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Pinata IPFS

Organization
pinatacloud

Integrates with Pinata's IPFS storage services for uploading, searching, organizing, and retrieving files on both public and private decentralized networks using secure API authentication.

Publisherpinatacloud
Repositorypinata-mcp
LanguageJavaScript
Forks
5
Stars
6
Available tools
0
Transport typestdio
Categories
LicenseMIT
Links
  • Connect tools to AI workflows

    Pinata IPFS exposes MCP capabilities that can be used by compatible AI clients and agents.

  • 0 available tools

    Browse the callable actions below, including names and descriptions when provided by the server.

  • Ready-to-copy setup

    Use the installation snippets to configure this server in your preferred MCP client.

  • Open source signals

    6 stars and 5 forks from the linked repository.

pinata-mcp

A Model Context Protocol (MCP) server that provides Claude with access to Pinata. This integration allows Claude to interact with Public and Private IPFS through Pinata's API.

Setup

Prerequisites

  • Node.js 18+ installed
  • A Pinata account with an API key (JWT)
  • A Pinata Gateway URL

Installation

Installation will depend on whether you are using Claude Code or Claude Desktop.

Claude Code

Run claude mcp add and follow the prompts with the following information:

Server Name: pinata
Server Scope: Project or Global
Server Command: npx
Command Arguments: pinata-mcp /path/to/allowed/directory
Environment Variables: PINATA_JWT=<YOUR_JWT>,GATEWAY_URL=example.mypinata.cloud

Claude Desktop

Add the following config to claude_desktop_config.json:

json
{
  "mcpServers": {
    "pinata": {
      "command": "npx",
      "args": [
        "pinata-mcp",
        "/path/to/allowed/directory",
        "/another/allowed/directory"
      ],
      "env": {
        "PINATA_JWT": "<YOUR_JWT>",
        "GATEWAY_URL": "example.mypinata.cloud"
      }
    }
  }
}

Note: The directory arguments are optional. If not provided, the server will only allow access to the current working directory. You can specify multiple directories to allow file access from multiple locations.

Available Tools

Authentication

ToolDescription
testAuthenticationVerify that your Pinata JWT is valid and working

File Operations

ToolDescription
uploadFileUpload a file to Pinata (public or private IPFS)
searchFilesSearch files by name, CID, or MIME type
getFileByIdGet detailed file information by ID
updateFileUpdate file metadata (name, key-values)
deleteFileDelete a file from Pinata

Content Access

ToolDescription
createLinkCreate a gateway link for public or private files
createPrivateDownloadLinkGenerate a temporary download link for private files
fetchFromGatewayFetch content from IPFS via Pinata gateway

Group Operations

ToolDescription
listGroupsList groups with optional filtering
createGroupCreate a new group for organizing files
getGroupGet group details by ID
updateGroupUpdate group information
deleteGroupDelete a group
addFileToGroupAdd a file to a group
removeFileFromGroupRemove a file from a group

x402 Payment Instructions

Tools for content monetization using the x402 protocol:

ToolDescription
createPaymentInstructionCreate payment requirements for gated content
listPaymentInstructionsList/filter existing payment instructions
getPaymentInstructionGet details of a specific payment instruction
updatePaymentInstructionModify payment instruction settings
deletePaymentInstructionRemove a payment instruction
listPaymentInstructionCidsList CIDs associated with a payment instruction
addCidToPaymentInstructionAssociate a CID with a payment instruction
removeCidFromPaymentInstructionRemove a CID association

CID Signatures

Tools for cryptographic content verification using EIP-712 signatures:

ToolDescription
addSignatureAdd a cryptographic signature to a CID
getSignatureGet signature details by CID
deleteSignatureRemove a signature

Signed Upload URLs

ToolDescription
createSignedUploadUrlCreate a presigned URL for client-side uploads

Pin by CID

ToolDescription
pinByCidPin an existing CID from the IPFS network
queryPinRequestsQuery the status of pin requests
cancelPinRequestCancel a pending pin request

