JSONBin.io MCP. Store, query, and structure JSON data from chat.
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JSONBin.io MCP Server lets your AI agent manage JSON data like a lightweight database. Create, read, and update structured data in private or public bins, and organize them into collections.
Use JSON Path expressions to query specific data points, or create XL bins for datasets up to 10MB. This is for developers needing a quick, cloud-based storage layer without setting up a full database.
What your AI agents can do
Add schema to collection
Adds a defined JSON schema to a collection, ensuring all bins added to it conform to the structure.
Count bin versions
Counts the total number of versions stored for a specific JSON bin.
Create access key
Generates a new, restricted access key for controlled API interaction.
Your agent fetches data from a JSON bin, optionally filtering the results using a specific JSON Path expression.
Your agent creates new JSON bins with required data, or updates existing bins and their metadata.
Your agent creates, lists, and manages logical groups of bins (collections) to structure project data.
Your agent defines and applies JSON schemas to collections, making sure any data written into the bins meets structural requirements.
Your agent creates and reads XL bins for complex datasets that exceed standard JSON limits (up to 10MB).
Your agent tracks and manages the version history of bins, allowing it to count versions or wipe the entire history.
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Supported MCP Clients
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JSONBin.io MCP Server: 27 Tools for Data Management
These tools let your AI agent perform every action needed to manage, query, and structure JSON data, from creating bins to managing schemas.
019e5d29add schema to collection
Adds a defined JSON schema to a collection, ensuring all bins added to it conform to the structure.
019e5d29count bin versions
Counts the total number of versions stored for a specific JSON bin.
019e5d29create access key
Generates a new, restricted access key for controlled API interaction.
019e5d29create bin
Creates a new JSON bin containing the data you provide.
019e5d29create collection
Creates a new, empty logical group to hold related JSON bins.
019e5d29create schema
Generates a new JSON schema definition document.
019e5d29create xl bin
Creates a large JSON bin (XL bin) capable of holding data up to 10MB.
019e5d29delete access key
Removes a specified access key, revoking its ability to access the bins.
019e5d29delete all bin versions
Wipes all historical versions and version metadata from a JSON bin.
019e5d29delete bin
Permanently deletes an entire JSON bin.
019e5d29delete xl bin
Permanently deletes a large JSON bin (XL bin).
019e5d29download usage logs
Downloads a record of server usage logs for a specific date range.
019e5d29fetch collection bins
Retrieves a list of all JSON bins contained within a specified collection.
019e5d29fetch uncategorized bins
Lists all JSON bins that haven't been assigned to any collection yet.
019e5d29list access keys
Lists every active access key associated with the account.
019e5d29list collections
Lists all existing collections you have created.
019e5d29list usage log dates
Provides a list of dates for which usage logs are available for download.
019e5d29read bin
Reads the content of a JSON bin, allowing optional filtering by JSON Path or fetching a specific version.
019e5d29read schema
Reads the content of an existing JSON schema document.
019e5d29read xl bin
Reads the content of a large JSON bin (XL bin).
019e5d29remove schema from collection
Removes a specific schema definition from a collection.
019e5d29update bin
Modifies the content or metadata of an existing JSON bin.
019e5d29update bin name
Changes the name displayed for a specific JSON bin.
019e5d29update bin privacy
Changes the privacy setting (public/private) of a JSON bin.
019e5d29update collection name
Changes the name of an existing collection.
019e5d29update schema
Modifies the content of an existing JSON schema document.
019e5d29update schema name
Changes the name displayed for a JSON schema document.
Choose How to Get Started
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Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
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Make Your AI Do More
Start with JSONBin.io, 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
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- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
What you can do with this MCP connector
You gotta connect JSONBin.io to your AI client to use it like a lightweight database. Need a spot to prototype data, store app configs, or just track state? This server gives your agent full control over cloud JSON storage.
Your agent can create a new JSON bin using create_bin with the data you give it, or it can make a massive one using create_xl_bin if your data hits the limit (up to 10MB). You can also group these bins into logical sections by calling create_collection, then list them all with list_collections, and grab the bins inside a group using fetch_collection_bins.
