How to Use the Celoxis MCP in Pydantic AI

Q: How do I initialize the Celoxis MCP Server in Pydantic AI?

Install pydantic-ai-slimmcp. Create an MCPToolset pointing to the Vinkius URL and pass it to your Agent's toolsets array.

Build type-safe project management agents by connecting the Celoxis MCP Server to Pydantic AI with strict runtime validation.

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Connect Celoxis MCP to Pydantic AI

Create your Vinkius account to connect Celoxis 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 Celoxis with Pydantic AI

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Perplexity

Validate Celoxis data with Pydantic AI

The `get_project` and `list_tasks` tools return complex intrinsic properties and Work Breakdown Structure deliverables. Your agent validates every single field against strict Python models before trusting the data. If the API returns a missing status field or an unexpected physical ID format, the system fails loudly. You get a clear validation error instead of silent corruption deep inside the logic loop.

Audit timesheets accurately

Your agent pulls actual logged hours using `list_time_entries` and cross-references them against pending tracking objects via `list_approvals`. It reads exact constraints over expenses and timesheets. Because the framework enforces strict typing, your agent never hallucinates a resource allocation. The system reads explicit user mappings from `list_resources` and guarantees the data matches the defined schema.

Catch financial anomalies via MCP Server

The `list_expenses` tool pulls raw billable and non-billable items physically mapped onto task items. Your agent processes these exact monetary values without guessing field types. It pairs financial data with custom app items representing blocked workflows pulled through `list_issues`. The model-agnostic setup means you can swap from Anthropic to a local model without rewriting your validation logic.

Setup guide

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

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

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

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Common questions about Celoxis MCP in Pydantic AI

Install pydantic-ai-slim[mcp]. Create an MCPToolset pointing to the Vinkius URL and pass it to your Agent's toolsets array.

The framework guarantees data shape. When your agent calls list_portfolios, the response must match your expected schema exactly, or the run terminates immediately.

Yes. The unified toolset approach supports Streamable HTTP and SSE transports out of the box. Your agent can stream large milestone lists natively.

Yes. The framework is entirely model-agnostic. You can run open-source models locally to parse deliverables while relying on the same rigid MCP tooling.

Every request routes through an ephemeral V8 sandbox requiring only one endpoint token. Your raw expense records, risk matrices, and unbilled timesheets are never stored on disk.

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