Enables LangChain ReAct and deep agents to manage WhatsApp sessions, chats, contacts, and messages—including sending text, images, and files—through MCP tools backed by a local WAHA instance.

Enables authorized AI clients to search locally stored WeChat conversations, media, and attachments, retrieve original-message context, and cite evidence through a local stdio MCP interface.

Enables database interaction through GridDB, including executing SQL queries, listing tables, describing schemas, and optionally performing writes and table creation.

Enables MCP clients such as Claude to access selected read-only Garmin activity and step data plus MyFitnessPal diary data through one self-hosted authenticated endpoint.

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 MCP clients to search job listings, read complete job descriptions, manage local search presets and candidate pools, and prepare application or message drafts. Optional session-backed chat reading and human-confirmed single-action execution are also supported.

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.

Enables forecasting of ultradian alertness peaks and troughs, modeling homeostatic sleep pressure against circadian drive to prescribe restorative nap durations that avoid sleep inertia.

Enables computing physiologically adaptive focus durations and personalized micro-break prescriptions by modeling exponential mental fatigue and Yerkes-Dodson arousal for sustainable work-rest cycles.

Enables agents to call deterministic microservice operations through a native FastMCP JSON-RPC interface, including URL prefix routing, HMAC-SHA256 JWT claims authentication, and P99 latency SLA auditing.

Enables agents to run deterministic protocol and schema synthesis through an MCP STDIO server, including Robinson-type unification, schema lens generation, and graph edit distance scoring.

Enables MCP clients to perform speculative tree-search action prediction and prefetching using Laplace-smoothed Markov transitions, returning deterministic JSON tool results.

Enables deterministic execution, cryptographic security, and game-theoretic optimization through the Model Context Protocol, returning deterministic JSON tool results for agent telemetry and task inputs.

Enables agents to estimate latent user intent, maintain belief states, and resolve ambiguous requests using entropy-based thresholding through MCP tools.

Enables agents to use ACT-R spreading activation memory, semantic association graphs, and automated memory consolidation via the Model Context Protocol.

Enables agents to guard external API calls with circuit-breaker health checks, success/failure reporting, and recovery state inspection, including exponential backoff with jitter and fail-fast rejection.

Enables agents and applications to detect, inspect, and neutralize prompt injection, jailbreak attempts, and hidden instruction escapes in untrusted text before it is passed into prompts.

Enables multiple autonomous agents to safely modify shared workspaces and documents by performing three-way reconciliation, detecting collisions, and returning structured conflict data for automated re-arbitration.