The REST-to-Postman MCP server converts REST API definitions into Postman collections and environments, enabling automatic synchronization between them.
Convert REST API endpoints to Postman collections
Create or update Postman environments with environment variables
Intelligently merge new endpoints with existing collections
Automatically handle sensitive environment variables as secrets
Support various authentication methods (e.g., Bearer token)
Integration with AI code editors like Claude Desktop, Cursor, Windsurf, and Roo Cline Editor
Support for both stdio and SSE transport modes
Development mode for local testing and customization
Converts FastAPI endpoints to Postman collections, enabling automatic synchronization of API definitions with Postman for documentation and testing.
Extracts Flask endpoints and converts them to Postman collections, allowing developers to automatically keep their Postman workspace in sync with their Flask API code.
Parses NestJS controllers to extract REST API endpoints and convert them into Postman collections, maintaining authentication settings and endpoint structures.
Enables automatic conversion of REST API endpoints to Postman collections and syncs environment variables with Postman environments, supporting various authentication methods and intelligent merging of new endpoints with existing collections.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@REST-to-Postman MCP create a Postman collection from my NestJS controller with base URL https://api.example.com"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A Model Context Protocol (MCP) server that converts REST API code (e.g., NestJS controllers, FastAPI/Flask endpoints) to Postman collections and environments. This tool helps developers automatically sync their REST API endpoints and environment configurations with Postman.
Convert REST API endpoints to Postman collections
Sync environment variables with Postman environments
Support for various authentication methods (e.g., Bearer token)
Intelligent merging of new endpoints with existing collections
Automatic handling of sensitive environment variables
Support for both stdio and SSE transport modes
Related MCP server: Codebase Insights MCP Server
Bun v1.2.2 or later
Postman API Key
Postman Workspace ID
This is a Model Context Protocol (MCP) stdio server that requires access to your Postman workspace to create/update collections and environments.
To install REST-to-Postman MCP for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @runninghare/rest-to-postman-mcp --client claudenpxTo use the MCP server with npx:
npx -y rest-to-postman@latest --postman-api-key your_api_key --postman-workspace-id your_workspace_idOr use environment variables:
export POSTMAN_API_KEY=your_api_key
export POSTMAN_ACTIVE_WORKSPACE_ID=your_workspace_id
npx -y rest-to-postman@latest You can integrate this command with various AI code editors that support MCP:
Claude Desktop
Cursor
Windsurf
Roo Cline Editor
Important Note: The server requires Postman API credentials to function. Make sure you have both the API key and workspace ID ready before starting the server.
The server provides two main tools:
rest_to_postman_env)Creates or updates a Postman environment with your application's environment variables.
Input Parameters:
envName (string): Name of the Postman environment
envVars (object): Key-value pairs of environment variables
Example Input:
{
"envName": "REST Environment",
"envVars": {
"API_URL": "https://api.example.com",
"API_TOKEN": "secret-token-1"
}
}rest_to_postman_collection)Creates or updates a Postman collection with your REST API endpoints.
Input Parameters:
collectionRequest (object): Postman collection configuration containing:
info: Collection metadata
auth: Authentication settings
item: Array of API endpoints
Example Input:
{
"info": {
"name": "REST Collection",
"description": "REST Collection",
"schema": "https://schema.getpostman.com/json/collection/v2.1.0/collection.json"
},
"auth": {
"type": "bearer",
"bearer": [
{
"key": "Authorization",
"value": "Bearer {{API_TOKEN}}",
"type": "string"
}
]
},
"item": [
{
"name": "Get Users",
"request": {
"method": "GET",
"url": {
"raw": "{{API_URL}}/users",
"protocol": "https",
"host": ["api", "example", "com"],
"path": ["users"]
}
}
}
]
}Both tools return a success message confirming the creation/update of the Postman resource:
{
"content": [{
"type": "text",
"text": "Successfully created/updated Postman environment: REST Environment"
}]
}You can use this MCP server in Cursor. Here's an example which generates a Postman Collection based on a Nest.js Typescript controller.
Prompt:
Create a postman collection named "Campaign Endpoints" based on this next.js controller. The baseUrl is `http://localhost:7022`. The collection should have a Bear token which applies to all the endpointsHere's the automatically generated Postman Collection:
Note all the endpoints from the Campaign Controller are generated, along with the Bear token setting.
Clone the repository:
git clone https://github.com/runninghare/rest-to-postman.git
cd rest-to-postmanInstall dependencies:
bun installCreate a .env file:
POSTMAN_API_KEY=your_api_key_here
POSTMAN_ACTIVE_WORKSPACE_ID=your_workspace_id_hereFor development, you can run the server directly using Bun:
# Start in stdio mode (default)
bun run src/mcp.ts
# Start in SSE mode
bun run src/mcp.ts --sseTo build the project:
bun run buildThis will create a bundled output in the dist directory.
bun run build - Build the project
bun run dev - Run the server in development mode
bun run startSSE - Start the server in SSE mode
Contributions are welcome! Please feel free to submit a Pull Request.
This project is licensed under the MIT License - see the LICENSE file for details.
