"Terraform Cloud - Managing Cost and Performance" matching MCP servers:

Enables AI assistants to interact with Terraform Cloud workspaces and runs, including checking run status, listing workspaces, and retrieving detailed information about workspaces and runs.

The Terraform MCP Server is a Model Context Protocol (MCP) server that integrates seamlessly with Terraform Registry and HCP Terraform APIs, enabling advanced automation and interaction capabilities for Infrastructure as Code (IaC) development.

MCP server for managing Terraform infrastructure as code, enabling initialization, planning, applying, destroying, validating, and showing state of Terraform configurations.

Enables AI assistants to search and retrieve information about Terraform providers and modules from the public Terraform registry, including detailed documentation, version information, and resource specifications.

Exposes Terraform/Terragrunt tooling as Claude Code tools for generating variables.tf and mock outputs automatically from input files.

Converts non-functional requirements and code into secure, traceable JMeter performance tests with coverage analysis, plan generation, execution, and GitHub draft PRs.

Enables sweeping commission and slippage assumptions across backtest results to see how performance degrades, pinpoint the exact cost level where a strategy's edge hits zero, and audit for look-ahead-bias patterns like data leakage and repainting indicators.

Local-first cost tracking and model routing for MCP agents with offline token counting, budget alerts, and spend reports.

Enables analysis of AWS costs and usage through the AWS Cost Explorer API, supporting multi-account architectures with role assumption and cost comparison, forecasting, and anomaly detection.

MCP server for the Partiri Cloud PaaS platform. Enables AI agents to manage workspaces, projects, services, deployments, and metrics.

Enables MCP-compatible AI agents to deploy and manage applications on Google Cloud Run, including service deployment, listing, logging, and project creation.

Enables MCP-compatible AI agents to deploy applications to Google Cloud Run by providing tools for deploying code, listing services, and managing Google Cloud projects.

Enables remote cloud control and live LAN camera access for Bambu Lab 3D printers through 31 MCP tools.

Enables automated access to LoadRunner Cloud APIs for retrieving performance test data, managing projects, and collecting test results. Supports integration with AI clients for building performance engineering workflows and dashboards.

Enables analyzing AWS cloud spending and Bedrock usage through natural language queries using Claude.

A command-line interface and API that allows users to analyze and visualize AWS cloud spending data by enabling Claude to query AWS Cost Explorer through natural language conversations.

Provides AI agents and teams with self-hosted adaptive memory, combining hybrid search and a self-improving neural memory graph to enable precise recall and serendipitous discovery across MCP-compatible clients.

Enables MCP clients to connect to a privacy-first, self-hostable workout planning and training log, allowing coaching agents to preview and apply program changes while accessing training data through OAuth-protected endpoints.

Enables AI assistants to discover and control ZenControl DALI-2 lighting systems through natural language via the ZenControl Cloud API.

Enables AI agents to search, filter, and monitor curated AI model API free credits and low-cost token promotions through eight tools, four resources, and three prompts — including finding deals expiring soon, checking eligibility and blocked conditions, and producing daily briefs.