"Finding people on LinkedIn using their names" matching MCP connectors:

Read Spike.sh incidents, on-call, escalations and services; acknowledge, resolve, set priority.

Manage cron/heartbeat checks, read pings and flips, pause/resume/delete on Healthchecks.io.

Read incidents, services, teams, on-call schedules; acknowledge, resolve and note incidents.

Manage incidents and on-call: list/create/update incidents, who is on call, on-call overrides.

Read monitors, incidents, heartbeats, on-call and status pages; acknowledge or resolve incidents.

Run a prompt through a LangChain (system + human) chain over Gemini on Vertex AI; optional LangSmith

Connect engineering metrics, DORA performance, and deploy risk scoring to any AI assistant. Score PRs for deployment risk using a 36-signal model, query team health, incidents, coverage, and more.

Enable secure connectivity between Sentry issues and debugging data, and LLM clients, using a Model Context Protocol (MCP) server.

MCP-native AI SRE. Exposes your production OpenTelemetry problems, traces, and logs over the Model Context Protocol, plus an AI remediation loop that opens a reviewed GitHub fix PR and verifies in production (reopening on regression). Tools include list_problems, get_problem, query_traces, detect_anomalies, and request_problem_remediation. Human-in-the-loop by default — the merge button stays yours.

Debug production issues using Shipbook logs and Loglytics error insights.

The Buildkite MCP server exposes Buildkite product data (pipelines, builds, jobs, and test data) to AI tools, editors, and agents through the Model Context Protocol. It provides capabilities including pipeline creation and management, build monitoring with specialized tools like 'wait_for_build', efficient log querying using Apache Parquet conversion and caching, and OAuth-based authentication for both read-write and read-only access to Buildkite's REST API.