Stop explaining production to your agent.
Let it see for itself.
Polylane exposes your entire stack over one MCP server: your clouds, your telemetry, your deploys, your code. In the tool you already use.
> why is checkout slow in prod?
●polylane · searchissues · checkout-edge
●polylane · runToolqueryMetrics · p99 by deploy
●polylane · runTooldeployHistory · checkout-edge
⏺ Deploy 9f3c2a1 shrank the Hyperdrive pool 50 → 5; P99 followed within a minute. Polylane already raised the issue, and the investigation carries the receipts. The fix is a one-line pool restore: want me to open it?
Waiting for your reply…
Your entire stack. Behind one server.
Your clouds
AWS, Cloudflare, Vercel, Fly.io, Render, Kubernetes, PlanetScale, Supabase, Modal: the live topology, every resource, every dependency.
Your telemetry
Logs, metrics, and traces through Datadog, Honeycomb, Axiom, and Sentry: the agent reads the same series your dashboards chart.
Your code and deploys
GitHub, deploy history, and code search: what shipped, when, and what it touched.
Your memory
The workspace remembers every incident: notes, what worked, the queries that found it. The agent never starts from zero.
Five tools. Not five hundred.
a stack of MCP servers
of the context window spent on tool schemas
polylane
five tools; schemas load when a tool runs
searchTools Discover the workspace's agent tools (observability queries, infra graph, code search, deployments) and fetch their schemas.
runTool Run an agent tool discovered via searchTools. Read-only by default; writes require write access and a session opt-in.
runCode Chain several agent tools in one call by running TypeScript against the tools namespace.
search Run a read-only query against the Polylane API: context graph resources, telemetry, issues, investigations, and more.
execute Execute an action against the Polylane API: create investigations, run automations, manage checks and integrations.
Prefer the CLI? One command wires everything.
curl -fsSL https://polylane.com/install | bash
How it works. Code mode.
Instead of calling tools one by one, the agent writes a small program that calls them all and returns just the answer.
any other setup · twenty questions
● queryMetrics → 41 kB of series
● deployHistory → 12 deploys
● queryMetrics → another 41 kB
● queryLogs → 3,000 lines
… 17 more round trips
every result lands in the context window
polylane · runCode
const deploys = await tools.deployHistory(…);
const p99 = await tools.queryMetrics(…);
return correlate(deploys, p99);
✓ one answer back: deploy 9f3c2a1
intermediate results stay in the sandbox
Give your agent production. It gives you back the fix.
Reading this as an agent? https://polylane.com/llms.txt · https://polylane.com/agent.json · https://polylane.com/.well-known/mcp · this page as markdown: https://polylane.com/for-agents.md