When it comes to data rules, your agent can generate a new structure definition using create_schema, then apply that to a collection with add_schema_to_collection, making sure everything put in meets the structural requirements. If the rules change, it can modify the schema with update_schema, or remove it using remove_schema_from_collection.
To get the data out, your agent reads the bin content using read_bin or read_xl_bin; it can even filter the results by using a specific JSON Path expression. You can also read the definition of a schema with read_schema. You can modify an existing bin's content or its metadata using update_bin or change the name with update_bin_name, and control who sees it by calling update_bin_privacy.
If you need to manage the structure, your agent can create a new logical group with create_collection, or list all existing groups with list_collections; it can also list every bin that hasn't been put into a collection yet using fetch_uncategorized_bins. When it comes to access, your agent can generate a restricted key using create_access_key, list all active keys with list_access_keys, or delete a key with delete_access_key.
For keeping track of data, your agent can count the total number of versions stored in a bin using count_bin_versions, or it can wipe out all history and version data with delete_all_bin_versions. You can also manage the underlying structures by creating a schema definition with create_schema, or modifying an existing one with update_schema and renaming it with update_schema_name.
If you wanna clean house, your agent can delete an entire bin using delete_bin or wipe out a large bin with delete_xl_bin. It can also delete a specific access key with delete_access_key.
Lastly, your agent handles usage records; it can list the dates available for logs with list_usage_log_dates, and download the actual server usage logs for a specific date range using download_usage_logs.
How JSONBin.io MCP Works
- 1 Subscribe to the JSONBin.io MCP Server and enter your Master Key and Access Key.
- 2 Tell your AI agent what data you need to store or query (e.g., 'Create a bin for app settings...').
- 3 The agent uses the appropriate tool (like
create_binorread_bin) to interact with the server and return the result.
The bottom line is: your agent talks to the server, and the server handles the data persistence and retrieval.
Who Is JSONBin.io MCP For?
Developers who need a quick, zero-setup place to store data mockups, API configurations, or app state. Data analysts who need to persist processed JSON results and query them directly from a chat window. Automation engineers who need to maintain complex state across multi-step AI workflows.
Uses create_bin or update_bin to store API mock data or temporary app configurations for testing.
Uses read_bin with JSON Path to pull specific metrics from a large JSON data dump without writing custom scripts.
Uses the collection tools (create_collection, list_collections) to organize and maintain environment-specific configuration files.
What Changes When You Connect
- Keep your work state persistent. Instead of relying on chat history, use
create_binandupdate_binto store application configurations or mock API responses, keeping your session data reliable. - Filter data on the fly. Don't read the whole JSON payload. Use
read_binwith JSON Path to pull out only the specific field you need, like a user ID or a single metric. - Structure massive projects. Use
create_collectionandadd_schema_to_collectionto group related bins and enforce that the data always follows a specific format. - Handle data volume spikes. If your datasets exceed standard limits, use
create_xl_binandread_xl_binfor files up to 10MB, preventing data loss. - Maintain a clean history. Track changes using
count_bin_versionsand know exactly when data was last updated. Need to start fresh?delete_all_bin_versionswipes the slate clean.
Real-World Use Cases
Debugging a broken workflow state
The agent fails halfway through a multi-step task. Instead of restarting, you tell it to read_bin using the last known bin ID. It pulls the current state, letting you figure out which step failed. You then use update_bin to correct the data and continue.
API Mocking for Frontend Devs
You need a fake API endpoint for a feature that isn't ready. You use create_bin to store the mock JSON payload. Your agent can then read_bin to feed that data to the frontend, letting development continue without backend delays.
Analyzing structured research data
You have a massive JSON dump of research results. You don't want to manually check every field. You use read_bin with a JSON Path query to pull only the 'confidence_score' for all entries, giving you a clean list for analysis.
Organizing client project data
You're juggling data for three different clients. You use create_collection for each client (e.g., 'Client X Config'). You then use add_schema_to_collection to enforce that every bin within that collection must contain a client ID and contact name.
The Tradeoffs
Treating the chat as persistent storage
Doing a complex task, leaving the chat, and coming back later to find the data gone. You rely on the chat memory, which is unreliable and loses context.
