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get_execution

Retrieve detailed workflow execution results to inspect step outputs, debug failures, and analyze intermediate data from completed workflows.

Instructions

Get full execution details including results from each completed step. The executionContent field maps stepId -> step output data. Use this to inspect what a workflow produced, debug failures, or check intermediate results.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
workflowIdYesThe workflow ID
executionIdYesThe execution ID

Implementation Reference

  • Registration and handler implementation for the 'get_execution' MCP tool.
        server.tool(
            'get_execution',
            `Get full execution details including results from each completed step.
    The executionContent field maps stepId -> step output data.
    Use this to inspect what a workflow produced, debug failures, or check intermediate results.`,
            {
                workflowId: z.string().describe('The workflow ID'),
                executionId: z.string().describe('The execution ID'),
            },
            async ({ workflowId, executionId }, extra) => {
                const client = clientFactory(extra);
                const result = await client.getExecution(workflowId, executionId);
                return {
                    content: [{
                        type: 'text' as const,
                        text: JSON.stringify(result, null, 2),
                    }],
                };
            }
        );
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It describes the tool as a read operation ('Get full execution details'), which implies it's non-destructive, but doesn't explicitly state permission requirements, rate limits, or error behaviors. The description adds some behavioral context by explaining the structure of the output ('executionContent field maps stepId -> step output data'), which is valuable given no output schema.

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 front-loaded with the core purpose in the first sentence, followed by specific usage contexts. Every sentence adds value: the first defines the tool, the second explains output structure, and the third provides usage scenarios. There is no wasted text or redundancy.

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

Completeness4/5

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

Given no annotations and no output schema, the description does a good job of explaining what the tool returns (execution details with step outputs) and its purpose. However, it lacks details on error cases, pagination, or authentication requirements, which could be relevant for a tool that retrieves execution data. It's mostly complete but has minor gaps.

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 description coverage is 100%, so the schema already documents both parameters (workflowId and executionId) adequately. The description doesn't add any parameter-specific details beyond what the schema provides, such as format examples or relationships between parameters. This meets the baseline for high schema coverage.

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

Purpose5/5

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 specific verbs ('Get full execution details including results from each completed step') and distinguishes it from siblings like 'list_executions' (which likely lists executions) and 'get_workflow' (which likely gets workflow definitions). It explicitly mentions what the tool retrieves: execution details with step outputs.

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

Usage Guidelines4/5

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

The description provides clear context for when to use this tool: 'to inspect what a workflow produced, debug failures, or check intermediate results.' It doesn't explicitly state when not to use it or name alternatives (e.g., 'list_executions' for summaries), but the context is sufficiently detailed for an agent to infer usage scenarios.

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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