SSAS Server

A read-only Model Context Protocol server for querying SQL Server Analysis Services — both multidimensional cubes (MDX) and tabular models (DAX) — with Windows Integrated Security (Kerberos/NTLM, no passwords).

Companion to mssql_mcp_server, but for the OLAP side of the house.

Features

• list_metadata — discover a cube/model's measures, dimensions and hierarchies (via MDSCHEMA_* DMVs, which work for both model types), so the agent never guesses bracketed unique names. The overview stays compact — measures, dimension names and multi-level user hierarchies; pass dimension="[Dim Event]" to drill into one dimension's full attribute hierarchies and levels (a whole cube's attribute list can run to tens of thousands of tokens).
• execute_query — run an MDX (SELECT/WITH) or DAX (EVALUATE/DEFINE) query and get a flattened JSON table back.
• execute_dmv — guarded passthrough for SELECT ... FROM $SYSTEM.* schema rowsets (MDSCHEMA_*, TMSCHEMA_*, DISCOVER_*) for deeper introspection.
• Read-only by construction — statements that don't start with SELECT/WITH/EVALUATE/DEFINE are rejected, XMLA payloads are rejected, and there is no DDL/process surface at all.
• Row cap + query timeout on every call so a runaway crossjoin can't flood the context window.

Requirements

Windows-only by construction (the server drives ADOMD.NET via pythonnet):

1. Windows, domain-joined, with network access to the SSAS instance (default instance = TCP 2383).
2. The Windows account running the server has Read access to the SSAS database (role membership). Integrated auth means "whoever runs the process".
3. ADOMD.NET client libraries — ships with SSMS, or install the Analysis Services client libraries redistributable. Typically lands in C:\Program Files\Microsoft.NET\ADOMD.NET\160.
4. Python 3.11+

Installation

git clone https://github.com/dbdave/mssas_mcp_server
cd mssas_mcp_server
pip install -e .

Smoke-test connectivity before wiring up MCP (if this fails it's network/auth/ADOMD install, not the server):

python test_connection.py <server> <catalog>

Configuration

Authentication is always Windows Integrated Security (Integrated Security=SSPI) — no credentials in config.

MCP registration (Claude Code / Claude Desktop)

{
  "mcpServers": {
    "ssas-cube": {
      "command": "python",
      "args": ["-m", "ssas_mcp.server"],
      "env": {
        "SSAS_SERVER": "my-ssas-host",
        "SSAS_CATALOG": "My_Cube_Db",
        "SSAS_DEFAULT_CUBE": "Sales"
      }
    }
  }
}

One registered server targets one SSAS database. To use both a multidimensional and a tabular instance, register the server twice with different SSAS_SERVER/SSAS_CATALOG env blocks — the query language is chosen per call (execute_query accepts MDX or DAX, and the engine itself only accepts the language matching the model type).

Example usage

Multidimensional (MDX):

SELECT NON EMPTY { [Measures].[Tickets Issued] } ON COLUMNS,
       NON EMPTY { [Sales Item Fact Ordered Date].[Calendar].[Year Name].MEMBERS } ON ROWS
FROM [Sales]

Tabular (DAX):

EVALUATE SUMMARIZECOLUMNS('Date'[Year], "Total Sales", [Total Sales])

DMV introspection:

SELECT [MEASURE_NAME], [MEASUREGROUP_NAME] FROM $SYSTEM.MDSCHEMA_MEASURES WHERE [CUBE_NAME] = 'Sales'
SELECT * FROM $SYSTEM.TMSCHEMA_MEASURES  -- tabular models

Result format

Tools return JSON:

{
  "columns": ["[Sales Item Fact Ordered Date].[Calendar].[Year Name].[MEMBER_CAPTION]", "[Measures].[Tickets Issued]"],
  "rows": [["2025", 123456], ["2026", 98765]],
  "row_count": 2,
  "truncated": false
}

Cells can be null and columns can be mixed-type — MDX cellsets are flattened defensively (nulls preserved, Decimal → float, DateTime → ISO string).

Tests

pip install pytest
pytest tests/

License

MIT