"An Overview of QlikView - Business Intelligence and Data Visualization Tool" matching MCP servers:

Transforms prompts into Chain of Draft (CoD) or Chain of Thought (CoT) format to enhance LLM reasoning quality while reducing token usage by up to 92.4%, supporting multiple LLM providers including Claude, GPT, Ollama, and local models.

Persistent memory for AI agents. Store, recall, and share knowledge across sessions with five MCP tools: remember, recall, context, forget, and share. Includes semantic search and agent/user/org scoping.

An MCP server adapter that allows LLM agents to autonomously play World of ClaudeCraft by wrapping the game's headless environment as MCP tools for state reading and action execution.

Structured reasoning MCP server that decomposes problems into atomic steps (premise, reasoning, hypothesis, verification, conclusion) with confidence scoring, live visualization, and approval feedback.

An MCP server implementation of Anthropic's Think Tool prompt engineering technique that enables Claude to break down complex problems and enhance its reasoning capabilities by providing a simple tool that echoes back thoughts.

Shrimp Task Manager is a task tool built for AI Agents, emphasizing chain-of-thought, reflection, and style consistency. It converts natural language into structured dev tasks with dependency tracking and iterative refinement, enabling agent-like developer behavior in reasoning AI systems.

A server built on mcp-framework that enables integration with Claude Desktop through the Model Context Protocol.

Enables AI agents to debug and repair historical Solaris/SPARC systems by providing an out-of-band control plane with guest DTrace, QEMU monitor access, SPARC-aware GDB, host eBPF/perf tracing, and an immutable evidence ledger for cross-layer hypothesis testing.

An MCP server that implements the 'think' tool, providing Claude with a dedicated space for structured thinking during complex problem-solving tasks to improve reasoning capabilities.

Enables sleeping agents to monitor multiple event sources and wake only when durable composite or temporal conditions are met, supporting derived events and targeted wakeups without keeping an agent loop alive.

Provides a 'reflect' tool that creates cognitive checkpoints for AI assistants, forcing structured step-by-step reasoning through complex problems to improve accuracy and maintain context during task execution.

Enables AI agents to perform workspace file operations (read/write/recover) with transaction-safe guarantees, durable recovery, and explicit human approval via a separate Broker.

An MCP (Model Context Protocol) server that chains calls to other MCP tools, reducing token usage by allowing sequential tool execution with result passing

Provides image recognition capabilities to Codex/Claude Code by routing 'look at screen/screenshot' requests to a multimodal model via an API relay. Enables UI automation agents to analyze local screenshots and return descriptions or structured JSON coordinates.

Provides a no-op 'think' tool that enables Claude to decompose complex problems and enhance reasoning by pausing to organize thoughts.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

Implements Anthropic's 'think' tool for Claude, providing a dedicated space for structured reasoning during complex problem-solving tasks that improves performance in reasoning chains and policy adherence.

Domain-expert SMB sales playbooks for AI agents. Discovery questions, objection handlers, cold email + LinkedIn DM templates, BANT/MEDDIC frameworks, closing tactics. Built by an ex-Criteo (268% quota) / ex-Deel ($12B) / ex-HBO / ex-Bloomberg enterprise AE. Use when your AI SDR needs real human-tested sales artifacts.

A Model Context Protocol server that provides Claude with a dedicated space for structured thinking during complex problem-solving tasks, helping improve its reasoning capabilities.

Evidence linting for AI answers: flag broken references, unmatched quotes/numbers and evidence gaps before human review. Local Python/stdio: 32 tools. Hosted Output Verification has a separate MCP catalog: https://output-verification.agendamcp.dpdns.org/mcp/agent. Start free with a saved synthetic example at https://output-verification.agendamcp.dpdns.org/. No factual-truth certification.

A multi-agent orchestration system that enables multiple Claude instances to collaborate through a centralized hub with a shared workspace and real-time communication. It features integrated task management, role assignment, and persistent memory to facilitate complex, synchronized agent workflows.

Implements the Chain of Draft reasoning approach to generate minimalistic intermediate reasoning outputs while solving tasks, significantly reducing token usage while maintaining accuracy.

This is a powerful Model Context Protocol (MCP) server that integrates multiple AI coding agents—Anthropic Claude Code, OpenAI Codex, and Google Gemini—directly into your workflow. It enables seamless cross-provider analysis, leveraging Gemini's massive token window, Codex's specialized coding capabilities, and Claude's advanced reasoning.

MCP server that builds itself by creating new tools as needed based on user requests (Requires restart of Claude Desktop to use newly created tools).

A Model Context Protocol server that enables AI assistants to interact with Google Gemini CLI, allowing them to leverage Gemini's large token window for analyzing files and codebases using natural language commands.

Enables on-demand generation of TypeScript tools using an LLM with human-in-the-loop approval and safe sandboxed execution. It allows users to create and persist custom functionality through natural language requests.

An MCP server that uses Groq's API to expose raw chain-of-thought tokens from Qwen's qwq model, enabling LLMs to think step-by-step before responding.

Enables AI assistants to interact with multiple AI models through the OpenCode CLI with a unified interface. Supports plan mode for structured analysis, flexible model selection, and natural language queries with file references.

Filters and blocks specific tools from upstream MCP servers using regex patterns to reduce context pollution and remove unwanted tools from AI agent interactions.

Enables an Alexa+ agent to check payment, access, and credential requests for fraud or social engineering before acting, using deterministic rule scoring with Bedrock-assisted signal extraction and human approval for held or reviewed cases.