Bring Dora Metrics
to Pydantic AI
Learn how to connect LinearB to Pydantic AI and start using 7 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.
GDPR Free for Subscribers
What is the 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.
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
How it works
1. Subscribe to this server
2. Enter your LinearB Public API Key
3. Start managing your engineering metrics from Claude, Cursor, or any MCP-compatible client
Who is this for?
- Engineering Managers — Monitor team cycle times and delivery health via simple natural language commands
- DevOps Engineers — Automate the reporting of deployments and incidents directly from CI/CD pipelines or IDEs
- CTOs — Quickly audit organizational performance and DORA metrics without opening the dashboard
Built-in capabilities (7)
List all connected repositories
List all teams defined in LinearB
List recent deployments
List engineering incidents
Requires a JSON body with requested_metrics and time_ranges. Query software engineering metrics (v2)
Requires repo_id and ref. Report a new deployment to LinearB
Requires provider_id and started_at. Report a new incident
Why Pydantic AI?
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.
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Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
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Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your LinearB integration code
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Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
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Dependency injection system cleanly separates your LinearB connection logic from agent behavior for testable, maintainable code
LinearB in Pydantic AI
LinearB and 3,400+ other MCP servers. One platform. One governance layer.
Teams that connect LinearB to Pydantic AI through Vinkius don't need to source, host, or maintain individual MCP servers. Every tool call runs inside a hardened runtime with credential isolation, DLP, and a signed audit chain.
Raw MCP | Vinkius | |
|---|---|---|
| Server catalog | Find and host yourself | 3,400+ managed |
| Infrastructure | Self-hosted | Sandboxed V8 isolates |
| Credential handling | Plaintext in config | Vault + runtime injection |
| Data loss prevention | None | Configurable DLP policies |
| Kill switch | None | Global instant shutdown |
| Financial circuit breakers | None | Per-server limits + alerts |
| Audit trail | None | Ed25519 signed logs |
| SIEM log streaming | None | Splunk, Datadog, Webhook |
| Honeytokens | None | Canary alerts on leak |
| Custom domains | Not applicable | DNS challenge verified |
| GDPR compliance | Manual effort | Automated purge + export |
Why teams choose Vinkius for LinearB in Pydantic AI
The LinearB 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. All 7 tools execute in hardened sandboxes optimized for native MCP execution.
Your AI agents in Pydantic AI only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure, zero maintenance.
* 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
How Vinkius secures
LinearB for Pydantic AI
Every tool call from Pydantic AI to the LinearB MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
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.
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.
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Update: pip install --upgrade pydantic-ai