Grafana MCP Server for Pydantic AI 4 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Grafana through the 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 Grafana "
"(4 tools)."
),
)
result = await agent.run(
"What tools are available in Grafana?"
)
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 Grafana MCP Server
Connect your Grafana instance to any AI agent and take full control of your application observability, dashboard inspections, and alerting workflows through natural conversation.
Pydantic AI validates every Grafana tool response against typed schemas, catching data inconsistencies at build time. Connect 4 tools through the 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
- Dashboard Discovery Orchestration — Search for Grafana dashboards by title or tag to retrieve unique UIDs and metadata natively within your chat
- Panel & Query Inspection — Retrieve the full configuration of any dashboard by UID, extracting precise PromQL, LogQL, or SQL queries and panel layouts flawlessly
- Data Source Auditing — List all configured data sources including Prometheus, Loki, CloudWatch, and SQL databases to verify connectivity boundaries securely
- Alert Monitoring Oversight — Enumerate active alert rules and retrieve current firing states to monitor system health and resolve incidents synchronously
- Observability Navigation — Analyze specific localized variables decoding active monitoring routes and extracting structural constraints from your Grafana environment
- SRE Command Center — Verify dashboard UIDs and retrieve query strings to debug performance regressions or analyze log patterns using natural language
The Grafana MCP Server exposes 4 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 Grafana to Pydantic AI via MCP
Follow these steps to integrate the Grafana 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 4 tools from Grafana with type-safe schemas
Why Use Pydantic AI with the Grafana MCP Server
Pydantic AI provides unique advantages when paired with Grafana 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 Grafana integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Grafana connection logic from agent behavior for testable, maintainable code
Grafana + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Grafana MCP Server delivers measurable value.
Type-safe data pipelines: query Grafana with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Grafana tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Grafana and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Grafana responses and write comprehensive agent tests
Grafana MCP Tools for Pydantic AI (4)
These 4 tools become available when you connect Grafana to Pydantic AI via MCP:
firing_alerts
Returns alerting rules that are currently in "firing" state, including their labels and annotations. Get currently firing alerts from Grafana Unified Alerting
get_dashboard
Requires the dashboard UID, which you can get from search_dashboards. Get full dashboard configuration including panels and queries
list_datasources
) are available in this Grafana instance. List all configured data sources in Grafana
search_dashboards
Returns basic info including the UID. To inspect the panels and queries of a dashboard, use get_dashboard with the uid. Search Grafana dashboards by title or tag
Example Prompts for Grafana in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with Grafana immediately.
"Search for dashboards tagged with 'production'"
"Show me the queries for dashboard 'abc-123'"
"Are there any firing alerts right now?"
Troubleshooting Grafana MCP Server with Pydantic AI
Common issues when connecting Grafana to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiGrafana + Pydantic AI FAQ
Common questions about integrating Grafana 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 Grafana 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 Grafana to Pydantic AI
Get your token, paste the configuration, and start using 4 tools in under 2 minutes. No API key management needed.