Storacha MCP Storage Server

Storacha’s MCP Storage Server provides a secure, decentralized storage gateway that AI assistants can tap into via the Model Context Protocol. By exposing IPFS‑based hot storage through a standardized MCP interface, it removes the need for custom integration code and gives developers a plug‑and‑play solution for persisting, retrieving, and sharing data across agents and applications.

The server solves the perennial problem of data sovereignty in AI workflows. Traditional cloud storage often relies on single‑point providers, creating trust and compliance risks when handling sensitive documents or training data. Storacha’s implementation uses IPFS content identifiers (CIDs) and the Web3.Storage delegation model to guarantee that data remains tamper‑evident, verifiable, and accessible regardless of the underlying infrastructure. This is especially valuable for teams that must audit data lineage or satisfy regulatory requirements.

Key capabilities include:

• Uniform MCP API: Clients can perform , , and other operations with simple JSON payloads, abstracting away the complexities of IPFS pinning and Filecoin deals.
• Free tier for quick onboarding: GitHub users receive 100 MB of free storage, while email users can unlock up to 5 GB with a credit card—enabling rapid experimentation without upfront costs.
• Delegated access control: Using the tool, developers generate a private key and delegation that scopes permissions to specific actions (e.g., ), ensuring fine‑grained security.
• Multi‑mode transport: The server supports , Server‑Sent Events (SSE), and REST, giving clients flexibility to choose the most efficient channel for their environment.

Real‑world use cases span a broad spectrum. AI agents can store large training corpora, version them by CID, and share them across distributed workflows. LLMs can retrieve contextual documents on demand, reducing latency compared to pulling from external APIs. Web applications can back up state snapshots for disaster recovery, while machine‑learning pipelines can manage datasets that are too large for conventional storage. Because the data is immutable and content‑addressed, downstream systems can cache results with confidence that the underlying input has not changed.

Integrating Storacha into existing AI pipelines is straightforward. Once the MCP client is configured with the server’s address and the agent’s private key, any tool that understands MCP can invoke storage operations as if they were local file system calls. This seamless integration lowers the barrier to adopting decentralized storage, allowing developers to focus on model logic rather than infrastructure plumbing.