude specific domains
- Custom output prompts
- Important URLs, keywords, and prompts
- Async task management
Setup
API Key
Get a UniFuncs API Key: https://unifuncs.com/account
NPX (STDIO)
{
"mcpServers": {
"unifuncs": {
"command": "npx",
"args": [
"-y",
"@unifuncs/ufn-mcp-server"
],
"env": {
"UNIFUNCS_API_KEY": "sk-**********"
}
}
}
}
SSE Server
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
Tool Reference
web-search
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)
web-reader
URL: Page URL to read
Format: markdown (optional)
IncludeImages: boolean (optional)
LinkSummary: boolean (optional)
deep-search-sync
Model: s3 (default: s3)
Messages: Array of {role: "user"|"assistant"|"system", content: string}
Stream: boolean (default: false)
deep-search-create-task
Model: s3 (default: s3)
Messages: Array of {role: "user"|"assistant"|"system", content: string}
Returns: task_id for querying status
deep-search-query-task
Task_ID: Task ID from create_task
Returns: Task status, progress, and results when completed
deep-research-create-task
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
deep-research-query-task
Task_ID: Task ID from create_task
Returns: Task status, progress, and results when completed
Examples
Web Search
{
"query": "OpenClaw AI",
"count": 5,
"format": "json"
}
Deep Search Async
// 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"
}
Deep Research
// 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"
}
Pricing
- Web Search: Pay per request
- Web Reader: Pay per request
- Deep Search: Pay per token usage
- Deep Research:
- U1: 0.6 PTC/M Tokens
- U1-Pro: 1.2 PTC/M Tokens
For detailed pricing, visit: https://unifuncs.com/pricing
Support
- Documentation: https://unifuncs.com/api
- GitHub: https://github.com/UniFuncs/ufn-mcp-server
- Email: [email protected]
- WeChat: unifuncs
License
MIT
Changelog
v0.1.0 (2026-02-25)
- Added Deep Search sync API support
- Added Deep Search async API (create_task + query_task)
- Added Deep Research async API (create_task + query_task)
- Enhanced documentation with all tool references
- Improved error handling
v0.0.6
- Initial release with web-search and web-reader
FAQ
- What is the UniFuncs MCP server?
- UniFuncs is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
- How do MCP servers relate to agent skills?
- Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
- How are reviews shown for UniFuncs?
- This profile displays 43 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.6 out of 5—verify behavior in your own environment before production use.
Use Cases▌
Extended AI Capabilities
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
Context Enhancement
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Workflow Automation
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
Implementation Guide▌
Prerequisites
- ›Claude Desktop 0.7.0+ or Cursor IDE with MCP support
- ›Basic understanding of MCP architecture and capabilities
- ›Access credentials for integrated services (if required)
- ›Willingness to experiment and iterate on configuration
Time Estimate
15-60 minutes depending on server complexity
Installation Steps
- 1.Install MCP server: npm install -g [package-name] or via GitHub
- 2.Add server configuration to ~/.claude/mcp.json
- 3.Provide required credentials and configuration
- 4.Restart Claude Desktop to load new server
- 5.Test basic functionality with simple prompts
- 6.Explore capabilities and experiment with use cases
- 7.Document successful patterns for reuse
Troubleshooting
- ⚠MCP server not loading: Check config syntax, verify installation
- ⚠Connection errors: Check network, firewall, credentials
- ⚠Feature not working: Read server docs, check required parameters
- ⚠Performance issues: Monitor resource usage, check for network latency
- ⚠Conflicts with other servers: Check port assignments, namespace collisions
Best Practices▌
✓ Do
- +Read server documentation thoroughly before setup
- +Start with simple use cases to validate functionality
- +Test in non-production environment first
- +Monitor resource usage and performance
- +Keep servers updated for bug fixes and new features
- +Document configuration for team members
- +Use environment variables for sensitive configuration
✗ Don't
- −Don't grant overly permissive access to MCP servers
- −Don't skip reading security considerations in docs
- −Don't expose sensitive data without proper controls
- −Don't run untrusted MCP servers without code review
- −Don't ignore error messages—investigate root cause
💡 Pro Tips
- ★Combine multiple MCP servers for powerful workflows
- ★Create custom MCP servers for your specific needs
- ★Share successful configurations with team
- ★Use MCP inspector for debugging
- ★Join MCP community for tips and troubleshooting
Technical Details▌
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
- Model Context Protocol (MCP)
- JSON-RPC 2.0
- stdio or HTTP transport
Compatibility
- Claude Desktop
- Cursor IDE
- Custom MCP clients
When to Use This▌
✓ 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.
Integration▌
- →Tool composition: Chain multiple MCP tools in workflows
- →Context augmentation: Provide AI with relevant external data
- →Action delegation: Let AI execute tasks on external systems
- →Bidirectional sync: Keep AI context and external systems in sync
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
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Ratings
4.6★★★★★43 reviews- ★★★★★Neel Patel· Dec 24, 2024
We evaluated UniFuncs against two servers with overlapping tools; this profile had the clearer scope statement.
- ★★★★★Sofia Khan· Dec 20, 2024
UniFuncs has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Shikha Mishra· Dec 16, 2024
We wired UniFuncs into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Ganesh Mohane· Dec 12, 2024
UniFuncs is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Xiao Okafor· Dec 12, 2024
Useful MCP listing: UniFuncs is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★Chen Farah· Dec 8, 2024
According to our notes, UniFuncs benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Sofia Haddad· Nov 23, 2024
We wired UniFuncs into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Tariq Mensah· Nov 15, 2024
UniFuncs is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Xiao Harris· Nov 11, 2024
Strong directory entry: UniFuncs surfaces stars and publisher context so we could sanity-check maintenance before adopting.
- ★★★★★Sakshi Patil· Nov 3, 2024
We evaluated UniFuncs against two servers with overlapping tools; this profile had the clearer scope statement.
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