productivity

Timeserver

secretiveshell

by secretiveshell

Access current date and time across timezones with Timeserver—ideal for hour tracking software and project management ta

Provides access to current date and time information across timezones through a custom datetime:// URI scheme and local system time tool.

github stars

42

0 commentsdiscussion

Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.

Custom datetime:// URI schemeZero configuration required

best for

  • / AI agents needing timezone-aware scheduling
  • / Chat applications requiring time context
  • / Cross-timezone coordination workflows

capabilities

  • / Get current time for any timezone
  • / Access datetime via custom datetime:// URLs
  • / Retrieve local system time
  • / Query time across multiple timezones

what it does

Provides current date and time information for any timezone through a custom URI scheme and system time tool.

about

Timeserver is a community-built MCP server published by secretiveshell that provides AI assistants with tools and capabilities via the Model Context Protocol. Access current date and time across timezones with Timeserver—ideal for hour tracking software and project management ta It is categorized under productivity.

how to install

You can install Timeserver 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

Timeserver is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

README content is unavailable from source data for this server.

Open GitHub repository

FAQ

What is the Timeserver MCP server?
Timeserver 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 Timeserver?
This profile displays 25 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.4 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. 1.Install MCP server: npm install -g [package-name] or via GitHub
  2. 2.Add server configuration to ~/.claude/mcp.json
  3. 3.Provide required credentials and configuration
  4. 4.Restart Claude Desktop to load new server
  5. 5.Test basic functionality with simple prompts
  6. 6.Explore capabilities and experiment with use cases
  7. 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

4.425 reviews
  • Luis Reddy· Dec 28, 2024

    Useful MCP listing: Timeserver is the kind of server we cite when onboarding engineers to host + tool permissions.

  • Shikha Mishra· Dec 24, 2024

    We evaluated Timeserver against two servers with overlapping tools; this profile had the clearer scope statement.

  • Evelyn Tandon· Dec 24, 2024

    According to our notes, Timeserver benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

  • Ama Rahman· Nov 19, 2024

    Strong directory entry: Timeserver surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Tariq Perez· Nov 15, 2024

    We wired Timeserver into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

  • Isabella Park· Oct 10, 2024

    I recommend Timeserver for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

  • Michael Shah· Oct 6, 2024

    Timeserver is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • Rahul Santra· Sep 21, 2024

    Timeserver has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Naina Okafor· Sep 1, 2024

    We evaluated Timeserver against two servers with overlapping tools; this profile had the clearer scope statement.

  • Fatima Srinivasan· Aug 20, 2024

    Timeserver is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

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