Skip to main content
Glama

generate_component

Create React Native components using the Native_MCP server by specifying the component name. Simplifies React Native development with a standardized interface for component generation.

Instructions

Generate a React Native component

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
component_nameYes

Implementation Reference

  • The core handler function for the 'generate_component' tool. It takes a 'component_name' parameter and generates a basic React Native functional component as a string.
    def generate_component(params):
        name = params.get("component_name", "MyComponent")
        code = f"""import React from 'react';\nimport {{ View, Text }} from 'react-native';\n\nconst {name} = () => (\n  <View>\n    <Text>{name} works!</Text>\n  </View>\n);\n\nexport default {name};\n"""
        return {"component": code} 
  • mcp_server.py:19-45 (registration)
    The load_tools method in ToolManager dynamically discovers tool directories under 'tools/', loads their mcp.json for metadata and entry_point, imports the module, and registers each command (like 'generate_component') by mapping command name to the function in self.commands.
    def load_tools(self):
        if not os.path.isdir(self.tools_dir):
            return
        for tool_name in os.listdir(self.tools_dir):
            tool_path = os.path.join(self.tools_dir, tool_name)
            if not os.path.isdir(tool_path):
                continue
            mcp_json_path = os.path.join(tool_path, 'mcp.json')
            if not os.path.isfile(mcp_json_path):
                continue
            with open(mcp_json_path, 'r', encoding='utf-8') as f:
                meta = json.load(f)
            entry_point = meta.get('entry_point')
            if not entry_point:
                continue
            entry_path = os.path.join(tool_path, entry_point)
            if not os.path.isfile(entry_path):
                continue
            # Dynamically import the tool's entry point
            spec = importlib.util.spec_from_file_location(f"{tool_name}_module", entry_path)
            module = importlib.util.module_from_spec(spec)
            spec.loader.exec_module(module)
            # Register commands
            for cmd, cmd_meta in meta.get('commands', {}).items():
                if hasattr(module, cmd):
                    self.commands[cmd] = (tool_name, getattr(module, cmd))
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. 'Generate' implies a creation or write operation, but it doesn't disclose behavioral traits like whether it requires specific permissions, what the output looks like, or any side effects. It adds minimal context beyond the basic action.

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 with no wasted words. It's appropriately sized and front-loaded, making it easy to scan and understand quickly.

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 complexity (a generation tool with no annotations, no output schema, and low schema coverage), the description is incomplete. It doesn't explain what the tool returns, how it behaves, or provide enough context for effective use, leaving significant gaps for an AI agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It mentions 'React Native component' but doesn't explain the 'component_name' parameter's semantics, such as naming conventions, format, or what it represents. The description adds little meaning beyond what the bare schema provides.

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 'Generate a React Native component' states a clear verb ('Generate') and resource ('React Native component'), but it's somewhat vague about what kind of component or what generation entails. It doesn't have siblings to distinguish from, but lacks specificity beyond the basic action.

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, such as prerequisites, alternatives, or specific contexts. It's a standalone tool with no siblings, but still offers no usage instructions or exclusions.

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

Related 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/ArkVex/Native_MCP'

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