Tabular

An MCP server that gives any LLM agent SQL over CSV and Excel files. DuckDB-powered, read-only, zero configuration.

Why

Embedding spreadsheets into a vector store gives agents mushy answers; tables chunk badly. The pattern that works in production is SQL-over-file: load the table into an analytical engine and let the agent query it. mcp-tabular packages that pattern as a standard MCP server, so it works with Claude Desktop, Claude Code, or any MCP client.

Agent ──MCP──▶ mcp-tabular ──▶ DuckDB (in-memory, read-only)
                  tools: load_file · list_tables · describe_table · query · sample_rows

Install & run

pip install -e .
mcp-tabular            # stdio transport

Claude Desktop config:

{
  "mcpServers": {
    "tabular": { "command": "mcp-tabular" }
  }
}

Tools

Safety

• Read-only: statements other than SELECT/WITH are rejected before execution.
• Path allow-listing: set MCP_TABULAR_ROOT to restrict which directory files may be loaded from.
• Bounded output: result sets are truncated (default 200 rows) so a bad query can't blow up the agent's context window.

Example session

> load_file("sales_q3.csv")
loaded table 'sales_q3' (8,412 rows). Columns: region VARCHAR, sku VARCHAR, units BIGINT, revenue DOUBLE

> query("SELECT region, SUM(revenue) r FROM sales_q3 GROUP BY 1 ORDER BY r DESC LIMIT 3")
| region | r        |
|--------|----------|
| South  | 412,050  |
| West   | 371,200  |
| East   | 298,700  |

Design notes

Built after shipping enterprise agents where tabular Q&A over uploaded files was the highest-volume use case. Schema + sample injection (via describe_table/sample_rows) is what makes agents write correct SQL on the first try.

License

MIT