Provides AI clients with public breach-response guidance, scenario choices, official links, and blank inquiry, compensation, or mediation templates, while avoiding reading or returning personal data documents.

A self-hosted MCP memory server that gives every MCP-compatible agent a shared, persistent memory layer, so preferences, project conventions, and past decisions carry across sessions and across different agents and vendors. It combines hybrid keyword-plus-vector retrieval with automatic context injection, zero-config sync, and selective injection of memories by agent and priority.

Deterministic Unit-Record Memory Engine for AI coding agents. Eliminates context-window amnesia and compaction bloat via zero-turn workspace hydration, byte-exact registers, and live context telemetry.

Enables AI agents and assistants to inspect live system telemetry, check TCP port connectivity, audit environment variables for leaked secrets, analyze and summarize log files, and list Docker containers — all with token-efficient, compressed output.

Enables AI coding agents to retrieve compact structural codebase context through repository architecture summaries, AST symbol extraction, cyclomatic complexity scoring, and TODO/FIXME hotspot search.

Enables AI agents and LLMs to persist facts, preferences, and observations in a local SQLite store and to build and traverse subject-predicate-object knowledge graphs across sessions. It provides fuzzy keyword search and graph queries so assistants retain context and recall prior project history without repeating prompts.

Enables AI clients to build, solve and inspect LS-DYNA analyses, either by driving Ansys Mechanical through PyMechanical or by submitting and monitoring standalone keyword-deck runs. It also reads and audits solver output files such as glstat, binout, d3plot and ASCII histories, with results comparison and deck checks.

Enables auditing, formatting, and compiling Brazilian academic manuscripts (TCCs, dissertations, articles) against ABNT and IBGE standards, with structural diagnostics, citation-reference cross-checks, tone analysis, bibliography lookup via Crossref/SciELO and DOI, and PDF generation through Typst or LaTeX. It also supports spec-driven research planning and simulated defense-board questioning so objectives and conclusions stay aligned.

Enables compatible assistants to inspect Live sessions, silently mix and master bounced stems, compare references, design with installed plugins, and track local evidence and progress through supported tools.

Enables AI agents to drive a real Chromium browser by receiving compact, token-efficient page maps and only the changes after each action. It guards against hidden prompt injections, blocks payments, sends and deletions until the user approves, and lets agents learn site-specific notes over time.

An intelligent Model Context Protocol (MCP) Server that acts as an AI model advisor. Compare LLM and media pricing, features, and quality across 1,000+ models from OpenRouter, fal.ai, Together AI, Replicate, and Fireworks AI. Deeply compatible with Claude

WordPress MCP server + companion plugin giving AI native access to page builder content, not just the REST API. 276 tools, 380 with WooCommerce, 19 page builders and the Site Editor. Element-level edits, full page creation, HTML to builder conversion, sna

Enables users to refine desired Korean roles and industries through dialogue, generate recent job-search links, and monitor job postings from company sources and optional official APIs via local stdio MCP tools.

A read-only Figma MCP server that lets Claude list frames, read a frame's layout as flexbox, extract color and text styles as CSS variables, export icons as SVG, and render a PNG of the frame for visual comparison. This gives Claude everything it needs to build a matching web page from a design on Figma's free plan, with responses cached locally.

Enables AI chat backends to fetch public webpages and obtain cleaned readable text, Markdown, article content, metadata, and summary-ready prompts through MCP tools.

Enables MCP clients such as Codex and Claude to plan natural-language or timed actions, generate and revise skeletal motion locally through a Kimodo runtime, and review results with diagnostics, candidate comparison, and interactive 3D previews. Provides checked handoffs of verified exports into Blender and Unreal Engine.

Enables users to compute symbolic derivatives and integrals, including definite and improper cases, and verify antiderivatives through SymPy-backed MCP tools.

Enables MCP-compatible clients to plan, validate, generate, and deploy complete network topologies from natural-language instructions, including live deployment to a running network simulator and export to configuration or script files.

Exposes an OSINT investigation toolkit to any MCP-compatible AI client, which can drive passive checks such as DNS, WHOIS/RDAP, breach, IP reputation, GitHub and news-geo lookups and chain findings across them. The server lists 20 tools by default (16 passive plus 4 graph and batch helpers) and 25 when active checks are explicitly allowed, running via uvx with no separate install.

Enables Claude to offload bulk reading, first-draft writing, and mechanical edits to a local or OpenAI-compatible model, with code-enforced verification, staged patches, project checks, and token budgets to save tokens and cost.

MCP server for Qut Pay (qut.kz): create Kaspi QR / Kaspi link invoices for Kazakhstan merchants, check status, list, cancel, refund (live needs confirm) and simulate payments in sandbox. 9 tools, 5 doc resources, 2 prompts. npm: npx -y @qutpay/mcp with QUTPAY_API_KEY. Source in examples/mcp. Independent service, not affiliated with Kaspi Bank.

Enables MCP clients to query and diagnose machine-learning training runs through read-only tools for listing experiments, reading configurations and final metrics, comparing runs, sampling metric curves, and detecting training failures.

Enables MCP clients to coordinate local AI model runs by monitoring RAM, system commit, and VRAM, queueing tasks across clients, performing admission checks before inference, renewing leases, completing and releasing runs, and reporting status, history, and health while preserving original generation quality parameters. It also supports controlled start/stop and optional idle application memory reclamation.

Enables AI agents to write, compose, and check assume-guarantee contracts, including refinement, consistency, compatibility, realizability, and LTL checks, and to use returned counterexamples to fix specifications.