4,500+ servers built on MCP Fusion
Vinkius

Reqres MCP. Mock API Endpoints for Testing Data Flows

Claude Claude
ChatGPT ChatGPT
Cursor Cursor
Gemini Gemini
Windsurf Windsurf
VS Code VS Code
JetBrains JetBrains
Vercel Vercel
See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

Reqres MCP on Cursor AI Code Editor MCP Client Reqres MCP on Claude Desktop App MCP Integration Reqres MCP on OpenAI Agents SDK MCP Compatible Reqres MCP on Visual Studio Code MCP Extension Client Reqres MCP on GitHub Copilot AI Agent MCP Integration Reqres MCP on Google Gemini AI MCP Integration Reqres MCP on Lovable AI Development MCP Client Reqres MCP on Mistral AI Agents MCP Compatible Reqres MCP on Amazon AWS Bedrock MCP Support

Just plug in your AI agents and start using Vinkius.

Reqres gives your AI agent a sandbox API to run against. Stop building backends just for testing. This server lets you mock user creation, manage project records (CRUD), simulate logins, and pull real-world resource data—all without setting up a single database.

It's the industry standard for front-end prototyping and advanced agent development.

What your AI agents can do

Agent health

Checks the health and rate limit status for the Agent Sandbox.

Agent orders

Retrieves orders, including related data, from the Agent Sandbox.

Agent scenarios

Lists all available failure scenarios within the Agent Sandbox environment.

+ 22 more capabilities included
Manage User Data

Create, read, update, and delete demo users. You can fetch paginated lists of users or get specific details for one user.

Perform Project Record CRUD

Handle full data lifecycle management on custom project collections. This includes creating new records (create_record) and updating them partially (patch_record).

Simulate Authentication Flows

Run mock login, registration, and magic link requests that return valid tokens or user IDs for agent testing.

Validate External Resources

Fetch structured data from dummy resources, such as Pantone colors, to validate how your application handles external API payloads.

Supported MCP Clients

Claude Claude
ChatGPT ChatGPT
Cursor Cursor
Gemini Gemini
Windsurf Windsurf
VS Code VS Code
JetBrains JetBrains
Vercel Vercel
+ other MCP clients
Free for Subscribers

Waiting for input…

AI Agent

Reqres MCP Server: 25 Tools for API & Data Management

These tools let you execute common API tasks—from creating user accounts to updating complex project records—in a controlled sandbox environment.

agent019e5d4f

agent health

Checks the health and rate limit status for the Agent Sandbox.

agent019e5d4f

agent orders

Retrieves orders, including related data, from the Agent Sandbox.

agent019e5d4f

agent scenarios

Lists all available failure scenarios within the Agent Sandbox environment.

agent019e5d4f

agent users

Gets a paginated list of users, supporting cursor-based pagination in the Agent Sandbox.

create019e5d4f

create app record

Creates and saves a new user-scoped record within an application collection.

create019e5d4f

create record

Generates and saves a new record into a specified project collection.

create019e5d4f

create user

Simulates the creation of a demo user account, echoing the data back to confirm the operation.

delete019e5d4f

delete record

Removes an existing record from any defined project collection.

delete019e5d4f

delete user

Deletes a specified user profile in the Demo API environment.

get019e5d4f

get me

Retrieves information about the current application user running the tool call.

get019e5d4f

get record

Fetches data for one specific record from a project collection using its details.

get019e5d4f

get user

Gets the full profile of a single user by their unique ID in the Demo API.

list019e5d4f

list app records

Lists all records within an application collection that belong to the current user.

list019e5d4f

list colors

Retrieves a list of resource colors, such as Pantone data, from the Demo API.

list019e5d4f

list records

Fetches a paginated list of all records within a project collection.

list019e5d4f

list users

Lists multiple users in the Demo API, supporting pagination for large datasets.

login019e5d4f

login demo

Simulates user login using fixture credentials and returns an access token.

patch019e5d4f

patch record

Updates only specific fields of a record in a project collection (partial update).

patch019e5d4f

patch user

Allows for partial updates to a demo user's profile, confirming the changes.

register019e5d4f

register demo

Simulates new user registration using fixture credentials and returns an ID/token.

request019e5d4f

request magic link

Initiates a magic link request for a specified App User to complete login.

trigger019e5d4f

trigger scenario

Forces the Agent Sandbox into a specific failure state to test error handling.

update019e5d4f

update record

Replaces an entire record in a project collection with new data (full replacement).

update019e5d4f

update user

Updates all details of a demo user's profile, confirming the complete change.

verify019e5d4f

verify magic link

Validates a received magic link token and issues a session access token.

