"A list of all MCP items" matching MCP servers:

Enables coding agents to add a bounded semantic-judgment layer for routing, ranking, extraction, verification, and escalation, returning typed signals and review recommendations.

Enables any MCP-capable agent to generate images in the cloud without a GPU, local models, or downloads, using the user's own API key for providers such as DashScope's Wan/Qwen image models and Volcano Ark's Seedream. It can list which channels are configured and return generated image links or save the files to a local directory, all locally over stdio.

Enables querying multiple AI models simultaneously and returns all answers unmerged for the user to weigh.

Enables users to create, inspect, list, and delete per-user JupyterLab servers on the UChicago ATLAS Analysis Facility Kubernetes cluster, with GPU availability and supported image listing tools.

Enables AI assistants to see which model IDs each provider's API has started listing since the last check, and to list the models a given provider currently serves with optional substring filtering, all without spending tokens.

MCP server for hybrid medical consultation integrating Western and Traditional Chinese Medicine, enabling symptom analysis, diagnosis, and treatment suggestions through two specialized models.

A local, fully vectorized computational engine for stock market analysis that enables AI to perform factor calculation, strategy backtesting, IC analysis, and GPU-based multi-dimensional visualization using local DuckDB data.

Enables AI agents to generate images with the Z-Image-Turbo text-to-image model, list available models, and browse past generations over Model Context Protocol. Supports stdio, SSE, and Streamable HTTP transports with identical structured content across all of them.

A Model Context Protocol (MCP) server that enables structured multi-path investigations using a Tree of Thoughts approach.

Enables AI agents to generate images from text prompts with the Z-Image-Turbo model, list available models, and browse past generation history. It supports local stdio entrypoints plus SSE and Streamable HTTP transports, returning identical structured content across all of them.

Dynamic multi-agent debate MCP server for structured argumentation and reasoning among AI agents.

Enables agents to detect and redact PII and secrets in text, HTML, and PDF documents before they reach an LLM, with reversible placeholder, mask, or hash redaction, and to list supported detectors and redaction styles.

Five-layer agent memory over MCP. zenbrain_store routes each memory: steps to a procedure, emotional weight to an episode, high confidence to a pinned core memory, the rest to a semantic fact. zenbrain_recall returns results with id and layer; zenbrain_forget deletes one plus what consolidation linked to it; zenbrain_consolidate runs one sleep-like pass. Local SQLite, no account, no network call.

Provides AI assistants with real-time web search, reasoning, and research capabilities through Perplexity's Agent API and Search API, enabling tools for search, conversational Q&A, deep research, and advanced reasoning.

Provides local, deterministic scoring and detailed auditing of text for AI-writing patterns via two read-only tools, with no network calls or language models.

Enables AI agents to access Cloudflare Workers AI for serverless LLM chat completions, text embeddings, and image generation via MCP tools.

Predicts the outcome of Dutch court cases dismissed, partly granted, or granted using a model trained on 609,715 rulings, with the outcome leakage measured and removed: 92% of Dutch judgment texts state the outcome verbatim, 0.1% remains after the cut, giving 78.2% accuracy against a 43.7% majority baseline. The lekkage_check tool exposes that same measurement so anyone can test a datase

17 decision intelligence algorithms as MCP tools for AI agents. Bandits (UCB1, Thompson), LP/MIP solver (HiGHS), Monte Carlo simulation, Bayesian inference, graph analytics (PageRank, Louvain), genetic algorithms, CMA-ES, anomaly detection, time series forecasting, and more. All under 25ms, deterministic, zero LLM cost.

Enables sending a shared context with multiple independent questions to TypeSafe's Jev model via the public OpenJEV API, returning typed judgments and probabilities (choice, score, or yes/no) instead of prose.

Enables MCP clients to generate images and iteratively edit them using OpenAI's gpt-image-2 models, with session management, background job polling, and local file saving.