"Information about backend development or backend systems" matching MCP servers:

A specialized toolchain that guides AI agents through a structured 'Atomic Development' workflow for building Python FastAPI and Supabase backends. It manages project scaffolding and enforces dependency-ordered generation of database models, API routes, and tests.

Validates startup ideas with a deterministic scorecard, evidence brief, and verdict before code is written, integrating with MCP-aware build agents to avoid building dead-on-arrival products.

A comprehensive framework for building reliable Python MCP servers that enforces incremental development practices through Cursor Rules, featuring built-in security measures and a smart rule system.

Enables AI agents to manage personal expenses via natural language through add, query, summarize, and delete expense tools.

Turnkey on-premise private Agentic AI on NVIDIA DGX. Query specs, model CapEx ROI & join Partner Program.

Enables AI agents to interactively explore PDDL planning problems by exposing a PDDL engine as MCP tools for initialization, action execution, state inspection, and goal checking.

Enables the creation and execution of task-specific AI sub-agents defined in markdown across any MCP-compatible tool like Cursor or Claude Desktop. It integrates with execution engines such as Claude Code, Cursor CLI, and Gemini CLI to provide portable and reusable specialized agent workflows.

MCP server that keeps language server sessions warm and routes multiple languages through one process. Agents get persistent cross-file awareness, speculative execution (simulate edits before writing to disk), and 20 skills that encode correct multi-step operations like safe rename, blast-radius analysis, and end-to-end refactoring. Single Go binary, no runtime dependencies.

Provides human-like memory dynamics for AI assistants where memories naturally fade over time unless reinforced through use, mimicking the Ebbinghaus forgetting curve. Enables automatic saving, searching, and management of contextual information with temporal decay algorithms.

Runtime decision firewall for AI agents that exposes the CSC engine as an MCP server, allowing agents to consult before executing risky actions.

Gates agent tool execution with human approval, audit trails, and replay-resistant permits, enabling safe use of tools in agent loops.

Governance primitives for autonomous agents. Verify actions against policy, record signed provenance, and bind intents cryptographically. Free tier available.

Enables AI agents to investigate backend incidents by executing runbooks that gather evidence from observability and storage systems.

Enables agents to get fast, calibrated probabilistic answers from Jev (Typesafe AI) to yes/no, scale, or choice questions about provided material, without using a generative model.

A generic OpenAI-compatible subagent MCP server that lets Claude Code delegate coding grunt-work to a local model via a tool-calling agent loop, with configurable concurrency and backend.

An improved implementation of persistent memory using a local knowledge graph with a customizable --memory-path. This lets Claude remember information about the user across chats.

Gives AI agents verifiable, tamper-evident receipts for their actions — attest_action signs a cryptographic receipt for what the agent did (email sent, payment made, form filed), verify_receipt checks it offline, get_identity returns the agent's did:key. Sign locally, verify anywhere, zero backend.

An MCP server that enables AI agents to pause and request human approval or information via Slack, Telegram, or macOS dialogs before proceeding with actions.

Connects MCP-compatible clients to an already-deployed DeerFlow instance over its HTTP API, exposing deep research runs, model listing, and thread, run, and artifact control as callable tools. It acts as a thin stateless adapter, so clients can launch cited research reports and manage DeerFlow workflows without running the backend themselves.

Provides comprehensive tools for React Native development, automating project initialization, version management, upgrades, Expo integration, and development workflows through AI assistance.