How do I query cycle time for a specific team?

Use the `query_software_metrics` tool and include the team name or ID in the `group_by` parameter of your JSON query.

What is the difference between coding_time and pickup_time?

Coding time is the duration from the first commit to the PR creation. Pickup time is the duration from the PR creation to the first review activity.

Can I report a release from the agent?

Absolutely. Use the `record_new_deployment` tool with the Git SHA or tag and the repository ID to inform LinearB that a deployment has occurred.

LinearB MCP Server for Pydantic AI 7 tools — connect in under 2 minutes

Name: LinearB
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 7 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect LinearB through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to LinearB "
            "(7 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in LinearB?"
    )
    print(result.data)

asyncio.run(main())

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About LinearB MCP Server

Connect your LinearB account to any AI agent to automate your engineering intelligence and DORA metrics reporting. This MCP server enables your agent to query cycle time, track deployments, and report incidents directly from natural language interfaces.

Pydantic AI validates every LinearB tool response against typed schemas, catching data inconsistencies at build time. Connect 7 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Metric Ingestion — Query complex engineering metrics including cycle time, coding time, and pickup time across teams
Deployment Management — Inform LinearB of new software releases by reporting Git refs (SHAs or tags) programmatically
Incident Tracking — Report and list engineering incidents to maintain accurate Change Failure Rate and MTTR metrics
Metadata Oversight — List teams and connected repositories to map technical IDs to organizational structures
DORA Analytics — Retrieve aggregated performance data to identify bottlenecks in your delivery pipeline

The LinearB MCP Server exposes 7 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect LinearB to Pydantic AI via MCP

Follow these steps to integrate the LinearB MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 7 tools from LinearB with type-safe schemas

Why Use Pydantic AI with the LinearB MCP Server

Pydantic AI provides unique advantages when paired with LinearB through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your LinearB integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your LinearB connection logic from agent behavior for testable, maintainable code

LinearB + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the LinearB MCP Server delivers measurable value.

Type-safe data pipelines: query LinearB with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple LinearB tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query LinearB and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock LinearB responses and write comprehensive agent tests

LinearB MCP Tools for Pydantic AI (7)

These 7 tools become available when you connect LinearB to Pydantic AI via MCP:

list_connected_repos

List all connected repositories

list_engineering_teams

List all teams defined in LinearB

list_software_deployments

List recent deployments

list_software_incidents

List engineering incidents

query_software_metrics

Requires a JSON body with requested_metrics and time_ranges. Query software engineering metrics (v2)

record_new_deployment

Requires repo_id and ref. Report a new deployment to LinearB

record_new_incident

Requires provider_id and started_at. Report a new incident

Example Prompts for LinearB in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with LinearB immediately.

"Query the average cycle_time for the last 30 days for team 'Backend'."

"Record a new deployment for repo ID '123' with Git ref 'v1.2.0'."

"Report a new incident starting now for provider 'OpsGenie'."

Troubleshooting LinearB MCP Server with Pydantic AI

Common issues when connecting LinearB to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

LinearB + Pydantic AI FAQ

Common questions about integrating LinearB MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your LinearB MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.