How to Use the Glean MCP in Pydantic AI

Get type-safe enterprise search for Pydantic AI with this managed MCP Server.

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Connect Glean MCP to Pydantic AI

Create your Vinkius account to connect Glean 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 Glean with Pydantic AI

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Search with type-safety in Pydantic AI

The `search_docs` tool returns responses that fit your Pydantic models perfectly. If the data is malformed, your agent stops before it hits the model. Use `autocomplete` to get validated suggestions while your agent builds its query. It ensures your inputs always match expected schemas.

Use this MCP Server for structured answers

Invoke `get_answer` to receive precise, validated knowledge from your internal docs. It keeps your agent's responses grounded in reality. `custom_request` allows you to send specific queries to your data stack. You define the structure, and the tool enforces it.

Monitor your knowledge base

Use `search_people` to retrieve contact details as objects. Your agent can then act on that data with full confidence in the type schema. `index_document` lets you push new data. Every operation is validated against your established models to prevent errors.

Setup guide

Set up Glean 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": {
        "glean-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Why Choose Vinkius

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Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

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Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Glean MCP in Pydantic AI

Every tool output is validated against your Pydantic schemas. If the data does not match, Pydantic AI throws a validation error immediately.

Yes, but it is better to map them into your models. This gives you strict control over how your agent processes the info.

It is. The server is hosted on the Vinkius platform, ensuring high uptime and consistent tool responses for your agent.

The validation layer catches it immediately. You avoid silent bugs or corrupted data flowing into your agent's logic.

The server handles your document strings and metadata through a strictly authenticated tunnel. It treats your sensitive internal records as private at all times.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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