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"AWS cloud service monitoring" matching MCP servers:

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    Enables interaction with AWS Fault Injection Service to create, manage, and execute chaos engineering experiments. Operates in read-only mode by default for security, with optional write operations for starting/stopping experiments and managing templates.
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    4
    MIT
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    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.
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    An MCP server that connects AI assistants to Xray Cloud for Jira test management, enabling natural language interaction with tests, test plans, executions, and CI result imports.
    MIT
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    Visual CVE audit dashboard for npm, Python, Go, and Rust projects. Scans your project manifests (package-lock.json, requirements.txt, go.sum, Cargo.lock) against OSV.dev live data, opens a browser dashboard for human review, then applies fixes only after explicit confirmation. Supports multi-service monorepos in one command.
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    MIT
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    A thin MCP server that wraps provenance-receipts and quality-gate services as tools, enabling AI agents to certify content origin and score quality via Ed25519-signed receipts.
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    MIT
  • A
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    Behavioral verification intelligence for AI coding agents. Reads a TypeScript or JavaScript repo, clusters functions into 25 semantic workflows (Authentication, Payments, Webhooks, Caching, Queue, and more), and emits concrete adversarial probes per workflow. 17 MCP tools, local SQLite state, zero cloud.
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    MIT
  • A
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    Compacts noisy test, build, and cloud-log output before an AI agent reads it — dedupes repeats, collapses stack frames, folds Playwright retries, and renders CloudWatch/GCP JSON logs down to the signal. Typically 80–95% fewer tokens on failures. Tool: compact_output. Run: npx -y logslim logslim-mcp
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    MIT
  • A
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    Enables interaction with Xray Cloud and Data Center for test management, including authentication, GraphQL queries, test execution/plan management, and result import via MCP.
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    MIT
  • A
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    Enables AI assistants to interact with SmartBear's suite of testing and monitoring tools including BugSnag, Swagger, Reflect, and more through natural language, allowing querying of testing data, analysis of performance metrics, and management of test automation.
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    MIT
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    Enables AI assistants to interact with Coval's evaluation platform for launching and monitoring evaluation runs, managing agents and test sets, and retrieving evaluation metrics.
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    MIT
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    An MCP server that exposes the QMetry Test Management for Jira Cloud REST API as tools for MCP-compatible clients. It enables users to manage test cases, test cycles, test executions, test plans, folders, and automation rules through natural language interactions.
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    MIT
  • A
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    Detects schema mismatches between data producers and consumers through static analysis, supporting extraction, comparison, code generation, and automated validation with watch mode for MCP tools, APIs, and service contracts.
    11
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    MIT