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

An advisory and generative copilot for Terraform that reviews existing scripts and generates compliant infrastructure using organization-specific rules.

An MCP server that wraps terraform-ls to provide Terraform language intelligence as MCP tools, enabling AI assistants to understand and navigate Terraform codebases.

Generates structured prompts for Claude Code to create comprehensive Terraform test cases including unit, integration, and mock tests.

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

Enables AI agents to perform read-only static analysis of Node.js backend projects, detecting database query anti-patterns, async bottlenecks, connection pooling mistakes, and dependency hygiene issues while returning structured evidence-backed findings.

Enables exact Claude API cost estimation from token counts with correct prompt-cache economics, plus cache break-even analysis showing whether and at which TTL reusing a prefix pays off. It also scans source code for tiktoken-based token counting used alongside Claude models and points to the correct fix.

MCP server for the Codex Security Cloud service, enabling agents to list, inspect, close, reopen findings, request PRs, and apply generated patches.

Enables AI assistants to read Bitbucket Cloud pull requests and diffs through natural conversation.

Enables AI agents to analyze and search IFS Cloud codebases using hybrid semantic-lexical search, PageRank importance ranking, and dependency graph analysis. It supports comprehensive exploration of PL/SQL, entities, and projection files to facilitate intelligent architectural understanding.

Audits dbt Core projects for migration blockers and generates actionable guidance for migrating to dbt Cloud, including auto-fixing deprecated syntax.

Provides intelligent assistance for Azure Terraform development, including documentation retrieval, code validation, security scanning, and best practices guidance.

Enables Cursor AI to query SonarQube for pull request metrics, issues, file issues, and duplication reports directly, without manual data export.

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Gives coding agents source-grounded Apple developer knowledge by searching an index of WWDC20–WWDC26 sessions, Apple documentation, tutorials, Human Interface Guidelines, Swift Evolution, and App Store Review Guidelines. Enables API availability and deprecation lookups, transcript and "what changed" queries, guideline checks, and repo-level Swift app audits through 45 read-only tools.

Enables AI coding agents to quickly search and navigate large codebases by indexing symbols and references, offering tools for fuzzy symbol lookup, file outlining, and reference discovery without a language server.

Enables code agents to run risk-based Jev quality reviews on focused diffs through a read-only MCP tool, returning structured scores and weakness hints without modifying code.

Enables IDE integration with a multi-agent AI pipeline for solving, reviewing, and optimizing code through adversarial peer review and security filtering.

Enables AI assistants to scan Terraform and Dockerfiles for security misconfigurations, returning structured Checkov findings that can be explained in plain language.

MCP server for searching code via SourceGraph's GraphQL API, enabling fast symbol and text search across local or cloud SourceGraph instances.

Enables AI coding agents to search a security corpus, scan Terraform plan JSON for cited findings, and list sources via MCP tools grounded in CIS AWS Foundations and OWASP Top 10 CI/CD risks.