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Waldur MCP Server

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by waldur

query

Execute read-only SQL queries to retrieve data from Waldur instances through the MCP server for integration and management tasks.

Instructions

Run a read-only SQL query

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sqlYes

Implementation Reference

  • Handler function for the MCP 'query' tool. Decorated with @mcp.tool(), it accepts an SQL string and executes it via the Waldur API client using api_query.asyncio.
    @mcp.tool()
    async def query(sql: str) -> list[Any]:
        """Run a read-only SQL query"""
        return await api_query.asyncio(client=client, body=QueryRequest(query=sql))
Behavior2/5

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

With no annotations, the description carries the full burden. It states 'read-only', which implies safety, but doesn't disclose other behavioral traits such as permissions needed, rate limits, error handling, or what the query returns. This is inadequate for a tool that executes arbitrary SQL.

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 extremely concise—a single sentence with no wasted words. It's front-loaded with the core purpose, making it easy to scan, though this brevity contributes to gaps in other dimensions.

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?

Given the complexity of executing SQL queries, no annotations, no output schema, and low schema coverage, the description is incomplete. It lacks critical context like return format, error cases, or usage prerequisites, making it insufficient for safe and effective tool invocation.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate. It adds no information about the 'sql' parameter beyond what the schema provides (a required string). No details on SQL dialect, allowed operations, or constraints are given, leaving significant gaps.

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 ('run') and resource ('SQL query'), specifying it's read-only. However, it doesn't differentiate from sibling tools like list_customers or list_invoices, which might also be read-only queries but with different scopes.

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 like the list_* siblings. It mentions 'read-only' but doesn't specify contexts or exclusions, leaving the agent to guess based on tool names alone.

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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