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., "@th0th search for login function in my project" 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.

th0th

by S1LV4

Overview Schema Related Servers Score Discussions

TypeScript

Local

th0th

Ancient knowledge keeper for modern code

Semantic search with 98% token reduction for AI assistants.

Como reduzi 98% do uso de contexto (e custos) de IA no meu workflow / How I reduced AI context usage (and costs) by 98% in my workflow https://www.tabnews.com.br/S1LV4/como-reduzi-em-98-por-cento-o-uso-de-contexto-e-os-custos-de-ia-no-meu-workflow

Quick Start

One-line install (recommended)

curl -fsSL https://raw.githubusercontent.com/S1LV4/th0th/main/install.sh | bash

Installs interactively. Three modes:

Mode	Requires	Best for
Docker (default)	Docker	Production, quick start
Docker build	Docker + Git	Custom builds, local changes
Source	Git + Bun	Development, contributors

Non-interactive (CI/scripted):

# Docker mode, custom port, skip start
TH0TH_MODE=docker TH0TH_API_PORT=4000 TH0TH_NO_START=1 \
  curl -fsSL https://raw.githubusercontent.com/S1LV4/th0th/main/install.sh | bash

Manual setup (from source)

# 1. Clone and install
git clone https://github.com/S1LV4/th0th.git
cd th0th
bun install

# 2. Setup (100% offline with Ollama)
./scripts/setup-local-first.sh
# - Installs/starts Ollama
# - Pulls bge-m3 embedding model (1024 dimensions)
# - Creates .env with defaults
# - Runs bun run diagnose to validate the stack

# 3. Build and start
bun run build
bun run start:api

Verify: curl http://localhost:3333/health

Tip: Run bun run diagnose at any time to validate Ollama connectivity, database access, embedding generation, and migration status.

Integration

OpenCode (recommended)

File: ~/.config/opencode/opencode.json

Via MCP package:

{
  "mcp": {
    "th0th": {
      "type": "local",
      "command": [
        "bunx",
        "@th0th-ai/mcp-client"
      ],
      "environment": {
        "TH0TH_API_URL": "http://localhost:3333"
      },
      "enabled": true
    }
  }
}

Via Plugin:

{
  "plugin": ["@th0th-ai/opencode-plugin"]
}

From source (development):

{
  "mcpServers": {
    "th0th": {
      "type": "local",
      "command": ["bun", "run", "/path/to/th0th/apps/mcp-client/src/index.ts"],
      "enabled": true
    }
  }
}

VSCode / Antigravity

Create .vscode/mcp.json in your workspace:

{
  "servers": {
    "th0th": {
      "command": "bunx",
        "args": ["@th0th-ai/mcp-client"],
      "env": {
        "TH0TH_API_URL": "http://localhost:3333"
      }
    }
  }
}

Or run ./scripts/setup-vscode.sh for automatic configuration.

Docker

{
  "mcpServers": {
    "th0th": {
      "type": "local",
      "command": ["docker", "compose", "run", "--rm", "-i", "mcp"],
      "enabled": true
    }
  }
}

Available Tools

Tool	Description
`th0th_index`	Index a project directory for semantic search
`th0th_search`	Semantic + keyword search with filters
`th0th_remember`	Store important information in persistent memory
`th0th_recall`	Search stored memories from previous sessions
`th0th_compress`	Compress context (keeps structure, removes details)
`th0th_optimized_context`	Search + compress in one call (max token efficiency)
`th0th_analytics`	Usage patterns, cache performance, metrics

REST API

# Development
bun run dev:api

# Production
bun run start:api

Swagger docs: http://localhost:3333/swagger

Endpoints

# Index a project
curl -X POST http://localhost:3333/api/v1/project/index \
  -H "Content-Type: application/json" \
  -d '{"projectPath": "/home/user/my-project", "projectId": "my-project"}'

# Search
curl -X POST http://localhost:3333/api/v1/search/project \
  -H "Content-Type: application/json" \
  -d '{"query": "authentication", "projectId": "my-project"}'

# Store memory
curl -X POST http://localhost:3333/api/v1/memory/store \
  -H "Content-Type: application/json" \
  -d '{"content": "Important decision...", "type": "decision"}'

# Compress context
curl -X POST http://localhost:3333/api/v1/context/compress \
  -H "Content-Type: application/json" \
  -d '{"content": "...", "strategy": "code_structure"}'

Configuration

Config file: ~/.config/th0th/config.json (auto-created on first run)

Quick Config Commands

# Show current configuration
npx @th0th-ai/mcp-client --config-show

# Show config file path
npx @th0th-ai/mcp-client --config-path

# Show config directory
npx @th0th-ai/mcp-client --config-dir

# Initialize configuration
npx @th0th-ai/mcp-client --config-init

# Show help
npx @th0th-ai/mcp-client --help

Embedding Providers

Provider	Model	Cost	Quality
Ollama (default)	qwen3-embedding, bge-m3, nomic-embed-text	Free	Good-Excellent
Mistral	mistral-embed, codestral-embed	$$	Great
OpenAI	text-embedding-3-small	$$	Great

Advanced Configuration

For detailed configuration management, use the config CLI:

# Initialize with specific provider
npx @th0th-ai/mcp-client --config-init                          # Ollama (default)
npx @th0th-ai/mcp-client --config-init --mistral your-api-key   # Mistral
npx @th0th-ai/mcp-client --config-init --openai your-api-key    # OpenAI

# Switch provider
npx @th0th-ai/mcp-client --config-init --mistral your-api-key
npx @th0th-ai/mcp-client --config-init --ollama-model qwen3-embedding

# Set specific configuration values
npx @th0th-ai/mcp-client --config-set embedding.dimensions 4096

Scripts

Command	Description
`bun run build`	Build all packages
`bun run dev`	Development (all apps)
`bun run dev:api`	REST API with hot reload
`bun run dev:mcp`	MCP server with watch
`bun run start:api`	Start REST API
`bun run start:mcp`	Start MCP server
`bun run test`	Run tests
`bun run lint`	Lint code
`bun run type-check`	Type checking
`bun run diagnose`	Validate full stack (Ollama, database, embeddings)

Architecture

th0th/
├── apps/
│   ├── mcp-client/           # MCP Server (stdio)
│   ├── tools-api/            # REST API (port 3333)
│   └── opencode-plugin/      # OpenCode plugin
├── packages/
│   ├── core/                 # Business logic, search, embeddings, compression
│   └── shared/               # Shared types & utilities
└── scripts/

Component	Description
Semantic Search	Hybrid vector + keyword with RRF ranking
Embeddings	Ollama (local) or Mistral/OpenAI API
Compression	Rule-based code structure extraction (70-98% reduction)
Memory	Persistent SQLite storage across sessions
Cache	Multi-level L1/L2 with TTL

License

MIT

This server cannot be installed

license - permissive license

quality - not tested

maintenance - not tested

How are these scores calculated?

Resources

GitHub Repository

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