opensearch-orchestrator 0.2.5

OpenSearch Solution Architect MCP server — guides you from requirements to a running search setup

This repo uses the strands framework to build an OpenSearch semantic search solution architect agent. The agent collects user requirements and gives recommendations for index types.

There are two ways to use the agent: as a standalone interactive CLI or via an MCP server that any MCP-compatible client can drive.

Standalone Agent

Start the interactive orchestrator in a terminal:

python opensearch_orchestrator/orchestrator.py

The orchestrator guides you through sample collection, requirements gathering, solution planning, and execution — all in one interactive session.

MCP Server (Cursor, Claude Desktop, etc.)

The MCP server exposes the same orchestrator workflow as a set of phase tools. A client LLM drives the conversation with the user and calls the tools in order.

Prerequisites

Install uv (one-time, no sudo needed):

curl -LsSf https://astral.sh/uv/install.sh | sh

Running manually

uv run opensearch_orchestrator/mcp_server.py

uv reads the inline script metadata in opensearch_orchestrator/mcp_server.py and auto-installs dependencies into a cached virtual environment.

Running from PyPI (uvx)

After publishing to PyPI, run the MCP server without cloning the repo:

uvx opensearch-orchestrator@latest

If you install via pip, you can also run:

opensearch-orchestrator

Important: this command starts a stdio MCP server (JSON-RPC), not an interactive CLI. It should be launched by an MCP client such as Cursor, Claude Desktop, or MCP Inspector. If you want an interactive terminal workflow, run:

python opensearch_orchestrator/orchestrator.py

MCP workflow tools

The server exposes high-level phase tools that mirror the standalone orchestrator workflow:

Tool	Phase	Description
load_sample	1	Load a sample document (built-in, file, URL, index, or paste); localhost-index mode supports explicit auth mode/credentials
set_preferences	2	Set budget, performance, query pattern, deployment preferences
start_planning	3	Start the planning agent; returns initial architecture proposal
refine_plan	3	Send user feedback to refine the proposal
finalize_plan	3	Finalize the plan when the user confirms
talk_to_client_llm	3/4	General MCP client-sampling bridge for client LLM turns
set_plan_from_planning_complete	3	Parse/store a <planning_complete> planner response
execute_plan	4	Return manual worker bootstrap payload (no server-side Bedrock execution in MCP)
set_execution_from_execution_report	4	Parse/store normalized <execution_report> and update retry state
retry_execution	4	Return resume bootstrap payload from last failed step
cleanup	Post	Remove test documents on user request

The following execution/knowledge tools are exposed by default for manual client-driven execution: create_index, create_and_attach_pipeline, create_bedrock_embedding_model, create_local_pretrained_model, apply_capability_driven_verification, launch_search_ui, set_search_ui_suggestions, read_knowledge_base, read_dense_vector_models, read_sparse_vector_models, search_opensearch_org.

Advanced tools (set_plan, raw sample-submit variants, indexing helpers, etc.) are hidden by default and only exposed when OPENSEARCH_MCP_ENABLE_ADVANCED_TOOLS=true.

Localhost index auth contract (Option 3 / source_type="localhost_index"):

• localhost_auth_mode="default": force username admin with password myStrongPassword123!
• localhost_auth_mode="none": force no authentication
• localhost_auth_mode="custom": require localhost_auth_username + localhost_auth_password
• Local Docker auto-bootstrap always uses the admin username and sets OPENSEARCH_INITIAL_ADMIN_PASSWORD from OPENSEARCH_PASSWORD when provided (otherwise uses myStrongPassword123!).

Planner backend in MCP mode:

• MCP planning uses client sampling / client LLM only (no Bedrock fallback in MCP mode).
• Manual fallback: if the MCP client does not support sampling/createMessage, start_planning returns manual_planning_required=true plus manual_planner_system_prompt and manual_planner_initial_input; run planner turns with the client LLM and call set_plan_from_planning_complete(planner_response).

Cursor integration

1. Add the following to .cursor/mcp.json in your workspace (adjust cwd to the repo path):

{
  "mcpServers": {
    "opensearch-orchestrator": {
      "command": "uv",
      "args": ["run", "opensearch_orchestrator/mcp_server.py"],
      "cwd": "/path/to/agent-poc"
    }
  }
}

2. Reload the Cursor window (Cmd+Shift+P → "Developer: Reload Window"), then enable the server in Cursor Settings → MCP.

3. A Cursor rule at .cursor/rules/opensearch-workflow.mdc auto-activates when you ask about OpenSearch solution design and teaches the LLM the tool sequence.

If Cursor cannot find uv on its PATH, use the absolute path (e.g. ~/.local/bin/uv).

Claude Desktop integration

1. Copy claude_desktop_config.example.json to your Claude Desktop config directory:

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

2. Edit the cwd path to point to this repo.

3. Restart Claude Desktop. The opensearch_workflow prompt is available in the prompt picker and describes the full tool sequence.

Generic MCP clients

Any MCP-compatible client can connect via stdio and discover tools with tools/list. The opensearch_workflow prompt (available via prompts/list) describes the workflow. Tool docstrings also include prerequisite hints.

Without uv

If you prefer not to install uv, install dependencies manually and use Python directly:

pip install mcp opensearch-py

{
  "mcpServers": {
    "opensearch-orchestrator": {
      "command": "python3",
      "args": ["opensearch_orchestrator/mcp_server.py"],
      "cwd": "/path/to/agent-poc"
    }
  }
}

Release checklist

Build and validate before publishing:

# 1) bump version manually (not automatic)
#    update both files to the same value, e.g. 0.10.1
#    - pyproject.toml: [project].version
#    - opensearch_orchestrator/__init__.py: __version__
#
# optional sanity check:
python -c "import tomllib; p=tomllib.load(open('pyproject.toml','rb')); import opensearch_orchestrator as pkg; print('pyproject=', p['project']['version'], 'package=', pkg.__version__)"

# 2) all tests have to pass
uv run pytest -q

# 3) build and verify artifacts
uv build
for whl in dist/*.whl; do python -m zipfile -l "$whl"; done
python -c "import opensearch_orchestrator.mcp_server as m; print(hasattr(m, 'main'))"
# pick wheel for the current package version (avoids selecting older builds)
VERSION="$(python -c "import tomllib; print(tomllib.load(open('pyproject.toml','rb'))['project']['version'])")"
WHEEL_PATH="$(ls dist/opensearch_orchestrator-${VERSION}-*.whl)"
uvx --from "$WHEEL_PATH" opensearch-orchestrator

# 4) upload to PyPI (needs a PyPI account + API token)
uv publish --token pypi-YOUR-TOKEN

Then publish to TestPyPI for smoke tests, followed by PyPI.