RanchHand — OpenAI-compatible MCP Server (Architecture)

RanchHand is a minimal MCP server that fronts an OpenAI-style API. It works great with Ollama's OpenAI-compatible endpoints (http://localhost:11434/v1) and should work with other OpenAI-compatible backends.

Features

• Tools:
  • openai_models_list → GET /v1/models
  • openai_chat_completions → POST /v1/chat/completions
  • openai_embeddings_create → POST /v1/embeddings
  • Optional HTTP ingest on localhost:41414 (bind 127.0.0.1):
    • POST /ingest/slack (index: chunk + embed + upsert in in-memory store)
    • POST /query (kNN query with embeddings)
    • GET /profiles | POST /profiles (role defaults: embed, summarizers, reranker, chunking)
    • POST /answer (retrieve + generate answer with bracketed citations)
• Config via env:
  • OAI_BASE (default http://localhost:11434/v1)
  • OAI_API_KEY (optional; some backends ignore it, Ollama allows any value)
  • OAI_DEFAULT_MODEL (fallback model name, e.g. llama3:latest)
  • OAI_TIMEOUT_MS (optional request timeout)

Development

Linting

This project uses ESLint to maintain code quality and consistency.

# Run the linter to check for issues
npm run lint

# Automatically fix linting issues where possible
npm run lint:fix

The linting rules enforce:

• Consistent code style (single quotes, semicolons, 2-space indentation)
• Error prevention (no unused variables, no undefined variables)
• Modern JavaScript practices (const/let instead of var, arrow functions)

CI will automatically run linting checks on all pull requests.

Testing

This repo uses Vitest for unit tests. External network calls are mocked, so tests run deterministically without Ollama or internet access.

Commands:

# Run tests once
npm test

# TDD: watch mode
npm run test:watch

# With coverage report
npm run test:coverage

Coverage thresholds are configured in vitest.config.mjs (initial targets):

• Lines/Statements ≥ 60%
• Functions ≥ 55%
• Branches ≥ 50%

These thresholds indicate the minimum proportion of code exercised by tests. They are a guardrail, not a guarantee of correctness. We can raise them as the test suite grows.

Notes:

• Tests live in tests/**/*.test.js
• Use vi.spyOn/vi.mock to stub fetch and other external calls
• For CI stability, avoid real network calls in tests

Run (standalone)

# Example with Ollama running locally
export OAI_BASE=http://localhost:11434/v1
export OAI_DEFAULT_MODEL=llama3:latest
node server.mjs

HTTP Ingest Service

node http.mjs
# Binds to 127.0.0.1:41414
# Shared secret is created at ~/.threadweaverinc/auth/shared_secret.txt on first run

Example request:

SECRET=$(cat ~/.threadweaverinc/auth/shared_secret.txt)
curl -s -X POST http://127.0.0.1:41414/ingest/slack \
  -H "Content-Type: application/json" \
  -H "X-Ranchhand-Token: $SECRET" \
  -d '{
    "namespace":"slack:T123:C456",
    "channel":{"teamId":"T123","channelId":"C456"},
    "items":[{"ts":"1234.5678","text":"Hello world","userName":"Dan"}]
  }'

Query:

SECRET=$(cat ~/.threadweaverinc/auth/shared_secret.txt)
curl -s -X POST http://127.0.0.1:41414/query \
  -H "Content-Type: application/json" \
  -H "X-Ranchhand-Token: $SECRET" \
  -d '{
    "namespace":"slack:T123:C456",
    "query":"hello",
    "topK": 5,
    "withText": true
  }'

Answer with citations:

SECRET=$(cat ~/.threadweaverinc/auth/shared_secret.txt)
curl -s -X POST http://127.0.0.1:41414/answer \
  -H "Content-Type: application/json" \
  -H "X-Ranchhand-Token: $SECRET" \
  -d '{
    "namespace":"slack:T123:C456",
    "query":"What did Dan say about hello?",
    "topK": 3
  }'

Profiles:

curl -s http://127.0.0.1:41414/profiles
curl -s -X POST http://127.0.0.1:41414/profiles \
  -H "Content-Type: application/json" \
  -d '{ "embed": { "model": "nomic-embed-text:latest" }, "chunking": { "chunk_tokens": 512 } }'

MCP Tools

• openai_models_list
  • Input: {}
  • Output: OpenAI-shaped { data: [{ id, object, ... }] }
• openai_chat_completions
  • Input: { model?: string, messages: [{ role: 'user'|'system'|'assistant', content: string }], temperature?, top_p?, max_tokens? }
  • Output: OpenAI-shaped chat completion response (single-shot; streaming TBD)
• openai_embeddings_create
  • Input: { model?: string, input: string | string[] }
  • Output: OpenAI-shaped embeddings response

Claude/Codex (MCP)

Point your MCP config to:

{
  "mcpServers": {
    "ranchhand": {
      "command": "node",
      "args": ["/absolute/path/to/server.mjs"],
      "env": { "OAI_BASE": "http://localhost:11434/v1", "OAI_DEFAULT_MODEL": "llama3:latest" }
    }
  }
}

Notes

• Streaming chat completions are not implemented yet (single response per call). If your backend requires streaming, we can add an incremental content pattern that MCP clients can consume.
• RanchHand passes through OpenAI-style payloads and shapes outputs to be OpenAI-compatible, but exact metadata (usage, token counts) depends on the backend.
• HTTP ingest is currently an acknowledgment stub (counts + sample). Chunking/embedding/upsert will be wired next; design is pluggable for local store or Qdrant.