JSONPlaceholder MCP for AI. Test complex agent logic with mock data.
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JSONPlaceholder provides a reliable mock REST API for simulating data interactions. It lets your AI agent perform full CRUD operations—creating, reading, updating, and deleting posts, comments, albums, photos, and todos.
Perfect for developers prototyping complex logic or testing client integrations without needing to set up a live backend.
What your AI can do
Create post
Generates a new fake post record when called by the agent.
Delete post
Simulates deleting an existing post by its ID.
Get album photos
Retrieves all photo records associated with a given album ID.
Execute full CRUD operations on posts, allowing your agent to draft, save, modify, and delete content records.
Retrieve user-specific resources like albums or posts using tools such as get_user_albums to map out connections between different data types.
Fetch and manage comment records, linking specific comments (get_comment) back to their parent post for context.
Query albums and photos using tools like list_albums or get_album_photos to simulate a media library workflow.
Check the status of tasks by listing todos via list_todos, ensuring your agent can handle different completion statuses.
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JSONPlaceholder: 21 Tools Available
These tools let you manage every aspect of content data—from listing all users to retrieving a single photo's metadata—all through structured calls.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using JSONPlaceholder on VinkiusCreate Post
Generates a new fake post record when called by the agent.
Delete Post
Simulates deleting an existing post by its ID.
Get Album Photos
Retrieves all photo records associated with a given album ID.
Get Album
Fetches the details for one specific album by its unique ID.
Get Comment
Looks up and returns all data for a single comment record using its ID.
Get Photo
Retrieves the details of a specific photo by ID.
Get Post Comments
Gathers all comments belonging to one specified post.
Get Post
Fetches the content and metadata for a single post by its ID.
Get Todo
Retrieves details for one specific to-do item using its ID.
Get User Albums
Gets all album records associated with a specified user's account.
Get User Posts
Retrieves posts written by a specific user ID.
Get User Todos
Gets all to-do items owned by a specified user.
Get User
Looks up and returns the profile details for one specific user ID.
List Albums
Lists all albums, with an option to filter them by a user's ID.
List Comments
Retrieves multiple comments, allowing filtering based on the parent post ID.
List Photos
Lists all photo records, with an option to filter them by album ID.
List Posts
Gets a list of posts, which can be filtered by the user who wrote them.
List Todos
Retrieves multiple to-do items, with an option to filter them by owner ID.
List Users
Returns a list of all user accounts available in the system.
Patch Post
Updates specific fields (partial update) of an existing post record.
Update Post
Replaces all content of a post, requiring the full set of data to modify it.
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
Claude AI
Open Claude Settings
Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.
Add Custom Connector
Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:
https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp
Replace [YOUR_TOKEN_HERE] with your token
from cloud.vinkius.com. For OAuth-protected servers, expand
Advanced settings to add credentials.
Start a conversation
Open a new chat. The JSONPlaceholder integration is available immediately — no restart needed.
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
Make Your AI Do More
Start with JSONPlaceholder, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,100+ 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
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by JSONPlaceholder. 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.
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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 connection provides 21 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Manually checking content relationships across different system tabs is tedious.
Right now, if your agent needs to build a profile page—say, showing everything related to User X—you're stuck. You have to manually jump between the 'Posts' tab, then open the 'Comments' section, and separately check the 'Media Gallery'. Every piece of information requires switching contexts and copy-pasting IDs just to make sure all the parts fit together.
With this MCP, your agent doesn't need to click around. It simply requests data by role. You ask for a user’s posts using `get_user_posts`, then follow up with comments using `list_comments` (filtered by postId). Your client gets all the necessary pieces of information back in one structured data exchange, ready for immediate use.
JSONPlaceholder provides predictable content structures.
The manual labor of testing CRUD operations—like first calling `get_post` to see the existing body, then having to manually edit it and finally running a separate call to `update_post` with all the new fields—wastes time. It's slow, error-prone, and requires managing multiple payloads.
Now, your agent handles the entire lifecycle automatically. You can simulate everything from drafting content using `create_post` to making small corrections via `patch_post`. The system guarantees the data structure you need for testing without any manual payload construction.
What your AI can actually do with this
This MCP connects your agent to JSONPlaceholder, the industry-standard fake REST API. You use this tool when you need to test out how your AI workflow handles structured data—like simulating content creation or verifying user relationships—without touching a real database. If you're building an agent that needs to process posts, fetch associated comments, and manage media metadata, this is what you want.
It lets your client run through full CRUD lifecycles; for instance, you can use create_post to draft content, then list_comments to see feedback, before simulating a final update with update_post. When developing complex agentic behaviors that require predictable data responses, Vinkius makes connecting this mock API simple. You get the full spectrum of testing tools—from listing users via list_users to tracking task completion status using list_todos—all in one place.
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Here's how it actually works
The bottom line is that you get predictable, structured mock API responses instantly, letting you test your agent's logic without any backend setup.
Subscribe to this MCP on Vinkius. No API keys are required for this public testing service, but you might provide a placeholder string if prompted.
Instruct your AI client to perform the desired data action (e.g., 'Get all posts for user 5').
The agent executes the tool call against JSONPlaceholder and receives structured mock data in response.
Who is this actually for?
Developers and AI engineers who spend time building agents but don't have a live database ready. You need to prove the logic works first. This MCP is for anyone whose job involves complex data flow, needing reliable mock endpoints.
