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boettiger-lab

DuckDB MCP Server

query

Execute SQL queries on DuckDB databases to retrieve, analyze, or modify data stored locally, in memory, or in the cloud.

Instructions

Use this to execute a query on the DuckDB database

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesSQL query to execute that is a dialect of DuckDB SQL

Implementation Reference

  • Handler logic within handle_tool_call that executes the 'query' tool by invoking DatabaseClient.query on the provided SQL query.
    if name == "query":
        if arguments is None:
            return [
                types.TextContent(type="text", text="Error: No query provided")
            ]
        tool_response = db_client.query(arguments["query"])
        return [types.TextContent(type="text", text=str(tool_response))]
  • JSON Schema definition for the 'query' tool input, specifying the required 'query' string parameter.
    types.Tool(
        name="query",
        description="Use this to execute a query on the DuckDB database",
        inputSchema={
            "type": "object",
            "properties": {
                "query": {
                    "type": "string",
                    "description": "SQL query to execute that is a dialect of DuckDB SQL",
                },
            },
            "required": ["query"],
        },
    ),
  • Registration of the 'query' tool via the @server.list_tools() decorator and handle_list_tools function that returns the tool definition.
    @server.list_tools()
    async def handle_list_tools() -> list[types.Tool]:
        """
        List available tools.
        Each tool specifies its arguments using JSON Schema validation.
        """
        logger.info("Listing tools")
        return [
            types.Tool(
                name="query",
                description="Use this to execute a query on the DuckDB database",
                inputSchema={
                    "type": "object",
                    "properties": {
                        "query": {
                            "type": "string",
                            "description": "SQL query to execute that is a dialect of DuckDB SQL",
                        },
                    },
                    "required": ["query"],
                },
            ),
        ]
  • DatabaseClient.query method, the core helper function that executes the SQL query and formats the results, called by the tool handler.
    def query(self, query: str) -> str:
        try:
            return self._execute(query)
    
        except Exception as e:
            raise ValueError(f"❌ Error executing query: {e}")
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions execution but doesn't describe whether queries are read-only or mutating, what permissions are required, error handling, or result formats. This leaves significant behavioral gaps for a database query tool.

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

Conciseness5/5

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

The description is a single, efficient sentence with zero wasted words. It's appropriately sized for a single-parameter tool and gets straight to the point without unnecessary elaboration.

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

Completeness2/5

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

For a database query tool with no annotations and no output schema, the description is insufficient. It doesn't address critical context like query types supported, result formats, error conditions, or security implications, leaving the agent with incomplete understanding.

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?

With 100% schema description coverage, the input schema already documents the single 'query' parameter thoroughly. The description adds no additional parameter semantics beyond what's in the schema, meeting the baseline for high schema coverage.

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

Purpose4/5

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

The description clearly states the action ('execute a query') and target resource ('DuckDB database'), providing a specific verb+resource combination. However, with no sibling tools mentioned, there's no opportunity to distinguish from alternatives, preventing a perfect score.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives, prerequisites, or contextual constraints. It simply states what the tool does without any usage context or exclusions.

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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