"Exploring Paperless Solutions" matching MCP servers:

An MCP server that puts a paperless-ngx document archive within reach of AI assistants, enabling full-text search, reading, filing, uploading, and managing documents and taxonomy through the paperless-ngx REST API.

Provides eight deterministic, source-grounded tools for exploring the Beautiful Solutions toolbox, enabling organizers and educators to search, compare, map, and build discussion guides around liberation-focused values, principles, solutions, and stories without runtime LLM or network calls.

An MCP (Model Context Protocol) server for interacting with a Paperless-NGX API server. This server provides tools for managing documents, tags, correspondents, and document types in your Paperless-NGX instance.

Enables AI agents to perform software engineering tasks inside an isolated, deterministic sandbox—exploring repositories, reproducing failures, applying patches, running tests, and verifying solutions against hidden suites through MCP tools.

MCP server for Paperless-ngx document management. Enables AI models to search, retrieve, update documents and manage metadata.

A Model Context Protocol server for Paperless-ngx that provides full REST API coverage for document management, including search, upload, metadata, workflows, sharing, and admin, with token-frugal responses and a scoped toolset.

Enables AI assistants to search, inspect OCR content, download, upload, and manage documents in a Paperless-ngx archive through natural language.

Analyzes Microsoft Sentinel solutions from GitHub repositories to map data connectors to Log Analytics tables and query security content like detections and playbooks. It provides instant access to the official Content Hub or private repositories through a high-performance pre-built index.

Enables AI assistants to manage Paperless-NGX documents, tags, correspondents, workflows, and more through the full REST API.

Enables MCP agents to list, query, fetch, and download documents in a Paperless-NGX instance, along with managing tags, correspondents, and document types. Adds bounded content reads — ranged content windows, passage search, and an inline character budget with length metadata — so agents can locate and extract document text without flooding their context.

Enables searching, tagging, uploading, and reading documents in Paperless-NGX, with management of tags, correspondents, document types, and custom fields via MCP tools and resources.

Local MCP server for Survey Solutions Headquarters workflows, enabling connection profile management, read operations, export handling, and guarded fieldwork/admin actions through natural language.

Search, filter, and sample over a million competition and olympiad math problems (AMC, AIME, IMO, Putnam, MATH, Omni-MATH) by topic, contest, year, and difficulty, and fetch any problem's LaTeX statement with its answer, hints, and solutions where available.

Enables searching TV series and exploring subtitle coverage and episode records from tvsubtitles.net, including release details, uploader, and download page links, with no API key required.

Searches your local Claude Code conversation history to recall and resume past solutions.

Enables AI agents to describe combinatorial optimization problems as JSON and obtain validated, ranked, re-verified solutions through MCP tools.

Enables LLMs to search and retrieve exam problems, solutions, and answers from the СДАМ ГИА educational platform across multiple subjects. It supports fuzzy text matching, catalog browsing, and structured data retrieval to assist with academic study and test preparation.

Analyzes Python project structure by exploring directory trees, extracting outlines from Python and Markdown files, and inspecting OpenAPI specifications.

Enables exploring and analyzing US Data Analyst job postings, offering job search, salary benchmarking, and skill-demand insights through MCP tools, resources, and a career gap review prompt.

A local MCP server that indexes a repository into line-range chunks and serves compact evidence packets to cut token usage when exploring or debugging code.