Skip to main content
Glama
punkpeye

HiveFlow MCP Server

execute_flow

Execute a specific workflow by providing its ID and optional inputs to trigger automation processes through the HiveFlow platform.

Instructions

Ejecuta un flujo de trabajo especĂ­fico

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
flowIdYesID del flujo a ejecutar
inputsNoInputs opcionales para el flujo

Implementation Reference

  • The executeFlow handler function that sends a POST request to the HiveFlow API to execute the specified flow and returns a success message with execution details.
    async executeFlow(args) {
      const response = await this.hiveflowClient.post(`/api/flows/${args.flowId}/execute`, {
        inputs: args.inputs || {}
      });
      
      return {
        content: [
          {
            type: 'text',
            text: `🚀 Flujo ejecutado exitosamente.\nExecution ID: ${response.data.executionId || 'N/A'}\nEstado: ${response.data.status || 'iniciado'}`
          }
        ]
      };
    }
  • The executeFlow handler function (TypeScript version) that sends a POST request to the HiveFlow API to execute the specified flow and returns a success message with execution details.
    private async executeFlow(args: any) {
      const response = await this.hiveflowClient.post(`/api/flows/${args.flowId}/execute`, {
        inputs: args.inputs || {}
      });
      
      return {
        content: [
          {
            type: 'text',
            text: `🚀 Flujo ejecutado exitosamente.\nExecution ID: ${response.data.executionId || 'N/A'}\nEstado: ${response.data.status || 'iniciado'}`
          }
        ]
      };
  • The input schema for the execute_flow tool, defining required flowId and optional inputs object.
    name: 'execute_flow',
    description: 'Ejecuta un flujo de trabajo especĂ­fico',
    inputSchema: {
      type: 'object',
      properties: {
        flowId: {
          type: 'string',
          description: 'ID del flujo a ejecutar'
        },
        inputs: {
          type: 'object',
          description: 'Inputs opcionales para el flujo'
        }
      },
      required: ['flowId']
    }
  • src/index.js:219-220 (registration)
    The switch case in CallToolRequestHandler that dispatches to the executeFlow handler when 'execute_flow' is called.
    case 'execute_flow':
      return await this.executeFlow(args);
  • src/index.ts:229-230 (registration)
    The switch case (TypeScript) in CallToolRequestHandler that dispatches to the executeFlow handler.
    case 'execute_flow':
      return await this.executeFlow(args);
Behavior2/5

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 mentions execution but doesn't disclose behavioral traits like whether it's synchronous/asynchronous, if it requires specific permissions, what happens on failure, or if it's idempotent. For a mutation tool with zero annotation coverage, this is a significant gap in transparency.

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

Conciseness4/5

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

The description is a single, efficient sentence in Spanish, front-loaded with the core action. It's appropriately sized for the tool's purpose, with no wasted words, though it could be more informative without sacrificing brevity.

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 tool's complexity (executing workflows with inputs), lack of annotations, no output schema, and siblings indicating state management, the description is incomplete. It doesn't explain return values, error handling, or how it fits with other tools like 'get_flow_executions'. For a mutation tool, more context is needed.

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 ('flowId' and 'inputs'). The description adds no meaning beyond the schema, such as examples of inputs or how flowId is obtained. With high schema coverage, the baseline is 3, as the description doesn't compensate but doesn't detract either.

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?

The description 'Ejecuta un flujo de trabajo especĂ­fico' translates to 'Executes a specific workflow' which provides a clear verb ('execute') and resource ('workflow'), but it's vague about what execution entails and doesn't distinguish from siblings like 'pause_flow' or 'resume_flow'. It states what it does but lacks specificity about the execution behavior.

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?

No guidance is provided on when to use this tool versus alternatives. With siblings like 'pause_flow' and 'resume_flow', there's no indication of prerequisites, such as needing an existing flow from 'create_flow' or 'get_flow', or when execution is appropriate compared to pausing/resuming. The description offers no usage context.

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/punkpeye/hiveflow-mcp-server'

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