Persistent AI memory server for contextual storage and artifacts - full-text search, version history, and automatic cont
by 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
Time estimate: 15-60 minutes depending on server complexity
Prerequisites
Asana
MCP server for Asana — enables Claude to interact with Asana data and workflows.
Knowledge Graph Memory
Build persistent semantic networks for enterprise & engineering data management. Enable data persistence and memory acro
Spec Workflow MCP
Structured spec-driven development workflow for AI-assisted software development. Creates detailed specifications before
Jira
Integrate Jira software with AI to automate tasks, manage bugs, and streamline your development workflow efficiently.
Atlassian Cloud
Access Confluence pages and Jira in the cloud with Atlassian API. Integrate effortlessly using the REST API for Jira.
Strong directory entry: @contextable/mcp surfaces stars and publisher context so we could sanity-check maintenance before adopting.
@contextable/mcp is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
Useful MCP listing: @contextable/mcp is the kind of server we cite when onboarding engineers to host + tool permissions.
Useful MCP listing: @contextable/mcp is the kind of server we cite when onboarding engineers to host + tool permissions.
I recommend @contextable/mcp for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
We evaluated @contextable/mcp against two servers with overlapping tools; this profile had the clearer scope statement.
According to our notes, @contextable/mcp benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
According to our notes, @contextable/mcp benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
I recommend @contextable/mcp for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
According to our notes, @contextable/mcp benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
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Claude Desktop configuration
{
"mcpServers": {
"contextable": {
"command": "npx",
"args": [
"@contextable/mcp"
]
}
}
}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.
@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.
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.
Give your AI a memory. Works with Claude Desktop and any MCP-compatible client.
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"
Save context that persists across conversations:
Find anything instantly:
Pick up where you left off:
Import your chat history (NEW):
| Feature | Description |
|---|---|
| Projects | Organize context by project or topic |
| Artifacts | Save decisions, code snippets, docs, conversations |
| Full-Text Search | Find anything across all projects |
| Version History | Every change tracked, rollback anytime |
| Auto-Chunking | Large content automatically split |
| Topic Clustering | Auto-detect themes across artifacts |
| Chat Import | Import from ChatGPT, Claude, Gemini exports |
project_save - Create or update a projectproject_list - List all projectsproject_resume - Load project with summariesproject_analysis_get - Get AI-generated insightsartifact_save - Save content (auto-chunks large files)artifact_list - List with size estimatesartifact_get - Load full contentartifact_delete - Archive (recoverable)artifact_restore - Restore archivedartifact_archived - List archived itemsartifact_versions - View historyartifact_rollback - Restore previous versionsearch - Full-text search across everythingimport_analyze - Analyze chat exports from ChatGPT, Claude, or Geminiimport_seed - Create projects from analyzed chat historyEverything is stored locally in SQLite:
~/.contextable/data.db
No account. No cloud. No tracking. Your data stays on your machine.
Contextable Cloud adds:
| Feature | Local | Cloud |
|---|---|---|
| Claude Desktop | ✓ | ✓ |
| ChatGPT | - | ✓ |
| Claude.ai (web) | - | ✓ |
| Sync across devices | - | ✓ |
| AI analysis & insights | - | ✓ |
| Team sharing | - | Coming soon |
| Variable | Default | Description |
|---|---|---|
CONTEXTABLE_DATA_DIR | ~/.contextable | Data directory |
CONTEXTABLE_DB_PATH | ~/.contextable/data.db | SQLite database path |
CONTEXTABLE_LOG_LEVEL | info | Logging: debug, info, warn, error |
{
"mcpServers": {
"contextable": {
"command": "npx",
"args": ["@contextable/mcp"],
"env": {
"CONTEXTABLE_DB_PATH": "/path/to/my/data.db"
}
}
}
}
For web-based MCP clients, run as HTTP server:
npx @contextable/mcp --sse --port 3000
Endpoints:
GET /health - Health checkPOST /mcp - MCP messages (JSON-RPC)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();
git clone https://github.com/Contextable-me/mcp.git
cd mcp
npm install
npm run build
npm test
Apache 2.0 - See LICENSE
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.
Qdrant
Qdrant is a powerful vector database for AI systems to store and retrieve vector-based memories with advanced vector sea