"Android Studio Development Platform" matching MCP servers:

Demonstrates agent-native platform onboarding with human-in-the-loop API key provisioning, plus live usage and rate-limit queries via Anthropic Admin APIs.

A read-only MCP server that enables AI agents to act as GCP platform engineers, allowing them to investigate incidents, take inventory, and find cost-optimization opportunities in Google Cloud projects without mutating any infrastructure.

Read-only MCP server that exposes Kubernetes platform state (tenants, pods, SLOs, ArgoCD applications, chaos schedules, and catalog services) to AI agents, enabling natural language queries about cluster health and configuration.

Read-only MCP server exposing Android phone timeline data, including events, hourly/daily features, data coverage, and gaps, so Claude can answer questions about observed phone activity.

Lets your coding agent talk to the RunWhen platform — workspace chat, issues, SLXs, run sessions, and the Tool Builder — over the Model Context Protocol. Enables workspace chat with AI assistant, task authoring via Tool Builder, and direct data access to workspaces, issues, SLXs, run sessions, and more.

Enables DevOps operators to query and operate the Sigma Streaming Platform from any AI client such as Claude Desktop, Cursor, or Kiro.

Enables approval-gated incident response workflows that gather evidence through read-only MCP tools, perform idempotent writes, and preserve a durable audit trail.

An MCP server that enables AI assistants to capture and analyze HTTP/HTTPS traffic from Android devices. It supports smart searching of network requests and provides tools for detailed traffic analysis via natural language.

MCP servers for real-time development monitoring and interactive pair programming.

Paid remote MCP for AI Studio Workspace with approval gate, structured receipts, audit logs, and reviewer-ready evidence, enabling workspace change checks, model run approval, spend estimation, and audit export.

Provides AI agents with real-time access to live Azure infrastructure, including AKS cluster health, resource management, policy validation, and Terraform analysis through a Model Context Protocol interface.

Enables ingestion and full-text search of Windows application logs via MCP tools. Supports dual-backend indexing with Elasticsearch and SQLite for fast retrieval.

Enables reading and building Splunk dashboards via the management REST API, using SSO for permissions and supporting both Studio and Classic formats.

Local MCP server for observing and controlling an authorized Android device over USB using ADB and scrcpy, providing screen capture, UI automation, app inspection, logcat, and evidence recording.

A FastAPI-based server that enables executing SQL queries, managing database connections, and retrieving analytics reports through MCP-integrated endpoints. It allows users to interact with database schemas, performance metrics, and access logs using structured queries.

Enables AI assistants to interact with SMB platform APIs for querying business data, managing tasks, accessing connectors, dashboards, and monitoring security.

Enables users to inspect ephemeral GitHub Actions runner fleet status, SLOs, incidents, capacity recommendations, and cost evidence through read-only MCP tools, without allowing infrastructure or cluster mutations.

A standalone Python/FastAPI server that implements the Model Context Protocol (MCP) for the OPTIX threat intelligence platform. It exposes 26 analyst-friendly tools that AI assistants and programmatic consumers can use to query threat feeds, search documents and indicators, manage watchlists, triage IOCs, generate detection rules, trigger AI research, and produce intelligence reports.

Enables named, authenticated callers to run read-only introspection of a Google Cloud project and to read and write per-tenant notes that stay isolated between callers. Every request is scope-checked against a reviewed policy, rate-limited per caller, and audited without ever logging argument values.

Provides read-only production observability through a small set of tools for health, job status, latest data, approved logs, and deployment metadata, enabling AI planning and review without a production write path.