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execute_sql

Execute SQL queries on PostgreSQL databases to retrieve, modify, or analyze data directly through the MCP server interface.

Instructions

Execute any SQL query

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sqlNoSQL to runall

Implementation Reference

  • The implementation of the `execute_sql` tool which executes SQL against the database driver.
    async def execute_sql(
        sql: str = Field(description="SQL to run", default="all"),
    ) -> ResponseType:
        """Executes a SQL query against the database."""
        try:
            sql_driver = await get_sql_driver()
            rows = await sql_driver.execute_query(sql)  # type: ignore
            if rows is None:
                return format_text_response("No results")
            return format_text_response(list([r.cells for r in rows]))
        except Exception as e:
            logger.error(f"Error executing query: {e}")
            return format_error_response(str(e))
  • Registration of the `execute_sql` tool within the MCP server, with different descriptions depending on the access mode.
    if current_access_mode == AccessMode.UNRESTRICTED:
        mcp.add_tool(execute_sql, description="Execute any SQL query")
    else:
        mcp.add_tool(execute_sql, description="Execute a read-only SQL query")
Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full responsibility for disclosing safety traits. It completely omits whether the tool can modify data, required permissions, transaction behavior, or result format—critical omissions for arbitrary SQL execution.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

While brief (3 words), it is under-specified rather than efficiently concise. The brevity masks critical missing information (safety warnings, scope) rather than eliminating waste.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Completely inadequate for a high-risk arbitrary execution tool. Missing: output format (result sets vs row count), destructive operation warnings, DDL vs DML capabilities, and error handling expectations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% for the single `sql` parameter ('SQL to run'). The description adds no validation rules, syntax examples, or clarification of the unusual default value 'all', meeting the baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

States the core action (Execute) and resource (SQL query) but 'any' is dangerously unscoped and fails to distinguish from analytical siblings like `explain_query` or `analyze_query_indexes`.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides no guidance on when to use this versus `explain_query` or other analysis tools, nor warnings about using read-only vs write queries.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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