JSONBin.io MCP for AI. Persistent, Queryable Storage for Structured Data.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
How this MCP server connects to your AI agent
JSONBin.io connects your AI agent to cloud-based storage for structured data. It lets you create, manage, and query complex JSON datasets—think of it as a persistent scratchpad for your AI workflow.
Need to store API mock results or maintain session state? This MCP handles everything from basic record creation to advanced version tracking using powerful JSON Path queries.
What AI agents can do with JSONBin.io Automation
Add schema to collection
Adds a defined JSON schema document to an existing collection so its contents can be validated.
Count bin versions
Returns the total number of versions stored for a specific JSON bin, useful for auditing data changes.
Create access key
Generates a new, restricted access key that limits what an agent can do with your bins.
You can create both collections (groupings) and individual JSON bins for structured storage.
Fetch the contents of a bin, optionally specifying a historical version or using JSON Path to pull out single fields.
Track every change made to your bins and count how many versions exist for auditing purposes.
Define reusable JSON schemas and apply them to collections before adding new records.
Create XL bins to store complex datasets up to 10MB that exceed standard limits.
Manage which parts of the data are visible by creating or deleting restricted access keys, and setting bin privacy levels.
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What AI agents can do with JSONBin.io: 27 Tools for Data Management
These tools let you handle the entire lifecycle of JSON data, from defining schemas to reading specific versions and managing collections.
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 JSONBin.io on VinkiusAdd Schema To Collection
Adds a defined JSON schema document to an existing collection so its contents can be validated.
Count Bin Versions
Returns the total number of versions stored for a specific JSON bin, useful for...
Create Access Key
Generates a new, restricted access key that limits what an agent can do with your...
Create Bin
Creates a brand new JSON bin containing the specific data you provide.
Create Collection
Establishes an organized container to hold multiple related bins together.
Create Schema
Creates a formal JSON schema document that defines the required structure for your data.
Create Xl Bin
Generates an XL bin, allowing you to store large datasets up to 10MB of complex JSON.
Delete Access Key
Removes a specific access key, immediately revoking its ability to interact with...
Delete All Bin Versions
Wipes out all historical versions associated with a given JSON bin.
Delete Bin
Permanently removes an entire JSON bin and all its contents.
Delete Xl Bin
Removes a large XL JSON bin from your account.
Download Usage Logs
Downloads usage activity logs for bins, restricted to a specific date range.
Fetch Collection Bins
Retrieves a list of all the individual JSON bins that belong within a specified collection.
Fetch Uncategorized Bins
Lists all JSON bins that haven't been assigned to any specific organizational...
List Access Keys
Shows a list of every access key currently active on your account.
List Collections
Retrieves the names and IDs of all collections you have created.
List Usage Log Dates
Returns a list of available dates for which usage logs can be downloaded.
Read Bin
Reads the data from an existing JSON bin, and lets you optionally filter it using JSON Path syntax.
Read Schema
Retrieves the content of a defined JSON schema document for review.
Read Xl Bin
Reads and downloads the contents of a large XL JSON bin.
Remove Schema From Collection
Deletes a specific schema definition that was previously linked to a collection.
Update Bin Name
Changes the visible name of a specific JSON bin without altering its contents.
Update Bin Privacy
Adjusts whether a particular bin is public or restricted to certain users.
Update Bin
Modifies the existing JSON data within an already created bin.
Update Collection Name
Renames an entire collection of related bins for better organization.
Update Schema Name
Changes the display name of a specific JSON schema document.
Update Schema
Modifies the structure or definition of an existing JSON schema document.
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 JSONBin.io 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 JSONBin.io, 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 JSONBin.io. 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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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
Built on the Model Context Protocol (MCP) for 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 27 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Dealing with temporary, forgotten mock data, Solved with Vinkius AI Gateway
Right now, if you're mocking up an API response for testing, or just need to save complex configuration settings while developing, where do you put that data? Usually, it lives in a bunch of local files—a mix of JSON, YAML, and text dumps. You end up having to copy those mock responses into the chat history, which gets messy, hard to track, and eventually gets overwritten.
With this MCP, the process changes completely. Instead of relying on ephemeral chat context, you use `create_bin` to save that entire JSON structure permanently. Your agent can then access it anytime via `read_bin`, keeping your workspace clean and ensuring the data is always available for testing.
Getting granular control with schema validation
The manual steps that go away are defining structure by hand. You don't have to manually tell your teammate, 'Hey, this JSON needs a `user_id` and it has to be an integer.' Instead, you define the rules once using `create_schema`. Then, when you use `add_schema_to_collection`, every new bin must comply with those exact rules.
The difference is control. You stop hoping your data looks right; you enforce that structure at the source. It's reliable.
What your AI can actually do with this
Look, sometimes your AI agent needs more than just the chat history; it needs reliable data storage. With this MCP, you turn your client into a genuine data management assistant. You can treat your AI workflow like a small application backend, storing configurations or processing results in persistent bins. Need to prototype an API that spits out JSON? Just create a bin and let your agent work with it.
The best part is the structure: you manage collections for organization, track every change using versioning, and even use advanced querying methods—like filtering data using JSON Path—to grab only what you need from massive datasets. Because Vinkius hosts this MCP, you connect once to your AI client and get access to all these powerful data structures.
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Here's how it actually works
The bottom line is: your AI client gains dedicated, persistent access to structured storage, letting it manage state outside of the chat window.
Subscribe to this MCP and provide your JSONBin.io Master Key and Access Key.