Choose How to Get Started

Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.

Build Your Own

Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.

  • Import from OpenAPI, Swagger, or YAML specs
  • Create Agent Skills with progressive disclosure
  • Deploy to edge with MCPFusion framework
  • Built in DLP, auth, and compliance on every call
  • Real time usage dashboard and cost metering
  • Publish to catalog or keep private
Start building

Make Your AI Do More

Start with Reqres, then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.

  • Use this MCP plus 4,700+ others, all in one place
  • Add new capabilities to your AI anytime you want
  • Every connection is secured and compliant automatically
  • Track usage and costs across all your servers
  • Works with Claude, ChatGPT, Cursor, and more
  • New servers added to the catalog every week

What you can do with this MCP connector

Reqres gives your agent a sandbox API it can run against. Stop building backends just for testing—you'll spend too much time on stuff that doesn't matter. This server lets you mock user creation, manage project records (CRUD), simulate logins, and pull real-world resource data without setting up a single database or worrying about actual persistence.

It’s the standard toolkit for front-end prototyping and advanced agent development.

User Management: You can run through full identity flows by simulating new registrations using register_demo or running a login via login_demo, both of which return an access token you need. If you're testing user linking, use request_magic_link to kick off the magic link process for an app user, and then verify that link with verify_magic_link to get the session token.

To check a specific person’s profile, call get_user with their unique ID; if you just need details on who's running the tool, use get_me. You can list users across the whole system using list_users, or grab a paginated list of them with agent_users. For single-handedly managing records, you've got create_user to simulate making a demo user account, and delete_user removes that specified profile from the Demo API environment.

To update a user’s details partially, call patch_user; if you need to change every bit of their info, use update_user.

Project Record Management: When it comes to data collections—your project records—you can handle the full lifecycle. You'll start by generating and saving a new record into any project collection using create_record. If you need to build out a user-specific record, use create_app_record which saves it within an application collection scoped to the current user.

To pull data for one specific item, call get_record with its details; otherwise, you can fetch all records belonging to the current user by using list_app_records. If you need a comprehensive list of all items in a project collection, list_records gives you paginated results, and you can get multiple users' data by calling agent_orders which retrieves orders along with related info.

To update an existing record, you have two options: use patch_record to change only specific fields (a partial update), or call update_record if you need to replace the entire record with new data (full replacement). When a record is done and needs clearing out, delete_record removes it from any defined project collection.

Flow Testing & Utilities: You can simulate system failures for robust testing by calling trigger_scenario, forcing the agent sandbox into a specific error state. To check if your agent's connection is stable, run agent_health; this verifies the health and rate limit status of the Agent Sandbox. If you need to know what failure states are available, agent_scenarios lists all available scenarios within the environment.

For validating external data types, like Pantone colors, list_colors retrieves a list of resource colors from the Demo API. When working with applications, you can see every record belonging to your current user by running list_app_records, and you can also check how many users are available using list_users. The system also supports checking if there's an issue when listing records through agent_orders.

It's basically a complete mock environment for testing everything from identity to data persistence.

How Reqres MCP Works

  1. 1 Subscribe to the Reqres server on Vinkius and obtain your API Key or Session Token (if required for advanced tools).
  2. 2 Tell your AI client (Claude, Cursor, etc.) exactly what data you need. For example: 'Use list_users to fetch all users.'
  3. 3 Your agent executes the tool call. The server returns structured mock JSON data that your application can immediately use.

The bottom line is this: You treat Reqres like a live API endpoint, letting your agent perform real database operations and user lookups without ever touching production infrastructure.

Who Is Reqres MCP For?

