An MCP server that offers 15 specialized tools to evaluate and improve the accuracy of RAG based systems.

MCP server and free JSON API for US election forecasts, with a public accuracy record

A deterministic tool-calling framework for small LLMs. Integrates SymPy and sandboxed Python to achieve better accuracy/latency without massive parameter counts.

Context Sherpa MCP server provides code mapping, deterministic feedback loops, and tiered inference for AI coding agents to increase accuracy and optimize token usage.

LLM Output Quality Evaluation u2014 quantify, evaluate, and track LLM response quality: relevance, accuracy, tone, hallucination scoring, and consistency checks

Perceptron MCP server for high-accuracy visual perception powered by fast, efficient vision-language models

Docs-Agent leverages AI to avoid outdated documentation, fix issues related to comprehensiblity or technical accuracy.

Compare classifier confidence with accuracy using your own predictions. Browser-based calibration analysis and reports.

Long-term conversational memory system for AI agents — cell-based architecture with hybrid BM25 + semantic retrieval. 96% accuracy on LoCoMo benchmark.

Manage and simulate user interactions in modern web apps to improve testing accuracy and reliability with UserDispatch MCP.

A chatbot that converts text questions into SQL queries and runs them against a company expense database. Uses RAG to retrieve relevant SQL examples and improve query accuracy. Built with FastAPI, Ollama, MCP, and SQLite.

Rust MCP server for lossless agent-context compression, reducing token usage while preserving retrieval accuracy.

Verify AI outputs in real-time to ensure code and data accuracy before execution with this Model Context Protocol server.

Fixes accessibility violations and proves it. Multi-engine WCAG detection routed by measured accuracy, verified minimal-diff repairs, and a published error rate.

Your portable AI memory — preferences, decisions, and corrections that follow you across models, tools, and devices. Zero-dependency, local-first, 85% PrefEval accuracy.

SwiftLens is a Model Context Protocol (MCP) server that provides deep, semantic-level analysis of Swift codebases to any AI models. By integrating directly with Apple's SourceKit-LSP, SwiftLens enables AI models to understand Swift code with compiler-grade accuracy.

Semantic Intelligence for Large-Scale Engineering. Context+ is an MCP server designed for developers who demand 99% accuracy. By combining RAG, Tree-sitter AST, Spectral Clustering, and Obsidian-style linking, Context+ turns a massive codebase into a searchable, hierarchical feature graph.

Authenticated, self-hosted WhatsApp MCP server that serves 96 tools for the token cost of 29, with a labeled eval for tool-retrieval accuracy. Go bridge + Python FastMCP.

Model Context Protocol (MCP) server for Indian equity (NSE) analyst consensus targets, horizon accuracy benchmarks, and SEBI-verified research analyst directories on StockTipsArena.

OCR + AI = Magic: Extract data from any image in a consistent structure with fine-tuned vLLMs for the highest accuracy. Learn more about AI backed OCR here: https://jigsawstack.com/docs/api-reference/ai/vocr Get your jigsawstack API key here: https://jigsawstack.com/dashboard/

Your AI forgets everything between sessions. This fixes that — 98%+ retrieval accuracy, 100% on LongMemEval, 99% token savings. 44 MCP tools. Fully local, zero cost.

Context-aware skill selection for AI agents — solves the 'too many skills' problem. Reduces skill context tokens by 85-98%, improves selection accuracy, learns from feedback.

Multi-AI consensus tool: MCP server that queries multiple AI models (OpenAI, Claude, Gemini, custom APIs) in parallel, synthesizes responses for better accuracy, and reduces AI bias through ensemble decision-making

Nexus-MCP solves Tool Space Interference in LLMs. It uses a single gateway to aggregate multiple MCP servers and employs a deterministic 4-phase workflow (Discovery, Mapping, Schema Verification, Bridged Execution) to prevent context saturation and tool hallucinations, maintaining reasoning accuracy with massive tool ecosystems.

CricketStudio MCP server — 60 tools across 7 leagues: IPL 2026, IPL historical (18 seasons), MLC, BBL, PSL, T20 WC, WPL. 2,654 matches · 623,535 deliveries. LLM accuracy: 0% → 85% on compound cricket questions. Citation-grade claims with provenance to bal

Auditable memory layer for AI agents: zero-LLM-call local ingest (~10ms/msg, air-gapped), matches Mem0 on accuracy at ~1000x lower ingest cost, bi-temporal belief-state, MCP server. Honest LoCoMo/LongMemEval benchmarks. Open source (Apache-2.0).

Multi-AI consensus MCP server that queries multiple AI models (OpenAI, Claude, Gemini, custom APIs) in parallel and synthesizes responses to reduce bias and improve accuracy. A Python implementation of the wisdom-of-crowds approach for AI decision making.

With our real time AI Translation, you can translate any text, & images conversation, or document to any language while maintaining the context, tone, and meaning with AI enabling high accuracy and reliability. We support 180+ language pairs! Check out the list of languages we support here: https://jigsawstack.com/docs/additional-resources/languages You can get your jigsawstack key here: https://jigsawstack.com/dashboard/

Open source AI data analyst that answers questions about your data in plain English. Governed by a dbt semantic layer, runs locally on DuckDB, refuses to guess when it is unsure, and publishes execution accuracy on every commit. Text to SQL you can put in a board deck.

MCP Chain of Draft (CoD) Prompt Tool is a BYOLLM MCP (Model Context Protocol) tool that transforms your prompt using another LLM, applying CoD or CoT reasoning techniques, before delivering the final result. CoD is a novel paradigm that allows LLMs to generate minimalistic yet informative intermediate reasoning outputs while solving tasks, significantly reducing token usage while maintaining accuracy. For fast setup -> scroll ↓ click "Connect"! --------------------------------------------------------------------------------------------- DO NOT be daunted by all the configurations—you can simply scroll down and hit “Connect” to get started effortlessly.

Convert DWG/DXF drawings into reviewable GIS and QGIS delivery with source hashes and validation evidence. CAD2GIS 0.8.0 exposes 63 local MCP tools; four wrappers add 15 verified optional CAD tools for source-preserving 2D edits through an isolated MIT runtime. Supports Codex, Claude Code, Cursor and VS Code through repository plugins or stdio MCP configuration. Original code: Apache-2.0. Requires uv and a selected local project root; optional CAD runtime is installed separately. Demo cases remain CONDITIONAL; absolute surveying accuracy is not independently verified. 中文：把图纸转换为可以审查和交接的 GIS 成果，保留原图、记录修订，并将 CAD 编辑返回统一的 GIS 检查流程。安装与中英文 Wiki：https://mola-maker.github.io/CAD2GIS/wiki/

MCP server for PocketLantern — blocker-aware decision layer for AI coding agents, grounded in source-linked, time-sensitive facts

Fact-check and fix AI outputs. Catches hallucinations, repairs broken JSON, corrects errors — before they reach users.

.NET SQL Agent MCP server featuring raw SQL input, strict AST validation, and an embedded Admin UI. Eliminates LLM hallucinations and security risks across 6 major databases.

The self-improvement layer for AI agents. Ratel reads your agent's traces, finds what fails, recommends fixes and runs evals on real sessions before you ship. Self-hosted: one app, one Postgres. Includes the TypeScript and Python SDK