Enables AI assistants to inspect PostgreSQL schemas and table metadata, preview SELECT queries with capped rows, and receive write or DDL statements for manual review without execution.

Enables an AI agent to see and control the screen of an already-running macOS virtual machine over VNC, exposing four tools: screenshot, click, type_text, and key_press. This lets the agent drive a disposable VM's UI directly with its own model, without touching the real desktop or running a separate agent loop.

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 read-only monitoring and diagnostics of Apache Flink clusters through a JobManager REST endpoint, including job, checkpoint, backpressure, TaskManager, and multi-cluster overviews with LLM-friendly summaries.

Provides OAuth-secured MCP tools for working with private, Git-backed Markdown knowledge bases: searching pages, reading content, ingesting new source documents, retrieving cited original files, and reviewing history or rolling back accepted pages. Every tool call is checked against each person's or agent's current wiki grant and returns source paths and Git revisions for traceability.

Enables LLM clients to perform sandboxed file operations, manage persistent notes, fetch live weather and Wikipedia summaries, calculate arithmetic safely, and retrieve current UTC time via a standalone tool server over stdio or HTTP.

A zero-dependency MCP suite exposing computational tools across metabolic, cardio-renal, chronobiology, neuro-recovery, and biomechanics domains. It enables clients to derive longevity and physiology metrics such as autophagy index, glucose-ketone ratio, glycemic variability, peripheral resistance, nocturnal BP dipping, DLMO clock time, melanopic lux, caffeine clearance, EEG fatigue entropy, and grip vitality age from user-supplied measurements.

Exposes MCP tools that compute longevity and metabolic metrics (autophagy index, GKI, MAGE), cardio-renal biomarkers (BUN/creatinine ratio, cardiac output and TPR, nocturnal BP dipping), chronobiology estimates (DLMO, melanopic lux, caffeine clearance), plus neuro-recovery and biomechanics scores. Runs as a zero-dependency Python JSON-RPC 2.0 stdio server so any MCP-compatible client can query these physiological domain calculators.

Exposes four read-only tools over extracted Hacker News "Who is hiring?" job posts — postings_search, posting_get, skills_top, and skills_trend — so an MCP client or agent can search postings, look up individual posts, rank top skills, or chart skill trends over time. Tools return only structured extracted fields and HN links (never raw post text) from a read-only SQLite database, including salary sorting and a hard row limit to keep queries cheap.

Enables an agent to author, render, and visually verify Manim animations from a described concept, returning evidence and structured verdicts so incorrect renders can be fixed before export.

Enables an LLM client to perform reverse engineering inside IDA Pro without the GUI, opening databases headlessly in persistent worker processes and exposing tools to decompile and disassemble functions, rename symbols, retype variables and arguments, add comments, and convert numeric bases. It can also run over stdio or HTTP transports and adopt an existing GUI session when desired.

Enables any agent to search skill packs with bounded results, fetch one full validated skill on demand, and act without copying directories into prompts.

Enables LLM clients to explore and query a local SQLite database through read-only, typed tools for table listing, schema inspection, ad-hoc SQL, customer lookup, and sales summaries.

Exposes tools that let an agent read a CV, search the Arbeitnow job board for Berlin AI/ML working student roles, score each vacancy against the CV, draft cover letters whose every claim cites CV facts, and queue them for human approval. The same tools can be driven from other MCP clients such as Claude Desktop, with scoring done deterministically in code and limits enforced outside the prompt.

A Claude Code plugin that stops vague prompts before they run. Detects your stack, asks targeted questions, and forges a structured prompt. Zero credits wasted.

Exposes agent-facing tools that let a personal AI agent submit each proposed action for an allow, require_approval, or deny verdict against OWASP PAA10 policy rules, plus tools to record provenance-tagged memories, log refusals, read status, and trigger a self-stop. Its decisions are advisory unless paired with the project's gating proxy or hook, which enforce them.

Enables MCP-compatible apps to expose a folder of RAPP agent.py files as callable tools, deriving each tool's schema from its metadata and running calls in isolated, timed processes.

Enables organizing personal contacts into concentric social layers, estimating warmth decay via an exponential half-life model, and scheduling mindful reconnection touchpoints within a weekly cognitive bandwidth.

Enables users to classify contacts into social closeness layers, estimate relationship warmth decay over time, and schedule intentional reconnections within a bounded weekly cadence.

This MCP server enables users to analyze personal reading and media streams by computing Shannon entropy, detecting Jaccard redundancy and echo chambers, and ranking content sources by decibel SNR to produce pruning recommendations and high-signal curation.

Enables users to audit personal reading streams by quantifying category diversity with Shannon entropy, detecting lexical redundancy and echo chambers via Jaccard similarity, and ranking content sources by decibel signal-to-noise ratio for pruning recommendations.

Enables computing composite glycemic load for multi-ingredient meals, modeling protein and fat attenuation of glucose spikes, and forecasting post-prandial fatigue and stable focus hours to guide meal and task scheduling.

Enables users to aggregate meal glycemic index/load, model protein and fat buffering, forecast post-prandial cognitive crash risk and focus hours, and receive macronutrient stabilization recommendations.

Enables forecasting of 90-minute ultradian rhythm phases, alertness peaks and dips, and restorative nap windows through two-process sleep-wakefulness modeling.