Your agent uses the tools to create a new collection or bin and populates it with structured JSON data.
The agent can then read that specific bin, querying fields using JSON Path or fetching an older version for review.
Who is this actually for?
This MCP is crucial for developers and data professionals whose work involves generating, testing, or maintaining complex JSON objects. If your current workflow requires passing configuration files, mock API responses, or multi-step results between different agent calls, you need this.
Using the MCP to store local app configurations or test API endpoints without spinning up a full database.
Persisting processed query results and running secondary analyses directly in the chat by querying specific fields using JSON Path.
Maintaining complex state across multi-stage agent workflows, ensuring that data from one step is available for retrieval in the next.
What Changes When You Connect
Stop losing data context. You can store complex API mock results using create_bin and retrieve them later via read_bin, keeping your entire workflow self-contained.
Gain audit control with bin versioning. Instead of guessing what the data looked like last week, use count_bin_versions to track every change made to a record.
Keep your project clean by grouping related assets. Use create_collection and then manage all associated bins using fetch_collection_bins, which is way better than dumping everything into one place.
Need to process huge files? Don't use standard bins; use create_xl_bin for up to 10MB of data. This handles complex datasets that would otherwise crash your agent session.
Refine your queries using JSON Path. When you read a bin, the read_bin tool lets your agent filter down to only one specific field, ignoring all the noise in the rest of the record.
See it in action
Testing API Mock Responses
You're building a feature that talks to an external service. Instead of writing those mock responses into temporary files, you use create_bin to store the JSON payload. Your agent then uses this MCP to ensure all subsequent logic runs against that controlled, persistent data source.
Analyzing Multi-Step Research
Your agent gathers several pieces of information over a week. Instead of having 10 different chat threads, you use list_collections to create 'Project Alpha,' then use multiple calls to create_bin within it. You can later query the whole project using JSON Path and retrieve only the dates or names you need.
Maintaining Agent State
You're running a long, multi-turn conversation that needs to remember user preferences (like dark mode setting: {"theme": "dark"}). You use create_bin to store this config and your agent uses the MCP to read it every time, ensuring consistency.
Data Integrity Check
Before running a process that needs strict data rules, you first define them using create_schema, then attach that schema via add_schema_to_collection. This forces the system to validate any new bin created later.
The honest tradeoffs
Storing unstructured text
Trying to dump a massive block of raw, unformatted text into a bin and expecting JSON Path to work on it.
This MCP is for structured data. If you just have plain text or logs, don't use read_bin. Instead, write the text into an appropriate field within your JSON structure when calling create_bin.
Over-relying on memory
Starting a new chat session and assuming the agent will remember the complex data structures from the previous conversation.
Always persist critical state. Use list_collections to find your existing project container, then use read_bin to explicitly pull the needed configuration before continuing work.
Ignoring access control
Letting any agent client read all bins without limits when they only need one specific data point.
Always manage permissions. Use create_access_key to generate a key, and then use that restricted key for the operation, limiting potential damage.
When It Fits, When It Doesn't
Use this MCP if your primary task is managing structured JSON data—like API payloads, configuration files, or database mockups. You need persistence but don't require complex cross-record join logic; it’s a key/value store for structured objects. Don't use it if you are building a relational application that needs strict foreign keys connecting different types of records (that requires a traditional SQL backend). If your data is mostly text or unstructured logs, save the MCP for when you can wrap that data into a JSON object first.
Questions you might have
How do I query specific fields using the JSONBin.io MCP? +
You use the read_bin tool and pass a JSON Path expression to filter your results. This lets you pull out only one field, like 'theme', without having to process the entire massive bin.
What is the difference between `create_bin` and `create_xl_bin`? +
create_bin holds standard JSON data. Use create_xl_bin when your dataset is huge—up to 10MB—and needs specialized handling for complex, large-scale files.
Can I rename a bin after creating it? +
Yep. You use the update_bin_name tool. This changes what the bin is called without touching any of the data inside it.
How do I delete all history in a specific bin? +
If you want to wipe out every single version and piece of data from a bin, use delete_all_bin_versions. This clears everything while keeping the bin container itself.
How do I secure data by creating a dedicated key using `create_access_key`? +
You generate a limited, scoped token that restricts exactly what the calling agent can do. This means your main master credentials aren't exposed when running workflows from external systems.
Before I write to a group of bins, how do I validate the format using `add_schema_to_collection`? +
You apply a formal JSON schema document that dictates required fields and data types for the entire collection. This forces all incoming data into a predictable structure, preventing runtime errors.
If I want to see all the JSON bins belonging to one project, what does `fetch_collection_bins` do? +
It retrieves a list of bin IDs and metadata associated with that specific collection name. This is much faster than listing every single individual bin across your entire account.
How can I check which clients accessed my stored JSON data using `download_usage_logs`? +
You generate a log file detailing usage metrics, including timestamps and actions taken. This is useful for compliance reviews or debugging unexpected activity.
Can I filter the data inside a bin without downloading the whole JSON? +
Yes. When using the read_bin tool, you can provide a json_path expression to filter the data on the server side and retrieve only the matching subset.
How do I store datasets larger than the standard bin limit? +
You should use the create_xl_bin tool. This is specifically designed for large JSON files (up to 10MB) and requires a paid JSONBin.io plan with Early Access enabled.
Is it possible to change a bin from public to private after creation? +
Absolutely. Use the update_bin_privacy tool with the target bin_id and set is_private to true or false as needed.
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