"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.

Enables Claude and other MCP clients to query and manage partner API records using self-hosted credentials, with read-only or full modes, paged results, and a confirmation step for high-impact writes.

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 AI agents and Amazon Alexa+ skills to orchestrate desktop load-testing workflows hands-free, including launching closed-loop or ramp-up traffic campaigns, streaming live RPS and P50/P95/P99 telemetry, gracefully halting runs, and retrieving bilingual executive capacity audits. Tools are Zod-validated and exposed over stdio with built-in circuit-breaker safety monitoring.

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

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

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 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.

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 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 AI clients speaking MCP over stdio to query a self-hosted Sencho hub read-only, listing nodes, stacks, container health, update and rollback readiness, and fleet health with no write, log, file, or shell tools.

Enables comprehensive NIST Cybersecurity Framework 2.0 assessments with 40+ MCP tools for maturity scoring, gap analysis, implementation planning, risk assessment, and executive reporting across 740 security questions.

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.

A high-performance MCP server integrated with a Django analytics dashboard, featuring 19 tools for AI interaction tracking, AST code analysis, and real-time token efficiency metrics.

Enables AI assistants to answer operators' questions about their Dreambooth Studio booths via a read-only API wrapper, providing session, project, revenue, credit, and wallet data with OAuth device flow authentication.

AI development observability platform. It silently captures structured data from vibe coding sessions via MCP and codifies deviation patterns into project rules to make AI write better code. 100% local and zero runtime dependencies.

Offline Python MCP server that audits Zapier, Make and n8n lead pipelines and lists where inbound leads are silently dropped.

An MCP server for automating Microsoft Dynamics CRM and Power Platform through C# plugin generation, schema management, and solution deployment. It includes comprehensive tools for system telemetry, performance monitoring, and web resource management to streamline CRM development workflows.

A remote MCP server that enables real-time interaction with the Dynatrace observability platform, bringing production-level monitoring data directly into development workflows.

Enables seamless interaction with the IBM Instana observability platform, allowing access to real-time observability data directly within development workflows.

Enables AI-assisted management of multi-vendor network devices (Cisco, Juniper, Nokia, Arista, Linux) through natural language, supporting health checks, configuration changes, routing analysis, and automation workflows.

This remote MCP server allows interaction with the Dynatrace observability platform. Bring real-time observability data directly into your development workflow.

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.

Provides cross-platform device information including system specs, battery status, storage, and memory details for Windows, macOS, Linux, and Android through a Model Context Protocol server.

A lightweight MCP server that enables AI assistants to execute local development tools and retrieve system status with low latency over stdio or HTTP.

Zero-dependency, single-file MCP server for Termux/Android, Raspberry Pi, and Linux. No external dependencies required.

A Model Context Protocol (MCP) server that lets an AI assistant read and operate the Lumics network monitoring platform via its REST API.

MCP server for Smart Compost platform that lets LLM agents list composting processes/devices and pull measurements as read-only tools.

Enables MCP-compatible AI assistants to inspect projects, processes, ports, logs, HTTP endpoints, Git state, and development databases for local runtime diagnostics.

Enables MCP clients to inspect Google Play releases, reviews, Android vitals, and scoped bulk reports, and to prepare explicitly authorized changes using locally supplied service-account credentials. Reads are default, while optional writes and sensitive reads require explicit configuration and single-use execution.