Creates or updates a Postman collection with the provided collection configuration. This tool helps synchronize your REST API endpoints with Postman. When updating an existing collection, it intelligently merges the new endpoints with existing ones, avoiding duplicates while preserving custom modifications made in Postman. Here's an example:
{
"info": {
"name": "REST Collection",
"description": "REST Collection",
"schema": "https://schema.getpostman.com/json/collection/v2.1.0/collection.json"
},
"auth": {
"type": "bearer",
"bearer": [
{
"key": "Authorization",
"value": "Bearer {{API_TOKEN}}",
"type": "string"
}
]
},
"item": [
{
"name": "Get Users",
"request": {
"method": "GET",
"url": {
"raw": "{{API_URL}}/users",
"protocol": "https",
"host": ["api", "example", "com"],
"path": ["users"]
}
}
},
{
"name": "Create User",
"request": {
"method": "POST",
"url": {
"raw": "{{API_URL}}/users"
},
"body": {
"mode": "raw",
"raw": "{"name":"John Doe","email":"john.doe@example.com"}"
}
}
}
]
}
| Name | Required | Description | Default |
|---|---|---|---|
| collectionRequest | Yes | The Postman collection configuration containing info, items, and other collection details |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It describes key behaviors: it can create or update collections, intelligently merges endpoints to avoid duplicates, and preserves custom modifications. However, it lacks details on permissions, error handling, or rate limits, which are important for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose but includes a lengthy example that may not be necessary for understanding the tool's function. While the example is helpful, it makes the description less concise, and some details could be moved to documentation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (1 parameter with nested objects, no annotations, no output schema), the description is moderately complete. It covers the tool's purpose and behavior but lacks information on return values, error cases, or prerequisites, which are important for a tool that mutates data.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, so the baseline is 3. The description adds minimal parameter semantics by mentioning 'collection configuration' and providing an example, but it does not explain parameter constraints or usage beyond what the schema already documents.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Creates or updates a Postman collection with the provided collection configuration.' It specifies the verb ('creates or updates'), the resource ('Postman collection'), and distinguishes it from sibling tools by mentioning synchronization with REST API endpoints, unlike the sibling 'rest_to_postman_env' which likely handles environments.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage: 'This tool helps synchronize your REST API endpoints with Postman.' It explains when to use it (for creating or updating collections) and hints at the update behavior (intelligent merging). However, it does not explicitly state when not to use it or name specific alternatives beyond the sibling tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Creates or updates a Postman environment with the provided environment variables. This tool helps synchronize your REST application's environment configuration with Postman. It supports both creating new environments and updating existing ones in your Postman workspace. Environment variables related to sensitive data (containing 'token' in their names) are automatically marked as secrets. Here's an example:
{ "envName": "REST Environment", "envVars": { "API_URL": "https://api.example.com", "API_TOKEN": "secret-token-1" } }
| Name | Required | Description | Default |
|---|---|---|---|
| envName | Yes | The name of the Postman environment to create or update | |
| envVars | Yes | A record of environment variables to be added to the Postman environment. Format: { [key: string]: string } |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals important behavioral traits: support for both create and update operations, automatic secret marking for variables containing 'token', and workspace context. However, it doesn't disclose authentication requirements, rate limits, error handling, or whether the operation is idempotent, leaving significant gaps for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded with the core functionality in the first sentence. The example is helpful but could be more concise. The text is well-structured with clear sentences, though the example takes up significant space relative to the explanatory content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with no annotations and no output schema, the description provides adequate basic information about what the tool does and includes a helpful example. However, it lacks important contextual details: no information about return values, error conditions, authentication requirements, or workspace selection logic. The example helps but doesn't compensate for these missing elements.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents both parameters. The description adds minimal value beyond the schema: it provides an example showing the expected JSON structure and mentions the secret-marking behavior for 'token' variables, but doesn't explain parameter semantics beyond what's in the schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
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 ('creates or updates') and resource ('Postman environment with environment variables'). It distinguishes from the sibling tool 'rest_to_postman_collection' by focusing on environments rather than collections, providing clear differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context ('helps synchronize your REST application's environment configuration with Postman') but doesn't explicitly state when to use this tool versus alternatives. No guidance is provided on prerequisites, error conditions, or specific scenarios where this tool is preferred over manual configuration or other tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
rest_to_postman_collectionrest_to_postman_envThe two tools have completely distinct purposes: one handles Postman collections (API endpoint configurations), while the other handles Postman environments (environment variables). There is no overlap in functionality, making it impossible to confuse them.
Both tools follow a perfect 'rest_to_postman_' prefix pattern with descriptive suffixes ('collection' and 'env'). The naming is completely consistent in style and structure throughout the set.
With only 2 tools, this server feels severely under-scoped for a REST-to-Postman integration purpose. A complete synchronization system would typically need tools for operations like listing collections/environments, deleting resources, or handling authentication flows, not just create/update operations.
The toolset is significantly incomplete for REST-to-Postman synchronization. While create/update operations exist for collections and environments, there are no tools for reading existing resources, deleting them, managing workspaces, or handling more complex Postman features like monitors or mocks. This creates dead ends for agents trying to perform full lifecycle management.
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