SAS MCP Server

A Model Context Protocol (MCP) server for executing SAS code, training AutoML projects, scoring models and so much more for SAS Viya environments.

Features

• 30+ Tools spanning the Analytics Life Cycle across SAS Viya

• Prompt Templates for improving your SAS Code

• OAuth2 authentication with PKCE flow

• HTTP-based MCP server compatible with MCP clients

Related MCP server: Lab Virtual MCP Server

Articles & Videos

Here you can find getting articles on how to use and integrate the SAS MCP Server in different tools and what to build with it:

• Connecting GitHub Copilot to SAS Viya with the SAS MCP Server

• Putting the SAS MCP Server to Work in GitHub Copilot

• Connecting Claude Code CLI to SAS Viya with the SAS MCP Server

• Putting the SAS MCP Server to Work in Claude Code CLI

• Integration with SAS Retrieval Agent Manager (RAM)

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)

Authenticate once. Two equivalent options:

# Option B1 — if you have the SAS Viya CLI installed:
sas-viya auth loginCode

# Option B2 — built-in helper, no external CLI needed (Viya 2022.11+):
uv run sas-mcp-login

Both flows write an access token to a local cache (~/.sas/credentials.json and ~/.sas-mcp-server/credentials.json respectively); the stdio server reads whichever it finds. When the token expires, re-run the same command.

Then configure your MCP client to launch the server directly (see below).

Option C: Docker / Podman (containerized deployment)

Pull the pre-built image from GitHub Container Registry:

docker pull ghcr.io/sassoftware/sas-mcp-server:latest
docker run -e VIYA_ENDPOINT=https://your-viya-server.com -p 8134:8134 ghcr.io/sassoftware/sas-mcp-server:latest

Or build locally from source:

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

Available image tags:

• latest — most recent tagged release

• <major>.<minor>.<patch> (e.g. 1.0.0) — specific release

• <major>.<minor> (e.g. 1.0) — latest patch of a minor release

• edge — tip of main (unreleased, for testing)

• sha-<short> — pinned to a specific commit

Programmatic clients with a pre-existing Viya token

If your caller already holds a Viya access token (e.g. an automation script that obtained one via the SAS Viya CLI), start the HTTP-mode server with ALLOW_RAW_BEARER=true and pass the token directly:

curl -H "Authorization: Bearer $VIYA_TOKEN" http://localhost:8134/mcp ...

The server validates the token against Viya's JWKS and uses it upstream as-is, bypassing the MCP JWT swap. The default OAuth2 PKCE flow keeps working alongside — both client types share the same /mcp endpoint.

Choosing a deployment mode

Quick guidance:

• Starting out or exploring? Use stdio — one sas-viya auth loginCode or uv run sas-mcp-login, then 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). Runs in a reusable, per-user compute session that is kept warm across calls, so SAS state (WORK tables, macro variables, assigned librefs) persists between successive calls — use reset_compute_session to start fresh.

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

• list_source_tables: List source tables not yet loaded into memory (candidates for promotion)

• 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: Load a source table into memory at global scope (idempotent)

• 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 from a loaded, global-scope CAS table (caslib + table + optional CAS server)

• 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

Compute Contexts & Code Execution

• list_compute_contexts: List available compute contexts

• list_compute_libraries: List the SAS libraries (librefs) assigned in a compute context

• list_compute_tables: List the tables in a SAS library within a compute context

• list_compute_columns: List the columns of a table in a SAS library

• reset_compute_session: Delete the cached compute session for a context, discarding its SAS state and forcing a fresh session on the next call

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

Every one of the 32 tools and 8 prompt templates has an integration test, enforced by the test_every_tool_has_integration_coverage / test_every_prompt_has_integration_coverage guards — adding a new tool or prompt without integration coverage fails the suite. The resource-dependent tests discover real targets on the instance: score_data scores the most recently modified MAS module (discovering a real step and its inputs), and run_ml_project re-runs the most recently modified completed ML project. They skip only if the instance has no such resource at all.

In CI: the .github/workflows/integration.yml workflow runs this suite on demand (manual dispatch, or by adding the run-integration label to a PR) using repository secrets, and publishes the results back to the PR as a status check, a sticky comment, and a downloadable JUnit artifact. Result files are written to reports/ (git-ignored) and are never committed.

Locally (attach results to a PR yourself): run with --report to write the JUnit XML and a Markdown summary into reports/ (git-ignored), then post them to a PR with the GitHub CLI — no commit, no CI required:

./run_tests.sh --integration-only --report
gh pr comment <PR> --body-file reports/integration-summary.md   # summary table as a comment
gh gist create reports/integration.xml                          # full XML as a linkable gist

GitHub has no API/CLI to attach a binary file to a PR (drag-and-drop upload is browser-only), so the summary is posted as a comment and the raw XML is shared via a gist link or pasted in a collapsed <details> block. To produce the canonical Actions artifact from your machine instead, trigger the workflow remotely: gh workflow run integration.yml.

Test Structure

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.

As with any container image, direct and indirect dependencies are governed by their own licenses. Users of the published container image are responsible for ensuring that their use complies with all applicable licenses.

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.

Third-Party Dependencies

This project requires the following dependencies.