SAS MCP Server

A Model Context Protocol (MCP) server for executing SAS code on SAS Viya environments.

Features

• Execute SAS code on SAS Viya compute contexts
• OAuth2 authentication with PKCE flow
• HTTP-based MCP server compatible with MCP clients

Getting Started

Prerequisites

• Required

  • Python 3.12+
  • uv 0.8+
  • SAS Viya environment with compute service
  • Setup the Viya environment for MCP
    • See configuration.md

• Optional

  • Docker: refer to docker setup

Installation

1. Clone the repository:

git clone <repository-url>
cd sas-mcp-server

2. Install dependencies

uv sync

NOTE: This will by default create a virtual environment called .venv in the project's root directory.

If for some reason the virtual environment is not created, please run uv venv and then re-run uv sync.

Usage

1. Configure environment variables:

cp .env.sample .env

Edit .env and set

VIYA_ENDPOINT=https://your-viya-server.com

2. Start the MCP server (see Choosing a deployment mode below):

Option A: HTTP mode (pre-run the server, connect from MCP client)

uv run app

The server will be available at http://localhost:8134/mcp by default. Authentication is handled via OAuth2 PKCE flow in the browser.

Option B: Stdio mode (MCP client starts the server on demand)

Set VIYA_USERNAME and VIYA_PASSWORD in your .env file, then configure your MCP client to launch the server directly (see below).

Option C: Docker / Podman (containerized deployment)

docker build -t sas-mcp-server .
docker run -e VIYA_ENDPOINT=https://your-viya-server.com -p 8134:8134 sas-mcp-server

Choosing a deployment mode

	HTTP	Stdio	Docker
How it runs	Long-running server you start separately	MCP client spawns it on demand	Containerized HTTP server
Authentication	OAuth2 PKCE flow (browser popup)	Password grant (credentials in .env)	OAuth2 PKCE flow (browser popup)
Best for	Multi-user or shared setups; production-like environments	Single-user local development; quick experimentation	Team deployments; CI/CD; environments without Python installed
Requires	Python + uv	Python + uv	Docker or Podman only
Credentials stored?	No — user authenticates interactively	Yes — username/password in .env	No — user authenticates interactively
MCP client config	Point client to http://localhost:8134/mcp	Client runs uv run app-stdio	Point client to http://host:8134/mcp

Quick guidance:

• Starting out or exploring? Use stdio — zero setup beyond .env, and your MCP client manages the server lifecycle.
• Need secure, interactive auth? Use HTTP — no stored passwords, each user authenticates via browser.
• Deploying for a team or on a server? Use Docker — portable, no Python dependency on the host, easy to integrate with orchestrators.
• Using Gemini CLI? Use stdio — Gemini CLI does not support HTTP mode or browser-based OAuth. See Gemini CLI configuration.

Available Tools

Code Execution

• execute_sas_code: Execute SAS code snippets and retrieve execution results (log and listing output)

Data Discovery (CAS Management)

• list_cas_servers: List available CAS servers
• list_caslibs: List CAS libraries on a server
• list_castables: List tables in a CAS library
• get_castable_info: Get table metadata (row count, columns, size)
• get_castable_columns: Get column names, types, labels, formats
• get_castable_data: Fetch sample rows from a CAS table

Data Operations & Files

• upload_data: Upload CSV data into a CAS table
• promote_table_to_memory: Promote a table to global scope in CAS
• list_files: List files in the Viya Files Service
• upload_file: Upload a file to Viya Files Service
• download_file: Download file content

Reports & Visualization

• list_reports: List Visual Analytics reports
• get_report: Get report metadata and definition
• get_report_image: Render a report section as an image

Batch Jobs

• submit_batch_job: Submit a SAS job for async execution
• get_job_status: Check job state
• list_jobs: List recent/running jobs
• cancel_job: Cancel a running job
• get_job_log: Retrieve job log

Model Management & Scoring

• list_ml_projects: List AutoML projects
• create_ml_project: Create a new AutoML project
• run_ml_project: Run pipeline automation
• list_registered_models: List models in repository
• list_models_and_decisions: List published MAS modules
• score_data: Score data against a published model

Prompt Templates

• debug_sas_log: Analyze SAS log for errors with root-cause explanations
• explore_dataset: Generate data-profiling SAS code
• data_quality_check: Generate DQ assessment code
• statistical_analysis: Set up a statistical workflow with diagnostics
• optimize_sas_code: Review and optimize SAS code
• explain_sas_code: Block-by-block code explanation
• sas_macro_builder: Build production-quality SAS macros
• generate_report: Generate ODS/PROC REPORT code

MCP Client Configuration

Example configurations are provided in the examples/ folder. Below are quick-start snippets for common clients.