Vectorize (AI/Semantic Search)

ToolDescription
vectorizeFileVectorize a file for semantic search
deleteFileVectorsDelete vectors for a file
queryVectorsQuery vectorized files using semantic search

Utilities

ToolDescription
listAllowedDirectoriesList directories the server can access for file operations

Local Development

To test the MCP server locally during development:

1. Clone and install dependencies

bash
git clone https://github.com/PinataCloud/pinata-mcp.git
cd pinata-mcp
npm install

2. Build the project

bash
npm run build

3. Set up environment variables

Create a .env file in the project root (optional, you can also pass these in the MCP config):

PINATA_JWT=your_pinata_jwt_here
GATEWAY_URL=your-gateway.mypinata.cloud

4. Configure Claude to use the local build

Claude Code

Run claude mcp add with the local path:

Server Name: pinata-dev
Server Scope: Project
Server Command: node
Command Arguments: /path/to/pinata-mcp/dist/index.js /path/to/allowed/directory
Environment Variables: PINATA_JWT=<YOUR_JWT>,GATEWAY_URL=example.mypinata.cloud

Claude Desktop

Update claude_desktop_config.json to point to your local build:

json
{
  "mcpServers": {
    "pinata-dev": {
      "command": "node",
      "args": [
        "/path/to/pinata-mcp/dist/index.js",
        "/path/to/allowed/directory"
      ],
      "env": {
        "PINATA_JWT": "<YOUR_JWT>",
        "GATEWAY_URL": "example.mypinata.cloud"
      }
    }
  }
}

5. Testing changes

After making code changes:

  1. Rebuild: npm run build
  2. Restart Claude Code or Claude Desktop to pick up the changes

Testing with MCP Inspector

Use the MCP Inspector to test the server:

Web UI (interactive debugging)

bash
npx @modelcontextprotocol/inspector \
  -e PINATA_JWT=your_jwt \
  -e GATEWAY_URL=your-gateway.mypinata.cloud \
  -- node dist/index.js

This opens a browser UI where you can interactively list tools, call them with parameters, and inspect responses.

CLI mode (for scripting/CI)

bash
# List all available tools
npx @modelcontextprotocol/inspector --cli --method tools/list \
  -e PINATA_JWT=your_jwt \
  -e GATEWAY_URL=your-gateway.mypinata.cloud \
  -- node dist/index.js

# Call a specific tool
npx @modelcontextprotocol/inspector --cli --method tools/call \
  --tool-name testAuthentication \
  -e PINATA_JWT=your_jwt \
  -e GATEWAY_URL=your-gateway.mypinata.cloud \
  -- node dist/index.js

Example Prompts for Claude

Test my Pinata connection:
"Test my Pinata authentication to make sure everything is working"

Upload an image to Pinata:
"Upload this image to my Pinata account as a private file named 'My Example Image'"

Search for files:
"Search my Pinata account for all PNG files"

Create a group and add files:
"Create a new group called 'Project Assets' on Pinata, then find all my JSON files and add them to this group"

Fetch content from IPFS:
"Fetch the content with CID QmX... from IPFS"

Create a payment instruction for content monetization:
"Create a payment instruction called 'Premium Content' that requires 0.01 USDC on Base to access"

Pin an existing CID:
"Pin the CID bafkreih5aznjvttude6c3wbvqeebb6rlx5wkbzyppv7garjiubll2ceym4 to my account"

Vectorize files for AI search:
"Vectorize the file with ID abc123 so I can search it semantically"

Query vectorized content:
"Search my vectorized files in group xyz for 'machine learning concepts'"

Installation

TypingMind
Prerequisites:

Node.js 18+

{
  "mcpServers": {
    "pinata": {
      "command": "npx",
      "args": [
        "pinata-mcp"
      ],
      "env": {
        "PINATA_JWT": "<YOUR_JWT>",
        "GATEWAY_URL": "example.mypinata.cloud"
      }
    }
  }
}

Use Pinata IPFS MCP with multiple AI models

TypingMind connects MCP tools at the workspace level, so once Pinata IPFS is connected, you can use it with different AI models in TypingMind instead of setting it up separately for each model. This MCP runs locally through the TypingMind MCP connector on your device.

