Skip to main content
Glama
bpamiri

CockroachDB MCP Server

by bpamiri

cancel_query

Stop a running query in CockroachDB by specifying its query ID to manage database performance and resource usage.

Instructions

Cancel a running query.

Args:
    query_id: The query ID to cancel.

Returns:
    Cancellation result.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
query_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Core implementation of cancel_query: executes 'CANCEL QUERY {query_id}' SQL command using the database connection.
    async def cancel_query(query_id: str) -> dict[str, Any]:
        """Cancel a running query.
    
        Args:
            query_id: The query ID to cancel.
    
        Returns:
            Cancellation result.
        """
        conn = await connection_manager.ensure_connected()
    
        try:
            async with conn.cursor() as cur:
                await cur.execute(f"CANCEL QUERY '{query_id}'")
    
            return {
                "status": "success",
                "query_id": query_id,
                "message": "Query cancelled",
            }
        except Exception as e:
            return {"status": "error", "error": str(e)}
  • MCP tool registration via @mcp.tool() decorator and wrapper that calls the cluster module's cancel_query function.
    @mcp.tool()
    async def cancel_query(query_id: str) -> dict[str, Any]:
        """Cancel a running query.
    
        Args:
            query_id: The query ID to cancel.
    
        Returns:
            Cancellation result.
        """
        try:
            return await cluster.cancel_query(query_id)
        except Exception as e:
            return {"status": "error", "error": str(e)}
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action ('cancel') but doesn't explain critical traits like permissions needed, whether cancellation is reversible, effects on system resources, or error handling. This leaves significant gaps for a mutation 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 highly concise and well-structured, using a brief purpose statement followed by clear sections for Args and Returns. Every sentence earns its place without redundancy, making it easy to parse quickly.

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

Completeness3/5

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

Given the tool's complexity (a mutation with no annotations) and the presence of an output schema (which handles return values), the description is minimally adequate. It covers the basic action and parameter but lacks details on behavioral context, usage scenarios, and error cases, making it incomplete for safe operation.

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?

The description adds minimal semantics beyond the input schema, which has 0% coverage. It names the parameter ('query_id') and its purpose ('The query ID to cancel'), but doesn't provide format details, examples, or constraints. This is the baseline score as it offers some value but doesn't fully compensate for the schema's lack of descriptions.

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 tool's purpose with a specific verb ('cancel') and resource ('a running query'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'rollback_transaction' or 'show_jobs' that might also manage query-related operations, 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, such as whether it applies to all query types or only specific ones from siblings like 'execute_query'. It lacks context on prerequisites (e.g., must be used on an active query) or exclusions, offering minimal usage direction.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/bpamiri/cockroachdb-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server