VS Code / Cursor / Claude Code (.vscode/mcp.json)

HTTP mode (requires uv run app running separately):

{
    "servers": {
        "sas-execution-mcp": {
            "url": "http://localhost:8134/mcp",
            "type": "http"
        }
    }
}

Stdio mode (starts the server on demand):

{
    "servers": {
        "sas-execution-mcp": {
            "command": "uv",
            "args": ["run", "app-stdio"],
            "cwd": "${workspaceFolder}"
        }
    }
}

Gemini CLI (.gemini/settings.json)

Gemini CLI only supports stdio mode. Add to your ~/.gemini/settings.json or project-level .gemini/settings.json:

{
    "mcpServers": {
        "sas-viya-mcp": {
            "command": "uv",
            "args": ["run", "app-stdio"],
            "cwd": "/path/to/sas-mcp-server",
            "timeout": 60000
        }
    }
}

Note: The timeout field (in milliseconds) is important — SAS Viya API calls can take longer than the Gemini CLI default of 10 seconds. A value of 60000 (60s) is recommended. Set cwd to the absolute path of your sas-mcp-server checkout.

Example

Execute SAS code through the MCP tool:

data work.students;
input Name $ Age Grade $;
datalines;
Alice 20 A
Bob 22 B
;
run;

proc print data=work.students;
run;

---

For more details, configuration options, and deployment options, please refer to the examples folder and follow the instructions listed there.

Testing

The project includes two layers of tests: unit tests (fast, no credentials required) and integration tests (run against a real SAS Viya instance).

Running Unit Tests

Unit tests verify tool schemas, request payloads, and internal logic without making any network calls:

./run_tests.sh

Or directly via pytest:

uv run python -m pytest -m "not integration" -v

Running Integration Tests

Integration tests call every tool against a live Viya environment. They require credentials, which can be provided via CLI arguments or .env:

Using .env (set VIYA_ENDPOINT, VIYA_USERNAME, VIYA_PASSWORD):

./run_tests.sh --integration

Using CLI arguments:

./run_tests.sh --integration \
    --endpoint https://your-viya-server.com \
    --username youruser \
    --password yourpassword

Integration tests only (skip unit tests):

./run_tests.sh --integration-only

Test Structure

File	Description
tests/test_tool_payloads.py	Payload assertions for all 26 tools — verifies URL paths, JSON body structure, query params, and headers
tests/test_integration.py	End-to-end workflow tests against a real Viya instance
tests/test_tools.py	Unit tests for HTTP helper functions (_get_json, _post_json, etc.)
tests/test_viya_utils.py	Unit tests for Viya compute session and job utilities
tests/test_mcp_server.py	Unit tests for MCP server and auth middleware
tests/test_prompts.py	Unit tests for prompt template rendering
tests/test_config.py	Unit tests for configuration loading

Contributing

Maintainers are accepting patches and contributions to this project. Please read CONTRIBUTING.md for details about submitting contributions to this project.

License & Attribution

Except for the the contents of the /static folder, this project is licensed under the Apache 2.0 License. Elements in the /static folder are owned by SAS and are not released under an open source license. SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration.

Separate commercial licenses for SAS software (e.g., SAS Viya) are not included and are required to use these capabilities with SAS software.

All third-party trademarks referenced belong to their respective owners and are only used here for identification and reference purposes, and not to imply any affiliation or endorsement by the trademark owners.

This project requires the usage of the following:

• Python, see the Python license here
• FastMCP, under the Apache 2.0 License
• uvicorn, under the BSD 3-Clause
• starlette, under the BSD 3-Clause
• httpx, under the MIT license