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hiveflowai

HiveFlow MCP Server

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by hiveflowai

create_flow

Create new automation workflows in HiveFlow by specifying name, description, and optional nodes for AI-assisted process automation.

Instructions

Crea un nuevo flujo de trabajo en HiveFlow

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesNombre del flujo
descriptionYesDescripción del flujo
nodesNoNodos del flujo (opcional)

Implementation Reference

  • The main handler function that implements the create_flow tool. It sends a POST request to the HiveFlow API to create a new workflow with the provided name, description, and optional nodes, then returns a success message with the created flow's ID and status.
    async createFlow(args) {
      const response = await this.hiveflowClient.post('/api/flows', {
        name: args.name,
        description: args.description,
        nodes: args.nodes || [],
        edges: [],
        status: 'draft'
      });
      
      const flow = response.data.data;
      
      return {
        content: [
          {
            type: 'text',
            text: `✅ Flujo "${args.name}" creado exitosamente.\nID: ${flow._id}\nEstado: ${flow.status}`
          }
        ]
      };
    }
  • Input schema definition for the create_flow tool, specifying the expected arguments: required 'name' and 'description' strings, and optional 'nodes' array.
    inputSchema: {
      type: 'object',
      properties: {
        name: {
          type: 'string',
          description: 'Nombre del flujo'
        },
        description: {
          type: 'string',
          description: 'Descripción del flujo'
        },
        nodes: {
          type: 'array',
          description: 'Nodos del flujo (opcional)',
          items: { type: 'object' }
        }
      },
      required: ['name', 'description']
    }
  • src/index.js:48-70 (registration)
    Registers the 'create_flow' tool in the ListToolsRequestSchema response, including its name, description, and input schema.
    {
      name: 'create_flow',
      description: 'Crea un nuevo flujo de trabajo en HiveFlow',
      inputSchema: {
        type: 'object',
        properties: {
          name: {
            type: 'string',
            description: 'Nombre del flujo'
          },
          description: {
            type: 'string',
            description: 'Descripción del flujo'
          },
          nodes: {
            type: 'array',
            description: 'Nodos del flujo (opcional)',
            items: { type: 'object' }
          }
        },
        required: ['name', 'description']
      }
    },
  • src/index.js:214-215 (registration)
    In the CallToolRequestSchema handler, dispatches calls to 'create_flow' by invoking the createFlow method.
    case 'create_flow':
      return await this.createFlow(args);
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. While 'Crea' implies a write operation, it doesn't specify permissions needed, whether creation is idempotent, error conditions, or what happens on success. This is inadequate for a mutation tool with zero annotation coverage.

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 that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded with the core action.

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 creation tool with no annotations and no output schema, the description is insufficient. It doesn't explain what constitutes a valid flow, what the response looks like, or error handling. Given the complexity of creating workflow objects, 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 all parameters are documented in the schema. The description adds no additional parameter semantics beyond what's already in the schema, meeting the baseline score when schema does the heavy lifting.

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 ('Crea un nuevo flujo de trabajo') and resource ('en HiveFlow'), making the purpose immediately understandable. However, it doesn't distinguish this tool from sibling tools like 'execute_flow' or 'pause_flow' beyond the creation aspect, which prevents 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. It doesn't mention prerequisites, when not to use it, or how it relates to sibling tools like 'list_flows' or 'get_flow', leaving the agent to infer usage context.

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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