"Using an MCP server to generate structured ADK agents" matching MCP servers:

An enhanced MCP server that enables users to interact with NotebookLM while ensuring strict source fidelity and citation accuracy through client-side prompt structuring. It guides Claude to transform user queries into structured prompts that prevent the addition of external knowledge during document analysis.

An MCP server that integrates AI agents with Figma, enabling reading and programmatic modification of designs.

Enables AI agents to directly control Figma Desktop via MCP, supporting UI creation, editing, prototyping, and variable management with over 60 tools.

An offline MCP server for SAP ABAP that provides static analysis, ABAP Cloud readiness checks, and RAP scaffolding using abaplint, enabling AI agents to analyze and generate ABAP code without a live SAP system.

An MCP server that enables AI assistants to perform web searches on Perplexity.ai using browser automation instead of an official API. It supports persistent authenticated sessions and returns search results along with cited sources directly to the client.

MCP server for B2B lead generation and company search using the Generect Live API. Provides tools to search leads, companies, generate emails, and get lead by URL.

An intelligence layer for discovering and orchestrating Japanese SaaS MCP tools through intent-based search and workflow recipes. It enables AI agents to evaluate service quality using community-driven insights while ensuring privacy through automated PII masking.

An MCP server that detects duplicate code using AST graph isomorphism, blocking writes of structurally identical code to reduce redundancy.

Universal MCP server for the Avito API (Russia's largest classifieds marketplace), built for autonomous AI agents to operate an account hands-free — 145 tools across 18 domains (listings, messenger, orders, delivery, promotion, autoload, reviews, analytics). Safe-by-default: dry-run, idempotency, structured errors, confirmation flow.

Enables local AI agents to generate and edit Figma designs via the Plugin API through a WebSocket relay, with no rate limits and all communication remaining local.

Enables AI assistants to read and modify Figma designs programmatically through the Model Context Protocol.

A lightweight MCP server that enables AI assistants to execute local development tools and retrieve system status with low latency over stdio or HTTP.

An MCP server that provides AI clients with access to developer documentation via llms.txt files. Exposes tools, resources, and prompts.

Deterministic encode, decode, hash and convert for agents, on the same pipeline engine as the String Utility Belt web app, CLI and editor extensions: a pipeline built and checked in the browser runs unchanged from its share link. Base64/hex/URL, JWT, hashes, JSON/YAML/CSV/XML, dates and regex; tools to search, describe and run utilities or whole pipelines, and to detect input formats. Runs locally

Enables AI assistants to perform Google searches with real, resolvable sources and transparent search terms.

Lets AI assistants discover public Socrata open-data portals, search their datasets, inspect schemas, and run bounded read-only SoQL queries to answer questions with live government data. All access is strictly read-only and results come straight from the publisher's portal.

Enables AI models to access and search offline Wikipedia and other knowledge bases stored in ZIM format files. Provides intelligent content retrieval, structured browsing, advanced search capabilities, and metadata extraction for comprehensive offline knowledge access.

Read-only MCP server for Grafana Loki: lets AI agents (Claude Code, Cursor, Copilot) discover labels, read bounded LogQL pages, count lines by label or time, and export logs — across several Loki stands

Enables multi-level reasoning with configurable depth levels, session-based state management, structured thought input, and real-time trace resources.

Relays error logs from other MCP servers when an LLM call fails and the returned error message is unclear, helping the model handle errors more intelligently.