"AWS Secrets Manager" matching MCP servers:

Provides tools for discovering, building, deploying, and troubleshooting AWS Deep Learning Containers (DLC) images. Supports multiple frameworks and instance recommendations.

Automates the AWS Foundational Technical Review (FTR) partner self-assessment by evaluating SOC 2 and WAFR reports against defined controls, returning structured PASS/FAIL decisions with reasoning.

Enables AI agents to scan text for leaked secrets and prompt injection markers, and redact them before reaching an LLM.

Rewrites resumes to beat ATS screening (Workday, Greenhouse, iCIMS, Taleo) against a specific job description, with strict truthfulness guardrails — never invents dates, metrics, titles, or seniority. Pay-what-you-want access codes ($0 works).

MCP server for multi-provider AI image generation (AWS Bedrock, OpenAI, Google Gemini) enabling image generation, transformation, and editing through a unified interface.

Pay-per-use IBM Granite AI over MCP — chat, code, embeddings, analysis, and forecasting, billed in USDC on Solana. No IBM account required.

AI e-commerce operations manager for MCP. Inventory forecasting, pricing optimization, RFM customer segmentation, order anomaly detection, and automated reports for Shopify and WooCommerce.

Enable AI agents such as Claude, Co-Pilot, Codex and other compatible tools to create, manage, and optimize advertising campaigns on ChatGPT.

DCL Trust Oracle is a deterministic AI audit layer, natively integrated with MCP, that evaluates LLM and agent outputs against configurable policies before and after action. Every verdict is written to a tamper-evident, hash-chained audit log storing only cryptographic metadata — never raw content — enabling privacy-first forensic review.

A deterministic MCP server that guards AI/agent output by detecting secrets, injection, and PII in ~85µs with zero dependencies. Gives a clear ship/block verdict and runs locally or hosted.

Open-source AWS security scanner with Attack Chains, Breach Cost Estimation, and MCP Server. 47 checks across 15 AWS services. Every finding includes copy-paste remediation (CLI + Terraform) and a dollar-risk estimate with verified source. First free standalone AWS security MCP server - Prowler and Wiz require paid SaaS.

Enables management of TestMu AI test projects, test cases, test runs, and integration with Jira, HyperExecute, and AI insights through natural language.

Read-only MCP server exposing Ads AI Creator's image styles, pricing, and FAQ for AI clients to generate ad knowledge.

MCP server that delegates Claude Code subagents to alternative backends such as local models, DeepSeek, or AWS Bedrock, while keeping your Claude Code orchestrator session intact.

Enables AI agents and assistants to work with a Databricks workspace — SQL, clusters and warehouses, notebooks, Jobs, Lakeflow pipelines, Unity Catalog, Volumes, dashboards, Genie, model serving, Vector Search, Lakebase and Apps — under a safety-first model with read/write/destructive/security classification, confirmation steps for dangerous changes, production resource protection, and a fully read-only mode. Every call is checked against the official Databricks SDK, returns typed schemas and redacted secrets, and unsupported features report an explicit error rather than faking results.

VibOps MCP is the control plane between your AI agents and your GPU infrastructure. 74 tools covering: GPU fleet management (deploy, scale, monitor across NVIDIA, AMD, Intel, AWS, Google, Groq), Agent Infrastructure Control Plane (per-agent GPU cost, budget enforcement, model policies, dependency graph), governance (AI Act, SOC 2, immutable HMAC audit chain), and GPU FinOps (chargeback, waste..)..

Provides tools to validate SageMaker workloads against the six pillars of the AWS Well-Architected Framework, returning findings with severity levels and recommendations.

An MCP server for the OpenAI Ads (ChatGPT Ads) Advertiser API, providing tools for agent-led performance marketing such as finding dead ads, auditing near-duplicate copy, and auditing ad-group context hints.

Enables a cloud orchestrator to query a local Ollama model over Tailscale or a LAN as a side-channel for drafts, summaries, and other helper output, with tools to list available models and to redact text. Answers leaving the server are passed through output-only DLP that masks card numbers, IBANs, emails, phone numbers, API keys, and similar secrets before they reach the cloud.

Enables observability and governance for local LLMs via Ollama, including auditing model usage, scanning prompts for secrets/PII, and enforcing allow/deny policies.