ai-mlproductivity

@contextable/mcp

Contextable-me

by Contextable-me

Persistent AI memory server for contextual storage and artifacts - full-text search, version history, and automatic cont

A persistent AI memory server that enables storage and retrieval of context and project artifacts across conversations. It features full-text search, version history, and automatic content chunking using local SQLite or hosted cloud storage.

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Works with local SQLite or cloud storageAutomatic content chunkingPersistent across sessions

best for

  • / Maintaining project context across multiple AI conversations
  • / Building a searchable knowledge base from AI interactions
  • / Teams sharing project context and artifacts

capabilities

  • / Store conversation context and project artifacts
  • / Search stored memories with full-text search
  • / Track version history of stored content
  • / Chunk large content automatically
  • / Retrieve context across different AI sessions

what it does

Stores and retrieves conversation context, project artifacts, and memories across AI sessions using local SQLite or cloud storage. Enables full-text search through your accumulated knowledge base.

about

@contextable/mcp is a community-built MCP server published by Contextable-me that provides AI assistants with tools and capabilities via the Model Context Protocol. Persistent AI memory server for contextual storage and artifacts - full-text search, version history, and automatic cont It is categorized under ai ml, productivity.

how to install

You can install @contextable/mcp 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

Apache-2.0

@contextable/mcp is released under the Apache-2.0 license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

@contextable/mcp

Give your AI a memory. Works with Claude Desktop and any MCP-compatible client.

Quick Start (30 seconds)

Add to your Claude Desktop config:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "contextable": {
      "command": "npx",
      "args": ["@contextable/mcp"]
    }
  }
}

Restart Claude Desktop. Done.

Now ask Claude: "Create a project called My App to track my development decisions"

What You Can Do

Save context that persists across conversations:

  • "Save this architecture decision to the project"
  • "Remember this API design for later"
  • "Store this bug investigation so I don't forget"

Find anything instantly:

  • "Search for authentication"
  • "What did we decide about the database?"
  • "Load all security-related artifacts"

Pick up where you left off:

  • "Resume my project"
  • "Show me the project summary"
  • "What were we working on?"

Import your chat history (NEW):

  • "Analyze my ChatGPT export at ~/Downloads/chatgpt-export.zip"
  • "Import the 'React Development' project from my chat history"
  • "What projects can you detect from my Claude conversations?"

Features

FeatureDescription
ProjectsOrganize context by project or topic
ArtifactsSave decisions, code snippets, docs, conversations
Full-Text SearchFind anything across all projects
Version HistoryEvery change tracked, rollback anytime
Auto-ChunkingLarge content automatically split
Topic ClusteringAuto-detect themes across artifacts
Chat ImportImport from ChatGPT, Claude, Gemini exports

All 15 Tools

Projects

  • project_save - Create or update a project
  • project_list - List all projects
  • project_resume - Load project with summaries
  • project_analysis_get - Get AI-generated insights

Artifacts

  • artifact_save - Save content (auto-chunks large files)
  • artifact_list - List with size estimates
  • artifact_get - Load full content
  • artifact_delete - Archive (recoverable)
  • artifact_restore - Restore archived
  • artifact_archived - List archived items
  • artifact_versions - View history
  • artifact_rollback - Restore previous version

Search

  • search - Full-text search across everything

Import (NEW in v0.2.0)

  • import_analyze - Analyze chat exports from ChatGPT, Claude, or Gemini
  • import_seed - Create projects from analyzed chat history

Your Data

Everything is stored locally in SQLite:

~/.contextable/data.db

No account. No cloud. No tracking. Your data stays on your machine.


Want More?

Contextable Cloud adds:

FeatureLocalCloud
Claude Desktop
ChatGPT-
Claude.ai (web)-
Sync across devices-
AI analysis & insights-
Team sharing-Coming soon

Try Contextable Cloud →


Configuration

Environment Variables

VariableDefaultDescription
CONTEXTABLE_DATA_DIR~/.contextableData directory
CONTEXTABLE_DB_PATH~/.contextable/data.dbSQLite database path
CONTEXTABLE_LOG_LEVELinfoLogging: debug, info, warn, error

Custom Database Location

{
  "mcpServers": {
    "contextable": {
      "command": "npx",
      "args": ["@contextable/mcp"],
      "env": {
        "CONTEXTABLE_DB_PATH": "/path/to/my/data.db"
      }
    }
  }
}

HTTP Mode (Advanced)

For web-based MCP clients, run as HTTP server:

npx @contextable/mcp --sse --port 3000

Endpoints:

  • GET /health - Health check
  • POST /mcp - MCP messages (JSON-RPC)

Programmatic Usage

import { SQLiteAdapter } from '@contextable/mcp';

const storage = new SQLiteAdapter({ path: './my-context.db' });
await storage.initialize();

// Create a project
const project = await storage.createProject({
  name: 'My Project',
  description: 'Project description',
});

// Save an artifact
const artifact = await storage.createArtifact({
  projectId: project.id,
  title: 'Design Decision',
  artifactType: 'decision',
  content: '# We chose PostgreSQL because...',
});

// Search
const results = await storage.search('postgresql');

await storage.close();

Development

git clone https://github.com/Contextable-me/mcp.git
cd mcp
npm install
npm run build
npm test

License

Apache 2.0 - See LICENSE

Links

FAQ

What is the @contextable/mcp MCP server?
@contextable/mcp 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 @contextable/mcp?
This profile displays 69 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. 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

4.669 reviews
  • Nikhil Garcia· Dec 28, 2024

    Strong directory entry: @contextable/mcp surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Hiroshi Jain· Dec 24, 2024

    @contextable/mcp is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • Dhruvi Jain· Dec 20, 2024

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

  • Yuki Bansal· Dec 12, 2024

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

  • Amina Dixit· Nov 19, 2024

    I recommend @contextable/mcp for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

  • Yuki Gonzalez· Nov 15, 2024

    We evaluated @contextable/mcp against two servers with overlapping tools; this profile had the clearer scope statement.

  • Oshnikdeep· Nov 11, 2024

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

  • Dev Garcia· Nov 3, 2024

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

  • Hiroshi Kapoor· Oct 22, 2024

    I recommend @contextable/mcp for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

  • Nikhil Flores· Oct 10, 2024

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

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