"AWS Cost and Usage Analysis with Cost Optimization Recommendations" matching MCP servers:

Enables local analysis of unstructured documents (PDF, DOCX, PPTX, SVG, PNG) by extracting text and structure with citation anchors, and verifies summaries against source material before a human approves saving a report.

Enables an agent to inspect an audio folder and report what is inside each file — duration, sample rate, channels, codec and bitrate — and to group byte-identical files, identical decoded audio stored in different containers, and near-duplicate candidates, with suggested keeps and explicit differences, all read-only.

Enables ChatGPT web to read local files, search code, run shell commands, and drive a local Codex CLI via an official MCP connector, with human approval for risky commands and full audit logging.

A local MCP bridge that lets ChatGPT chat mode operate the user's own computer, running project, file, and shell tasks through a local executor and returning progress and results to the conversation. It also coordinates Codex sessions (persistent or one-off) and other agent CLIs as sub-agents.

Assists in Advanced Persistent Threat malware analysis by providing tools to securely download samples via jump servers using SSH and SCP. It enables automated retrieval of remote files through a multi-hop configuration for analysis workflows.

Provides secure access to AWS S3 buckets through pre-signed URLs, enabling listing, downloading, uploading, and deleting objects.

Enables forensic analysis of Windows notification databases (wpndatabase.db) from E01 forensic images, automatically extracting and parsing notification data with search, filtering, timeline analysis, and sensitive information detection capabilities.

Enables intelligent analysis and organization of image collections with smart filename generation, metadata extraction, and automated folder organization. Supports batch processing, color analysis, EXIF data extraction, and multiple naming styles for efficient photo management.

An MCP server that converts Markdown files containing Mermaid diagrams into PDF documents by rendering diagrams as SVG images. It provides a specialized tool to automate document conversion while ensuring all visual charts are correctly embedded in the final output.

Provides a sandboxed filesystem API for AI agents, enabling natural-language file read, write, append, delete, move, list, create, and metadata operations through MCP tools.

Enables extracting text, structure, and image data from unstructured documents (PDF, DOCX, PPTX, SVG, PNG, JPG) and scanning folder trees.

Enables local folder analysis of unstructured documents (PDF, DOCX, PPTX, TXT, SVG, PNG, CSV, XLSX) by extracting structure, reading content, and generating reports, with a strict approval gate before any save operation.

Enables read-only scanning and text extraction from PDF, DOCX, PPTX, SVG, and PNG files in a local folder, providing the raw text to AI models for summarization or analysis without an external LLM API.

Enables document conversion between PDF, DOCX, and Markdown formats to facilitate reading and editing complex files in AI tools like Claude Desktop or Cursor. It utilizes marker-pdf and pandoc to provide structured text versions of documents, helping to manage context and support unsupported file types.

Enables LLM hosts like Claude Code or Codex CLI to inspect folder structures and read unstructured documents (PDF, PPTX, DOCX, SVG, and images) by returning raw text and images for the host to summarize or analyze.

An Amazon S3 Model Context Protocol server that allows Large Language Models like Claude to interact with AWS S3 storage, providing tools for listing buckets, listing objects, and retrieving object contents.

Memory bank with Server as SSH support for central knowledge base

Enables secure filesystem access with privacy controls using .mcpignore files to block MCP clients from reading or writing sensitive files and directories while allowing directory browsing and search.

Provides a read-only MCP bridge that lets Claude Web inspect workspace files, git state, and recorded test results while Codex remains the sole executor of changes.

Enables AI models to access external services including weather data, file system operations, and SQLite database interactions through a standardized JSON-RPC interface. Features production-ready architecture with security, rate limiting, and comprehensive error handling.