Setup guide to use the local connector

Use this when the MCP server needs access to local files, apps, or private resources on your computer.

1

Open the MCP settings

In TypingMind, go to Settings, Advanced Settings, then Model Context Protocol and choose Setup Connector.

  1. Open TypingMind in your browser.
  2. Click the Settings icon.
  3. Go to Advanced Settings.
  4. Open the Model Context Protocol section.
  5. Click Setup Connector and choose This Device.
TypingMind MCP connector setup screen with This Device selected
2

Run the connector command

Choose This Device, copy the command from TypingMind, and run it in Terminal. Keep the process running while you use MCP.

  1. Copy the setup command shown by TypingMind.
  2. Open Terminal on macOS or Windows Terminal on Windows.
  3. Paste and run the command.
  4. Approve the package install if Terminal asks you to proceed.
  5. Keep the Terminal window running while using MCP tools.
3

Add Pinata IPFS as a server

When the connector status is Ready, click Edit Servers and paste the MCP server configuration.

  1. Wait until the connector status shows Ready.
  2. Click Edit Servers.
  3. Paste the Pinata IPFS MCP server configuration.
  4. Save the server list.
  5. Refresh if you want to confirm the connector is still ready.
TypingMind MCP settings showing active server and Edit Servers button
{
  "mcpServers": {
    "pinata-ipfs": {
      "command": "npx",
      "args": [
        "-y",
        "pinata-mcp"
      ]
    }
  }
}
4

Use it across models

Save the server list, open Plugins, enable the Pinata IPFS MCP tools, then select any supported AI model in TypingMind and use the tools in chat or assign them to an AI agent.

  1. Open the Plugins page in TypingMind.
  2. Enable the Pinata IPFS MCP tools.
  3. Start a chat and choose the AI model you want to use.
  4. Use the MCP tools in chat or assign them to an AI agent.
  5. Switch to another AI model whenever needed without reconnecting MCP.
TypingMind chat using enabled MCP tools with a selected AI model
Can you use Pinata IPFS to help me with this task?
Pinata IPFS
Sure. I read it.
Here is what I found using Pinata IPFS.

Frequently asked questions

What is the Pinata IPFS MCP server used for?

Pinata IPFS is an MCP server that lets compatible AI clients connect to external tools and context. In TypingMind, you can add this MCP server once and make its tools available in your AI workspace.

Can I use Pinata IPFS MCP with multiple AI models in TypingMind?

Yes. TypingMind connects MCP tools at the workspace level, so you can use Pinata IPFS with different AI models such as Claude, ChatGPT, Gemini, or other models you have configured in TypingMind without setting up the MCP server separately for each model.

Why use Pinata IPFS MCP with TypingMind?

TypingMind is one of the best frontends for LLM chat because it brings multiple AI models, prompts, plugins, AI agents, API keys, and MCP tools into one workspace. With Pinata IPFS connected, you can use its MCP tools across your preferred models while keeping your chat workflow organized in TypingMind.

How do I connect Pinata IPFS MCP to TypingMind?

Pinata IPFS runs through the TypingMind local MCP connector. This is best when the MCP server needs access to local files, desktop apps, command-line tools, or private resources on your computer.

What tools does Pinata IPFS MCP provide in TypingMind?

Pinata IPFS exposes MCP capabilities that can be enabled from the TypingMind Plugins page and used in chat or assigned to AI agents.

Do I need to share my API keys with TypingMind to use Pinata IPFS MCP?

No. TypingMind is local-first and lets you keep your model providers, API keys, prompts, and MCP configuration under your control. If Pinata IPFS requires authentication, add the required headers, OAuth settings, or local configuration for that MCP server when you create the connection.

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