How to Use the Figshare MCP in Pydantic AI

Q: How does Pydantic AI handle API schema changes on Figshare?

The framework validates all Figshare tool outputs, such as getarticle, against runtime Pydantic models. If the API schema changes, the agent fails loudly and immediately, preventing corrupted data from entering your database.

Q: Can I search public repositories using Pydantic AI and Figshare?

Yes, your agent uses searcharticles and listpubliccollections to find public research outputs. The framework automatically parses the JSON responses into type-safe Python objects for safe processing.

Q: How do I handle custom institutional fields in Figshare with Pydantic AI?

You run getcustomfields to retrieve your institution's metadata requirements. The agent then dynamically validates your draft updates against these fields before calling updatearticle.

Q: Can this Pydantic AI setup handle Figshare project collaboration?

The agent can use createproject and searchprojects to manage shared workspaces. Pydantic AI validates the project membership and security settings at runtime.

Manage Figshare research datasets with type-safe runtime validation using the Pydantic AI framework.

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Connect Figshare MCP to Pydantic AI

Create your Vinkius account to connect Figshare to Pydantic AI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Figshare with Pydantic AI

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Type-Safe Figshare Article Creation with Pydantic AI

The `create_private_article` tool registers a new draft in Figshare while enforcing strict schema validation at the Pydantic AI runtime level. This tool ensures that every Figshare field—from titles to custom categories—matches the expected Python types before making the API call. If your model generates malformed Figshare metadata, the Pydantic AI framework blocks the call and raises a validation error. This prevents corrupted or incomplete draft records from ever reaching your institutional Figshare repository.

Validate File Upload Workflows via MCP Server

The `initiate_file_upload` tool starts the multi-step Figshare file ingestion process by returning specific upload parameters to Pydantic AI. This tool provides the exact part sizes and upload tokens required for the transfer. Once the chunks are transmitted, the agent calls `complete_file_upload` to lock the Figshare file. If the Figshare API returns an unexpected status, your Pydantic AI application catches the type mismatch instantly, preventing silent upload failures.

Strict Figshare Metadata Harvesting and Verification

The `get_article` tool fetches complete Figshare metadata records for verification against your local Pydantic AI schemas. This tool validates the returned Figshare JSON structure, ensuring fields like authors, categories, and license types are fully populated. Your agent can run `list_article_files` and `get_file_details` to verify that all attached Figshare files match their expected sizes and hashes. This programmatic auditing via the Pydantic AI MCP Server integration ensures your archived Figshare research remains integral and compliant over time.

Setup guide

Set up Figshare MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "figshare-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Figshare tools.",
)

result = await agent.run("List recent Figshare transactions")
print(result.output)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Figshare. 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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Common questions about Figshare MCP in Pydantic AI

The framework validates all Figshare tool outputs, such as `get_article`, against runtime Pydantic models. If the API schema changes, the agent fails loudly and immediately, preventing corrupted data from entering your database.

Yes, your agent uses `search_articles` and `list_public_collections` to find public research outputs. The framework automatically parses the JSON responses into type-safe Python objects for safe processing.

You run `get_custom_fields` to retrieve your institution's metadata requirements. The agent then dynamically validates your draft updates against these fields before calling `update_article`.

The agent can use `create_project` and `search_projects` to manage shared workspaces. Pydantic AI validates the project membership and security settings at runtime.

Yes, all data transfers for private Figshare articles and files are encrypted in transit via TLS. The MCP server runs in an ephemeral isolate, ensuring your private research files and metadata are never exposed to external storage.

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