SAS 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). For SSO/federated environments (e.g. Okta) the password grant does not work — set VIYA_REFRESH_TOKEN instead (see Headless authentication for SSO environments).

Option C: Direct HTTP mode (long-running server, no browser OAuth — for server-to-server MCP clients such as SAS Retrieval Agent Manager)

Set VIYA_USERNAME and VIYA_PASSWORD — or, for SSO/federated environments and unattended 24/7 use, VIYA_REFRESH_TOKEN (see Headless authentication for SSO environments) — (and optionally MCP_API_KEY) in your .env file, then:

uv run app-http-direct

The server authenticates to Viya itself with the .env credentials and serves streamable HTTP at http://host:8134/mcp (or SSE at http://host:8134/sse with MCP_TRANSPORT=sse). If MCP_API_KEY is set, clients must send it as an X-API-Key header or Authorization: Bearer token.

Option D: 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	Direct HTTP	Docker
How it runs	Long-running server you start separately	MCP client spawns it on demand	Long-running server you start separately	Containerized HTTP server
Authentication	OAuth2 PKCE flow (browser popup)	Password grant, or refresh token for SSO (in .env)	Password grant, or refresh token for SSO (in .env); optional API key on the endpoint	OAuth2 PKCE flow (browser popup)
Best for	Multi-user or shared setups; production-like environments	Single-user local development; quick experimentation	Server-to-server MCP clients that cannot do browser OAuth (e.g. SAS Retrieval Agent Manager)	Team deployments; CI/CD; environments without Python installed
Requires	Python + uv	Python + uv	Python + uv	Docker or Podman only
Credentials stored?	No — user authenticates interactively	Yes — username/password or refresh token in .env	Yes — username/password or refresh token 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 (+ API key if set)	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.
• Connecting from SAS Retrieval Agent Manager (RAM)? Use direct HTTP — in RAM, add a Remote MCP server with transport Streamable HTTP, URL http://<host>:8134/mcp, and authentication API Key (matching MCP_API_KEY) or None. If your Viya uses SSO/Okta, authenticate the server to Viya with VIYA_REFRESH_TOKEN (set it as a secret on the tool server's Environment Variables tab) rather than a username/password — see Headless authentication for SSO environments.

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
• generate_synthetic_data: Generate a synthetic CAS table from a column spec (id/int/float/category/bool/date), saved promoted to CAS — for building demo/mock datasets
• list_files: List files in the Viya Files Service
• upload_file: Upload a file to Viya Files Service
• download_file: Download file content

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
• delete_ml_project: Delete an AutoML project
• list_registered_models: List models in repository
• list_models_and_decisions: List published MAS modules
• score_data: Score data against a published model

Data Insights

• explain_data: Natural-language insights about a table column (SAS Insights)

Visualization

• render_chart: Emit an interactive chart spec (bar/line/area/pie/scatter) for the custom UI to render

Use-Case Scoping

• get_use_case: Report the datasets, models, and decisions this assistant is limited to

Note: SAS Visual Analytics reporting tools (listing, authoring, rendering, and PDF export of VA reports) are intentionally not part of this server — they are handled by a dedicated reporting MCP server.

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

Use-Case Scoping

By default the server exposes the entire SAS Viya environment. To build a chatbot focused on a single use case, scope it to a curated set of resources using environment variables — no code changes:

Variable	Purpose
USE_CASE_NAME / USE_CASE_DESCRIPTION	Identify the use case (returned by get_use_case)
ALLOWED_TABLES	CAS tables — table, caslib.table, or server.caslib.table
ALLOWED_MODELS	Model IDs or names
ALLOWED_DECISIONS	Decision / MAS-module IDs or names
SCOPE_ENFORCE	true (default) blocks out-of-scope access; false only hides it from listings

Entries are comma- or newline-separated and matched case-insensitively against both IDs and names. When a scope is active:

• list tools (list_castables, list_registered_models, list_models_and_decisions) return only the allowed resources;
• get_use_case tells the agent its scope deterministically (so you don't rely on the system prompt);
• resource-access tools (e.g. get_castable_info, score_data, explain_data) refuse out-of-scope IDs when SCOPE_ENFORCE=true;
• execute_sas_code remains unrestricted.

With none of the ALLOWED_* variables set, the server behaves exactly as before (full access). This makes it easy to stand up many per-use-case assistants from one image — for example, in SAS Retrieval Agent Manager, register the container once as a Container MCP Server code template, then create one tool server per use case and set these variables on its Environment Variables tab.

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 every tool — 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