Linear MCP Server for Pydantic AI 12 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Linear through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
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 Linear "
"(12 tools)."
),
)
result = await agent.run(
"What tools are available in Linear?"
)
print(result.data)
asyncio.run(main())
* 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 Linear MCP Server
Connect your Linear workspace to any AI agent and take full control of your issue tracking and sprint workflows through natural conversation.
Pydantic AI validates every Linear tool response against typed schemas, catching data inconsistencies at build time. Connect 12 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
- User & Team Discovery — Retrieve the authenticated user profile and list all teams configured in your Linear workspace
- Issue Management — List, search, inspect and create issues with full metadata including assignees, labels, priority and state
- Project Oversight — Browse all active projects, view their status and drill into specific project details by ID
- Comments & Collaboration — Add comments to issues to keep your team context aligned without switching to the Linear app
- Cycle Tracking — List all sprint cycles for any team, including start/end dates and completion progress
- Label Organization — View all issue labels used for categorization across teams
The Linear MCP Server exposes 12 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 Linear to Pydantic AI via MCP
Follow these steps to integrate the Linear 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 12 tools from Linear with type-safe schemas
Why Use Pydantic AI with the Linear MCP Server
Pydantic AI provides unique advantages when paired with Linear 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 Linear integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Linear connection logic from agent behavior for testable, maintainable code
Linear + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Linear MCP Server delivers measurable value.
Type-safe data pipelines: query Linear with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Linear tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Linear and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Linear responses and write comprehensive agent tests
Linear MCP Tools for Pydantic AI (12)
These 12 tools become available when you connect Linear to Pydantic AI via MCP:
create_comment
The body supports Linear Markdown format including @mentions and ~~strikethrough~~. Add a comment to a Linear issue
create_issue
Requires the team ID and issue title. Optionally set description, assignee, priority (0=No priority, 1=Urgent, 2=High, 3=Normal, 4=Low) and label IDs. Create a new Linear issue
get_issue
Use the issue ID (UUID) or the human-readable identifier (e.g. TEAM-123). Get full details for a Linear issue
get_project
Get details for a specific Linear project
get_viewer
Useful to verify which account the API token belongs to. Get current authenticated Linear user details
list_cycles
Each cycle has a number, name, start date, end date and completion progress percentage. List Linear cycles (sprints) for a team
list_issues
Optionally filter by team ID to get issues for a specific team only. List Linear issues
list_labels
Optionally filter by team ID. Each label has a name, color and optional description. List Linear issue labels
list_projects
Projects group issues across multiple teams. Use optional limit to control how many results to fetch. List Linear projects
list_teams
Each team has a unique ID, name, key prefix and optional description. Use this to discover teams before querying their issues or cycles. List all Linear teams
search_issues
Optionally filter results to a specific team. Returns issues with identifier, title, state, priority, assignee and URL. Search Linear issues by text
update_issue
Provide the issue ID (UUID) and only the fields you want to change. Update an existing Linear issue
Example Prompts for Linear in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with Linear immediately.
"Show me all unresolved issues assigned to the Engineering team."
"Create a new issue in the Backend team titled 'Add rate limiting to /api/search endpoint' with high priority."
"What's the current sprint cycle progress for the Mobile team?"
Troubleshooting Linear MCP Server with Pydantic AI
Common issues when connecting Linear to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiLinear + Pydantic AI FAQ
Common questions about integrating Linear MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
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?
Can I switch LLM providers without changing MCP code?
Connect Linear with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Linear to Pydantic AI
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.