"Lottery games and results" matching MCP servers:

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.

An MCP server that enables Claude Code and Claude Desktop to generate and edit images using OpenAI's gpt-image-2 model, with results saved to disk.

Stop hallucinating numbers. An MCP server that runs every math expression through four independent numeric engines in parallel and returns all four results side by side — so when they disagree, you know the answer is numerically unreliable.

MCP server for SimplyPNG background removal APIs. Enables AI agents to remove backgrounds from images, estimate credits, check job status, and download results programmatically.

Lets MCP clients delegate tasks to the Google Antigravity CLI, including starting and continuing conversations and listing models, while returning structured results over stdio.

Manages brand monitoring projects, runs scans across 350+ LLMs, and analyzes results through natural language.

Generates images via any OpenAI-compatible API, auto-detecting image models and saving results to disk with optional inline previews.

Enables production-grade answer verification for LLM agents by independently re-checking answers with a configurable verifier model, enforcing confidence policies, and producing Ed25519-signed, auditable verification results with optional web search and knowledge-base evidence.

Enables AI agents to analyze images via user-configured cloud vision APIs (Gemini or OpenAI-compatible), returning structured results such as summaries, OCR text, and objects.

An MCP server that grades prompts against a validated rubric, tracks real outcomes, and provides guidance grounded in research on what predicts good results.

Enables coding agents to query files, logs, and search results through TypeSafe Jev's typed, probabilistic answers, so they retrieve only the needed conclusion instead of raw context. Supports classification, scoring, yes/no checks, extraction, page ranking, and injection screening.

Enables AI assistants to obtain exact credit quotes, create posters after explicit approval, and retrieve owned results through Poster.sh.

An MCP server that lets an agent read its own eval results, run suites, explain failures, check drift, and view judge calibration status.

Enables AI agents to generate game-ready 3D assets from reference images with PBR textures, and to retexture meshes the user already owns, while recording full provenance for every generated file.

Keyless, local MCP server bringing ISTQB / OWASP / IEEE / ISO / EU AI Act QA standards into your AI client. Standards-grounded retrieval, deterministic QA effort estimation, automated QA document quality review (0-100 rubric), and JUnit/CSV test-results flakiness analysis.

Enables AI-assisted aquaculture manuscript writing through six specialized agents for literature review, drafting, results, abstract, copyediting, and integrity checks, all accessible from MCP-compatible chat clients.

Enables Claude to generate and edit images through a live-discovered model catalog, including masked edits and exact output sizes, with browser-based sign-in and cost-annotated saved results. It runs locally over stdio and stores images in a configurable directory.

Lets AI assistants browse live model catalogs, compare prices, create generation tasks, and collect results, with confirmation for every billable step.

Provides twelve read-only tools that report how often ChatGPT, Claude, Gemini, Perplexity and Google AI Overviews name and cite a brand, covering visibility, share of voice, sentiment, competitor mentions, sources, alerts and ranked recommendations with the underlying answers. It lets users query these measurements directly from Claude, Cursor or any MCP client, with denominators and null-handling rules included so results are interpreted correctly.

Enables MCP-compatible clients to discover Maginary's image/video generation flags, submit generation requests, and poll for results, including support for API-key and OAuth authentication.

Enables a local STDIO MCP client to submit tasks to your own Notion personal AI chats, resume reading results by job ID after timeouts or disconnects, and send follow-up corrections in the same thread. Full long-form answers, code files, and SHA-256 hashes are saved to local files so complete outputs can be retrieved rather than just short previews.

Enables building and running custom LLM benchmarks with multi-judge evaluation, supporting GUI, MCP client, and CLI usage for ranked, auditable results.

Enables private, one-shot image generation and editing via a self-hosted ComfyUI, with a simple model-agnostic tool surface and results delivered directly to the client without server-side storage.

Manages a local fleet of llama.cpp GGUF models, enabling AI agents to discover, start, stop, test, and query evaluation results for llama-server processes.

A hybrid AI chess coach MCP server that uses Stockfish for grounded evaluation and LLM for natural-language coaching, enabling game analysis, weakness diagnosis, and personalized drills from your own games.

MCP server for MarkItUp's AI image-annotation pipeline. Generate polished marketing-visual variations of any screenshot, regenerate, AI outpaint, and remove backgrounds — powered by Claude analysis + Gemini rendering.

Enables image generation, editing, and composition using any OpenAI-compatible Images API within MCP clients, Dify, and n8n, delivering results as image blocks, structured fields, or resource links.

Enables any stdio MCP client to send evidence and allowed answers to a locally running Nimble model through Ollama and receive typed, validated results as choices, probabilities, or rubric scores. Supports three question types and includes diagnostics that check configuration, endpoint, and model availability without running inference.

Enables LLMs to search open-source AI models by semantic meaning using directional coordinates and anchors, returning scored, ranked results in under 100ms.

Enables agents to run image A/B preference tests with human participants, retrieve live pricing and availability, create and purchase studies within configured USDC spending limits, and check vote results.