Writing and testing agent workflows that rely on reading structured inputs (like post lists) and writing back outputs (like new posts).
Designing API integration layers; using this to validate how the client handles missing data, pagination, or differing response schemas.
Demonstrating a functional, multi-step AI feature prototype—for example, having an agent draft a post and then list all related photos—using safe mock data.
What Changes When You Connect
You can test the entire content creation flow. Use create_post to generate initial draft content and then use update_post or patch_post later in your workflow to simulate revisions, validating that your client handles both full replacements and partial field updates correctly.
Verify complex user relationships without needing real credentials. You can check who owns what by calling tools like get_user_posts or get_user_albums, ensuring your agent accurately links data across users and content types.
Simulate media handling realistically. By using list_photos (filtered by albumId) and then fetching specific photos with get_photo, you ensure your agent knows how to manage hierarchical resource calls, which is critical for modern applications.
Test state-based logic rigorously. Use list_todos or get_todo to confirm your agent correctly interprets data status (e.g., 'completed' vs. 'pending'), making sure the business rules are encoded right from the start.
It eliminates setup time. Instead of spending hours building a mock database, you subscribe and immediately begin testing complex data retrieval patterns using tools like list_comments or get_post_comments.
See it in action
Building a Content Review Agent
A product designer needs to show how an AI agent drafts content. The agent calls create_post, then uses list_photos and get_album_photos to pull in supporting media context, finally using patch_post to tag the draft for review.
Debugging User Profile Views
An engineer needs to verify a user profile page. They call get_user, then use list_posts and get_user_todos to pull in all related activity, ensuring the agent correctly aggregates multiple data sources for one view.
Simulating Comment Threading
Testing a comment section. The agent calls get_post first, then uses list_comments and get_comment to pull all existing replies, proving the client can build context around nested data.
The honest tradeoffs
Over-relying on a single list call
Attempting to get a user's entire content history by just calling list_posts without filtering. The resulting data set is too large and lacks critical relationship context.
To accurately build the view, first use get_user_posts (filtering by userId) to narrow down the posts, then follow up with get_post_comments for each post ID to get related comments. This structured approach keeps the data manageable.
Assuming full content creation
Calling create_post and expecting permanent storage of the generated content, leading to confusion when the mock API resets its state.
Remember this is a mock service. Use it for logic testing only. If you need data persistence, you have to point your client at a real backend.
Using `get_post` and then trying to update the user
Trying to change the author ID of a post by calling update_post after retrieving it with get_post. The tool only allows modification of content fields, not ownership.
If you need to simulate changing ownership or metadata outside of the post body, use dedicated tools like patch_post if that specific field is exposed, or acknowledge that the relationship must be handled by a separate system.
When It Fits, When It Doesn't
Use this MCP if your primary goal is testing how your agent handles structured data interactions—read/write cycles for content types (posts, photos, comments) and verifying resource ownership. For example, if you need to prove that calling get_user then using the resulting ID in list_posts works, use this. Don't use it if you are building a complex 'feed' aggregation endpoint; those require aggregating multiple endpoints (like listing posts AND photos) which is client logic, not an API call. If your need is for real-time data or high transaction volume, this mock service isn't enough; you need a live backend.
Questions you might have
How do I use JSONPlaceholder with my agent? +
You connect this MCP to your preferred AI client via Vinkius. Your agent simply calls the tools (like list_users or get_post) by name, and the connection handles the communication flow.
Can I delete a post using JSONPlaceholder? +
Yes, you use the delete_post tool. This simulates the action of removing content, allowing your agent to test its deletion logic without affecting any live data.
Does list_users give me enough info about a user? +
It gives basic user profile information. If you need detailed activity or associated posts, you must follow up by calling tools like get_user_posts or get_user_albums, passing the retrieved ID.
What if I need to update only one field of a post? +
Use the patch_post tool. This allows you to send only the specific fields that changed, which is cleaner and more accurate than calling update_post, which requires replacing the entire resource.
Does calling `get_user` require any form of API key or authentication? +
No, it doesn't. This MCP is designed as a public mock service for prototyping and testing. You can call tools like get_user immediately from your agent without needing to manage keys or credentials.
If I run many queries using `list_posts`, will the JSONPlaceholder MCP enforce rate limits? +
No, you won't encounter rate limits. Since this is a mock API, it’s built for stress testing and prototyping complex agent behaviors. You can make high volumes of calls to simulate heavy traffic safely.
How do I ensure precise data retrieval when using `list_photos`? +
You filter by passing specific IDs as parameters. To narrow down the results, you should include an albumId. This allows your agent to retrieve exactly the photos tied to a known album.
Is the data created using `create_post` saved anywhere permanently? +
No, it simulates success but doesn't save. The MCP is strictly for demonstrating CRUD lifecycles within your AI client; any posts you 'create' are temporary mock records.
Can I filter posts by a specific user? +
Yes! Use the list_posts tool with the userId parameter to retrieve only the posts created by that specific user ID.
Does creating or updating a post actually save the data? +
No. JSONPlaceholder is a fake API. Tools like create_post, update_post, and delete_post simulate the response as if the action succeeded, but the server state remains unchanged.
How do I find comments for a specific post? +
Use the list_comments tool and provide the postId. This will return all comments associated with that specific post ID.
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