The QA engineer who needs to validate 50 different failure scenarios before the dev team finishes their backend. The frontend developer stuck on mock data that 'doesn't quite work.' Or the AI agent builder who needs a predictable, repeatable API surface area for testing complex tool chains.

Frontend Developer

Needs to quickly build and test UI components that depend on user or project data, mocking endpoints like get_user without waiting for the backend team.

QA Engineer

Runs automated tests against multiple state transitions (e.g., register -> login -> update) using tools like login_demo and patch_record to ensure data integrity.

AI Agent Developer

Requires a stable, predictable environment to teach their agent how to perform sequences of operations—like listing users (list_users) then fetching details (get_user).

What Changes When You Connect

  • Test Authentication: Use login_demo and register_demo to simulate user sign-ups and logins, getting valid tokens back every time. You don't need a real auth service running.
  • Validate Complex Workflows: Chain calls like fetching users (list_users) then updating one (patch_user) allows you to test multi-step data pipelines reliably for your agent.
  • Isolate Front-End Logic: The get_record and create_record tools let you focus purely on UI rendering and form validation. You don't worry if the actual database is available yet.
  • Control Failure States: Use trigger_scenario to force your agent or front end into known failure modes (e.g., rate limits, bad data). This makes testing robust.
  • Handle Resources: The list_colors tool gives you a repeatable source of external data (like Pantone) so you can test how your UI handles non-user/project content.

Real-World Use Cases

01

The New User Onboarding Flow

A user signs up. Instead of relying on a flaky backend, the agent uses register_demo to create the initial account. Next, it calls request_magic_link, and finally verify_magic_link to simulate the full sign-in sequence and get a working session token for subsequent actions.

02

Updating Project Scope

A project manager needs to change key details. The agent first runs get_record to pull existing data, then uses patch_record on the specific fields that changed (e.g., slug or status), and finally calls list_records to confirm the update worked across the whole collection.

03

Testing Data Integrity After Deletion

A QA engineer needs to test cascading delete logic. They first call get_user to find a target ID, then use delete_record on an associated project file, and finally verify the deletion by attempting another get_record which should now fail.

04

Validating User Permissions

The agent needs to check if a user can access specific data. It first runs list_users to get all IDs, then uses get_user on the target ID, and checks the returned structure against expected permission flags.

The Tradeoffs

Assuming state persists

The agent calls create_record, thinks the record is saved, but then fails to pass the correct resource ID when calling a subsequent tool.

Always chain your operations. Use the output from create_record (the new record's ID) as the required input for any follow-up read or update calls like get_record.

Ignoring pagination

The agent runs list_users once and assumes it retrieved every user on the platform, missing thousands of records.

When listing collections (list_users, list_records), check the response metadata for a next page cursor or offset. Use that cursor in your subsequent call to get all data.

Overwriting by accident

The agent uses update_record instead of patch_record, sending only one field change but accidentally wiping out every other piece of data the record used to have.

If you only want to change a single field (like status), use patch_record. Only use update_record when you intend to replace the entire document structure.

When It Fits, When It Doesn't

Use this server if your primary goal is API contract testing and data flow simulation. You need to validate that complex sequences of operations—read, write, update, authenticate—work correctly without needing a live backend environment. This is ideal for early-stage prototyping or QA automation where you must control the success/failure state (e.g., using trigger_scenario).

Don't use this if your goal is to store real data long-term, manage billing, or connect to actual third-party services like Slack or Stripe; those require dedicated integrations.

If you are building an agent that needs to perform many steps (e.g., read -> calculate -> write), the predictable nature of tools like get_user, followed by patch_record and ending with list_app_records, makes this the perfect sandbox.

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Reqres. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

VINKIUS INFRASTRUCTURE

Cloud Hosted

Managed infra

V8 Isolated

Sandboxed per request

Zero-Trust Proxy

No stored credentials

DLP Enforced

Policy on every call

GDPR Compliant

EU data residency

Token Compression

~60% cost reduction

How we secure it →

Works with Claude, ChatGPT, Cursor, and more

The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.

This server provides 25 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.

