"A resource for solving equations" matching MCP servers:

Stop paying for your agent to rediscover what other agents already figured out. Prior is a shared knowledge base where agents exchange proven solutions — one search can save 10 minutes of trial-and-error and thousands of tokens. Your Sonnet gets access to solutions that Opus spent 20 tool calls discovering. Search is free with feedback, and contributing earns credits.

Local memory for coding agents that can refuse, not only recall. A rule carrying a machine-checkable proof blocks the matching command before it runs, while prose-only rules can warn but never block.

Provides a read-only interface to audit and continue coding agent sessions by extracting plans, intents, and edit authorship from history across multiple agents (Claude, Codex, OpenCode, Antigravity, Pi) via MCP, CLI, and Python SDK.

Enables MCP-compatible agents to answer questions about you by exposing your CV through a tool, resource, and prompt.

Provides AI assistants with a hierarchical task management system that maintains focus and context across complex problem-solving sessions, solving context window limitations through organized task structures.

Provides a compressed knowledge graph of the NVIDIA AI developer stack for deterministic traversal, enabling agents to answer questions about dependencies and prerequisites with minimal tokens.

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.

A minimalist and elegant Model Context Protocol (MCP) server for fast context memo capture, keyword search, dynamic resource digests, and AI prompt workflows.

Enables complex problem-solving through multi-modal reasoning, dynamic knowledge graphs, meta-cognitive reflection, and collaborative AI personas.

Gives agents a stable interface for durable memory, combining exact, lexical, semantic, and graph search results.

Provides a shared, persistent memory layer for AI assistants, letting them read and write to a local Obsidian vault through MCP with validation, secret rejection, and deduplication.

Provides a compiled knowledge substrate, extracting atomic claims from an append-only capture log and querying a bitemporal claim graph over MCP. Read-only by default, with opt-in writes for trusted sources.

An MCP server for managing work logs, research results, and task checkpoints to enable seamless collaboration and state recovery between AI agents. It provides a persistent memory layer for tracking project history and resuming workflows across different sessions or tools.

Enables problem-solving using a structured wisdom framework inspired by the Lotus Sutra, combining analytical thinking with intuitive wisdom through multi-faceted contemplative approaches and meditation pauses.

A custom Memory MCP Server that acts as a cache for Infrastructure-as-Code information, allowing users to store, summarize, and manage notes with a custom URI scheme and simple resource handling.

A note-taking knowledge base MCP server that enables CRUD operations on notes with tags, resource listing/detail, and prompt-based analysis and brainstorming.

Provides a scalable knowledge graph implementation for Model Context Protocol using Elasticsearch, enabling AI models to store and query information with advanced search capabilities, memory-like behavior, and multi-zone architecture.

Provides 10 structured reasoning strategies (Chain of Thought, ReAct, Tree of Thoughts, etc.) for complex problem-solving with session persistence, branching, and tool integration capabilities.

Provides AI assistants with enhanced reasoning capabilities through structured thinking, persistent knowledge graph memory, and intelligent tool orchestration for complex problem-solving.

An MCP server implementing the Unified Cognitive Processing Framework for advanced problem-solving, creative thinking, and cognitive analysis through structured tools for knowledge mapping, recursive questioning, and perspective generation.