Skip to main content
Glama
hiveflowai

HiveFlow MCP Server

Official
by hiveflowai

get_flow

Retrieve specific automation flow details from the HiveFlow platform by providing the flow ID. This tool enables AI assistants to access and manage workflow information through natural language commands.

Instructions

Obtiene detalles de un flujo especĂ­fico

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
flowIdYesID del flujo

Implementation Reference

  • The main handler function for the 'get_flow' tool. It fetches the specific flow details from the HiveFlow API using the provided flowId and returns a formatted text response with key flow information.
    async getFlow(args) {
      const response = await this.hiveflowClient.get(`/api/flows/${args.flowId}`);
      const flow = response.data.flow;
      
      return {
        content: [
          {
            type: 'text',
            text: `📊 Detalles del flujo "${flow.name}":\n• ID: ${flow._id}\n• Estado: ${flow.status || 'draft'}\n• Nodos: ${flow.nodes?.length || 0}\n• Descripción: ${flow.description || 'Sin descripción'}\n• Última actualización: ${flow.updatedAt || 'N/A'}`
          }
        ]
      };
    }
  • Input schema for the 'get_flow' tool, defining the required 'flowId' parameter as a string.
    inputSchema: {
      type: 'object',
      properties: {
        flowId: {
          type: 'string',
          description: 'ID del flujo'
        }
      },
      required: ['flowId']
  • src/index.js:90-102 (registration)
    Registration of the 'get_flow' tool in the tools list provided to the MCP server, including name, description, and input schema.
    {
      name: 'get_flow',
      description: 'Obtiene detalles de un flujo especĂ­fico',
      inputSchema: {
        type: 'object',
        properties: {
          flowId: {
            type: 'string',
            description: 'ID del flujo'
          }
        },
        required: ['flowId']
      }
  • src/index.js:218-219 (registration)
    Dispatch case in the main tool request handler that routes 'get_flow' calls to the getFlow method.
    case 'get_flow':
      return await this.getFlow(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 of behavioral disclosure. It states the tool 'gets details' but doesn't describe what those details include (e.g., metadata, status, configuration), whether it's a read-only operation, potential errors (e.g., invalid flowId), or response format. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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 front-loaded with the core action ('Obtiene detalles') and resource, making it easy to parse. Every part of the sentence earns its place by conveying essential information concisely.

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 (a read operation with one parameter), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what 'detalles' entail, potential outputs, or error conditions. For a tool that likely returns structured data about flows, more context is needed to help an agent use it effectively beyond just knowing it retrieves details.

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 schema description coverage is 100%, with the single parameter 'flowId' documented as 'ID del flujo'. The description adds no additional meaning beyond this, such as format examples (e.g., UUID) or where to obtain the ID. Given the high schema coverage, the baseline score of 3 is appropriate, as the schema adequately handles parameter semantics without extra description.

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 verb 'obtiene' (gets) and the resource 'detalles de un flujo especĂ­fico' (details of a specific flow), which directly communicates what the tool does. It distinguishes itself from siblings like list_flows (which lists multiple flows) by focusing on a single flow. However, it doesn't explicitly mention the resource type (e.g., workflow, data flow) beyond 'flujo', which keeps it from being fully specific.

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 (e.g., needing a flowId), exclusions, or comparisons to siblings like get_flow_executions (which might retrieve execution details) or list_flows (for browsing flows). Without such context, an agent must infer usage from the tool name and schema alone.

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/hiveflowai/hiveflow-mcp-server'

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