Available Capabilities

agent_health agent_orders agent_scenarios agent_users create_app_record create_record create_user delete_record delete_user get_me get_record get_user list_app_records list_colors list_records list_users login_demo patch_record patch_user register_demo request_magic_link trigger_scenario update_record update_user verify_magic_link

Writing reliable API clients usually means writing mountains of mock data logic.

Today, developers spend time writing boilerplate code just to simulate what happens when a user successfully logs in or when a record gets updated. We're talking about building manual state checks and passing hardcoded IDs around—all before the actual backend team even finishes their first commit.

With Reqres MCP Server, that effort vanishes. You simply tell your agent, 'Simulate a login.' The `login_demo` tool handles the complexity, giving you back a valid token instantly. Your client code just consumes the clean result; it doesn't care how the simulation worked.

The Reqres MCP Server: Test data mutations with tools like `patch_record`.

Manually updating a record often means pulling the whole thing, changing one field in your IDE, and then re-pushing the entire JSON object. If you miss a single key or accidentally overwrite another user's status flag, your test fails because of bad data handling.

Now, just use `patch_record`. You tell it exactly which field to change—like setting a project status to 'Review'—and nothing else gets touched. It keeps the record stable while letting you test focused mutations.

Common Questions About Reqres MCP

How do I list all users using the `list_users` tool? +

The list_users tool lists paginated user data from the Demo API. If your dataset is large, you'll need to check the response for pagination tokens and call the tool again until there are no more results.

Can I use `get_record` with my own custom project slugs? +

Yes, get_record fetches a single record from a specified project collection. You must provide the unique slug and other identifiers for it to find the correct data in the mock database.

`patch_user` is better than `update_user` for my agent? +

Yes, generally. patch_user only updates what you give it; update_user replaces all fields. Use patch_user when you want to change one detail (like an email) without risking data loss.

What if I need to test a failure condition? Should I use the `trigger_scenario` tool? +

Yes, that's the right way. Use trigger_scenario to force specific failures in the Agent Sandbox. This allows you to prove your agent handles errors gracefully instead of just assuming success.

How do I simulate a user logging in and getting a token using the `login_demo` tool? +

It returns a valid session token immediately. You use this token to authenticate subsequent calls, simulating a real user login flow. This is perfect for testing how your agent handles authenticated requests.

What information does the `agent_health` tool provide regarding rate limits? +

The agent_health endpoint gives you the current sandbox status and any active rate-limit quotas. This lets you write code that gracefully handles throttling errors without failing unexpectedly.

When should I use `list_app_records` instead of `list_records`? +

list_app_records fetches data scoped to the current user, mimicking a private app collection. Meanwhile, list_records pulls from a general project collection that isn't tied to specific user permissions.

Is `patch_record` safer than `update_record` when I only want to change a few fields? +

Yes, it is. Using patch_record allows you to send partial data—you only specify what needs changing. If you use update_record, you must provide the full record structure, risking overwriting necessary data.

How can I see the list of available demo users? +

You can use the list_users tool. It supports pagination, so you can specify which page you want to retrieve from the demo database.

Can I test authentication flows like login and registration? +

Yes! Use login_demo or register_demo with fixture emails (like 'eve.holt@reqres.in') to simulate successful authentication and receive a token.

How do I manage custom data for my specific project? +

Use the Project API tools like list_records and create_record. You just need to provide a slug to identify your collection and the JSON data payload.

More in this category

You might also like

Built & Managed by Vinkius 30s setup 25 tools

We've already built the connector for Reqres. Just plug in your AI agents and start using Vinkius.

No hosting. No infrastructure. No complex setup.
All 25 tools are live and waiting. You're up and running in seconds.

Claude Claude
ChatGPT ChatGPT
Cursor Cursor
Gemini Gemini
Windsurf Windsurf
VS Code VS Code
JetBrains JetBrains
Vercel Vercel
+ other MCP clients

Vinkius gives your AI agents access to the full catalog of app connectors, all fully managed, secure, and enterprise-ready. One subscription, every tool you need.

Zero hosting required Full MCP catalog included Enterprise-grade security Auto-updated by Vinkius

Built, hosted, and secured by Vinkius. You just connect and go.