→
Always save the core data using create_bin. If you need to change it, use update_bin. This keeps the data in a structured bin, independent of the conversation history.
Over-engineering simple data needs
Starting a new project by building a full PostgreSQL database just to store a few configuration flags. That's overkill and adds overhead.
→
For simple config storage, use create_bin right away. It's faster, requires less setup, and gives you immediate JSON structure support.
Querying without knowing the structure
Asking the agent to 'get all user IDs' when the data might be nested deeply and structured differently. The query fails or returns garbage.
→
Before querying, define the structure using create_schema and add_schema_to_collection. Then, use read_bin with JSON Path for reliable data extraction.
Forgetting data lineage
Updating data without knowing if a previous version was correct, resulting in data corruption that's hard to trace.
→
Always check the history first. Use count_bin_versions to see how many changes exist, then use read_bin to view a specific version ID before you commit an update with update_bin.
When It Fits, When It Doesn't
Use this server if you need a persistent, schema-aware, and easily queryable JSON layer without running a full database. It's perfect for mocking API data, storing app settings, or maintaining session state in agent workflows. If your data structure is simple JSON, this is your tool. Don't use it if you need complex relational joins (like linking tables) or if your data needs to be read by specialized database tools (like a graph database). For those cases, stick with a dedicated SQL or NoSQL platform. If you just need to store a few random, unrelated files, consider a simple file storage service. When in doubt, use create_collection to group related bins first; it helps keep your project scope clean.
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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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 27 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Manually tracking app state across chat sessions is a pain.
Right now, if your agent does a multi-step task—say, generating a report and saving the settings—it dumps that data into the chat transcript. If the chat times out, or you start a new session, that data is lost. You're stuck copy-pasting everything into a local file just to keep the context.
With the JSONBin.io MCP Server, the agent doesn't dump data into the chat. It saves it permanently using `create_bin`. You can then retrieve it later with `read_bin`, keeping your work state stable and accessible, regardless of how many times you close and reopen the chat.
JSONBin.io MCP Server: Structured Data Persistence
You can't just write random JSON. You need to ensure that every bin contains the right fields. You'd have to manually define those rules and then check them against the data structure every single time.
Now, you use the server to define a schema with `create_schema` and enforce it with `add_schema_to_collection`. The server handles the validation. You get structured, reliable data every time, every time.
Common Questions About JSONBin.io MCP
How do I read only a specific field from a bin using read_bin? +
You use JSON Path filtering when calling read_bin. This lets you specify exactly which data point you want (e.g., $.user.id), even if the bin contains gigabytes of other junk.
Is JSONBin.io MCP Server good for massive datasets? +
Yes, it handles large files via XL Bins, supporting up to 10MB. For even bigger data, you'll need a dedicated data warehouse, but for complex datasets, XL Bins are usually enough.
What is the difference between create_bin and create_xl_bin? +
create_bin is for standard JSON payloads. Use create_xl_bin when the data volume exceeds the standard limits, giving you more space for complex files.
How do I make sure my data is consistent when I update it? +
Define a schema first using create_schema, then apply it using add_schema_to_collection. This enforces data integrity before you run update_bin.
How do I manage access keys using list_access_keys and create_access_key? +
You first use list_access_keys to see what keys are active. Then, you use create_access_key to generate a new, restricted key for your agent. This keeps your data secure and limits what your AI client can actually do.
What is the difference between update_bin and update_bin_privacy? +
The update_bin tool changes the actual JSON content inside a bin. The update_bin_privacy tool only changes whether the bin is public or private. You use the first one for data changes, and the second one for visibility.
Can I organize my data using create_collection and fetch_collection_bins? +
Yes, you group related bins using create_collection. After that, you use fetch_collection_bins to pull all the data from that specific group. This keeps your project structure clean and makes finding data much faster.
How do I delete or manage data versions using delete_all_bin_versions and delete_bin? +
Use delete_bin to completely remove a bin from your account. If you just want to wipe all the historical versions of a bin, run delete_all_bin_versions. You'll keep the bin structure but lose all the old data.
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.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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