Bring Prometheus
to Pydantic AI
Learn how to connect Prometheus to Pydantic AI and start using 14 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.
GDPR Free for SubscribersCompatible with every major AI agent and IDE
Gemini
What is the Prometheus MCP Server?
Connect your Prometheus instance to any AI agent and transform your observability data into actionable insights through natural conversation.
What you can do
- Instant & Range Queries — Evaluate complex PromQL expressions for real-time status or historical trends over specific time windows.
- Metric Discovery — Find time series matching specific label selectors and explore available labels and their values across your environment.
- Metadata Inspection — Retrieve detailed metadata about metrics scraped from targets to understand units, types, and help text.
- Admin Operations — Create data snapshots, delete specific series, and clean tombstones (requires admin API enabled).
- System Status — Inspect your Prometheus configuration, flags, and runtime information to ensure your monitoring stack is healthy.
How it works
- Subscribe to this server
- Enter your Prometheus Server URL (and optional Auth Token)
- Start querying your metrics from Claude, Cursor, or any MCP-compatible client
No more manual dashboard building just to answer a quick question about system health. Your AI acts as a dedicated SRE or DevOps engineer.
Who is this for?
- SRE & DevOps Engineers — instantly troubleshoot incidents by querying metrics and checking configurations without leaving the terminal or chat.
- Backend Developers — verify service performance and resource consumption directly from the code editor.
- Platform Teams — automate infrastructure health reports and audit monitoring configurations via natural language.
Built-in capabilities (14)
enable-admin-api to be enabled. Remove deleted data from disk
enable-admin-api to be enabled on the Prometheus server. Create a snapshot of all current data
enable-admin-api to be enabled. Delete data for a selection of series in a time range
Find time series matching label selectors
Get all values for a specific label
Get a list of all label names
Get metadata about metrics scraped from targets
Get Prometheus build information
Get the currently loaded Prometheus configuration (YAML)
Get configured Prometheus flag values
Get Prometheus runtime information
Get TSDB cardinality statistics
Evaluate a PromQL expression at a single point in time
Evaluate a PromQL expression over a range of time
Why Pydantic AI?
Pydantic AI validates every Prometheus tool response against typed schemas, catching data inconsistencies at build time. Connect 14 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.
- —
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 Prometheus 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 Prometheus connection logic from agent behavior for testable, maintainable code
Prometheus in Pydantic AI
Prometheus and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect Prometheus 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 | 4,000+ 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 Prometheus in Pydantic AI
The Prometheus 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 14 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
Prometheus for Pydantic AI
Every tool call from Pydantic AI to the Prometheus MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
Can I run a PromQL query to get the current value of a metric?
Yes. Use the query tool to evaluate any PromQL expression at a single point in time. This is perfect for checking current CPU usage, memory levels, or error rates.
How do I see how a metric has changed over the last hour?
Use the query_range tool. You can specify the start and end timestamps along with a step duration to retrieve historical data points for graphing or trend analysis.
Can I perform administrative tasks like creating backups?
Yes, if your Prometheus server has the Admin API enabled (--web.enable-admin-api), you can use the create_snapshot tool to create a snapshot of all current data on disk.
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 Prometheus MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.
MCPServerHTTP not found
Update: pip install --upgrade pydantic-ai
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