Which integrations are available for this server?

Enables configuration of database connections, server network settings, and agent communication transport through environment variables. Provides a web interface and JSON API endpoints for accessing the TPC data, allowing users to browse thoughts, plans, and changes via a simple HTML interface. Supported as an optional alternative database backend that can be configured by changing the DATABASE_URL environment variable. Supported as an optional alternative database backend that can be configured by changing the DATABASE_URL environment variable. Manages database operations for storing and retrieving thoughts, plans, and changes with support for relationship tracking between these entities. Used as the default database backend for storing the TPC data with simple file-based storage requiring no separate database server.

How do I use tpc-server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@tpc-server search for plans about AI collaboration with urgent tags" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

TPC Server

A Node.js/Express API for AI-human collaboration, starting with JSON file storage for thoughts and plans.

Setup and Usage

Install dependencies: npm install
Start the server: node server.js
The server runs on http://localhost:3000

Changelog

See CHANGELOG.md for detailed release notes.

Testing

Run npm test to execute Jest tests verifying the endpoint functionality. Run npx playwright test for E2E UI tests.

Project Structure

server.js: Main Express server with modular structure (db/, routes/, middleware/).
mcp-server.js: MCP server for AI clients (stdio transport). Run with npm run mcp.
data/tpc.db: SQLite database for persistent storage of thoughts and plans.
tests/: Test files (Jest unit tests, MCP tests).
public/: Static single-page UI.

Usage Examples

REST API

Retrieve all thoughts: curl http://localhost:3000/thoughts
Retrieve all plans: curl http://localhost:3000/plans
Create a thought: curl -X POST http://localhost:3000/thoughts -H "Content-Type: application/json" -d '{"content": "My thought"}'
Create a plan: curl -X POST http://localhost:3000/plans -H "Content-Type: application/json" -d '{"title": "My Plan", "description": "Plan details"}'
Update plan status: curl -X PATCH http://localhost:3000/plans/1 -H "Content-Type: application/json" -d '{"status": "in_progress"}'
Search across plans/thoughts: curl "http://localhost:3000/search?q=AI&type=plans&tags=urgent&limit=5"
Add tags to a plan: curl -X PATCH http://localhost:3000/plans/1/tags -H "Content-Type: application/json" -d '{"tag": "ai"}' (appends) or {"tags": ["ai", "urgent"]} (replaces)
Filter plans by tags: curl "http://localhost:3000/plans?tags=ai,urgent"
View UI: Visit http://localhost:3000/index.html after starting the server.

MCP Server

Start MCP server: npm run mcp
Connects via stdio — configure your MCP client to use this
Available tools: list_plans, get_plan, create_plan, update_plan, list_thoughts, create_thought, search_thoughts, get_context
Available resources: tpc://plans, tpc://thoughts, tpc://context

Related MCP server: Satellite Tracking MCP Server

Features

MCP Server: Full MCP protocol implementation for AI clients. Run with npm run mcp (stdio transport).
Modular server architecture: Organized into db/, routes/, and middleware/ for maintainable API development.
SQLite persistence: Single data/tpc.db for thoughts and plans with idempotent schema migrations.
Comprehensive testing: Jest for unit tests (endpoints, validation, integrations) and Playwright for E2E UI tests (rendering, interactions).
AI collaboration focus: Endpoints like /context aggregate data for agent memory; supports human edits with review flags.
Rich text support: Plan descriptions accept and display Markdown formatting (bold, lists, etc.) in the UI using marked.js.
Search and organization: Full-text search API (/search?q=) with type/tags/limit filters; UI search input and tag-based filtering/editing.
Tagging system: Add/edit/filter tags on plans/thoughts via API/UI for better categorization (e.g., ['ai', 'urgent']).

v2.7 - Search and Organization

Features Implemented

Full-text search endpoint (GET /search?q=<query>) with optional ?type=plans|thoughts, ?tags=tag1,tag2, ?limit=N for targeted results.
Tagging system: tags column (JSON array) on plans/thoughts tables; manage via POST/PUT/PATCH /plans/:id/tags (and for thoughts).
Tag filtering: ?tags=ai,urgent (AND logic) on list endpoints (/plans, /thoughts, /search).
Enhanced /context with ?search=<query> to filter aggregated data.
UI: Global search input, tag editing in details, tag filtering dropdowns in lists.

Usage

API search: curl "http://localhost:3000/search?q=collaboration&type=plans&tags=ai"
Tag a plan: curl -X PATCH http://localhost:3000/plans/1/tags -H "Content-Type: application/json" -d '{"tags": ["ai", "urgent"]}' (replace) or {"tag": "new"} (append).
Filter by tags: curl "http://localhost:3000/plans?tags=ai,urgent"
UI: Enter query in search box; click tags to edit/filter in plan/thought views.

Notable Changes

Schema addition: tags TEXT column (default '[]') to plans/thoughts tables; backward compatible migration with backfill.
New route: /search for unified querying.
Builds on v2.6 Markdown support and modular structure.