"American Express" matching MCP servers:

Enables AI assistants to discover and invoke generated CRUD tools and read-only resources, letting them interact directly with REST-backed services built on Express, Mongoose, or Sequelize.

Multi-jurisdictional legal AI MCP server for Spanish, Latin American, and European law. 11 tools: analyze, audit, draft, jurisprudencia search (CENDOJ ~141k + Colombian courts ~106k), cross-border comparison, Monte Carlo litigation simulation, doctrina, redteam, and more. ISO 31000 certainty locks. Zero Retention. GDPR/LGPD compliant. Install: npx -y @nexus-legal/mcp

Enables text-to-image generation and image editing or composition with Google's Gemini image models via MCP tools, prompts, and resources. Supports automatic model selection, aspect ratio and resolution control, curated prompt templates, and three credential modes (Gemini API key, Vertex AI Express Mode, or ADC).

Enables AI assistants to discover and invoke CRUD tools and read-only resources over the Model Context Protocol for generated API services.

Enables TypeScript developers to build Model Context Protocol servers by declaring tools, resources, resource templates, prompts, and roots through a concise Express-like or experimental decorator API that infers argument types and input schemas automatically. It also exposes MCP capabilities such as logging, progress reporting, and resource reading via a context object passed into handlers.

Enables developers to build TypeScript MCP servers that expose tools, prompts, resources, and resource templates with minimal boilerplate via Express-like or decorator APIs.

Enables developers to build TypeScript MCP servers with minimal boilerplate by declaring tools, prompts, resources, and resource templates through an Express-like or decorator API.

Enables developers to build Model Context Protocol servers in TypeScript with minimal boilerplate, declaring tools, prompts, resources, resource templates, and roots through either an Express-like API or an experimental decorators API that infers types and input schemas automatically. It also provides a context object for logging, progress reporting, and reading resources within tool handlers.

Enables coding agents to inspect, patch, validate, diff, and execute RunningHub / ComfyUI workflows through structured graph tools (13 total) over MCP, routing parameter changes to nodeInfoList and topology changes to full workflow JSON, and returning structured browser-fallback requests when only the UI can express an operation.

Enables developers to build and serve Model Context Protocol servers in TypeScript with minimal boilerplate, supporting tools, resources, resource templates, prompts, and roots via an Express-like API or experimental decorators.

Enables developers to create and serve Model Context Protocol servers in TypeScript by declaratively defining tools, resources, resource templates, prompts, and roots with minimal boilerplate. It provides an Express-like API or experimental decorators, with optional context access for logging and progress reporting.

Enables developers to build Model Context Protocol servers in TypeScript with a simple Express-like API or experimental decorators for defining tools, prompts, resources, resource templates, and roots with minimal boilerplate.

Enables developers to build Model Context Protocol servers in TypeScript with minimal boilerplate by declaring tools, prompts, resources, resource templates, and roots through either an Express-like API or an experimental decorators API. It hides protocol plumbing, infers argument schemas from TypeScript annotations, and exposes MCP capabilities like logging and progress reporting via a context object.

Enables LLMs to interact with generated CRUD services through MCP tools and resources, so they can create, update, list, and fetch records via a standard protocol. It also provides REST API access from the same codebase for traditional clients.

Enables TypeScript developers to build Model Context Protocol servers by defining tools, prompts, resources, resource templates, and roots with minimal boilerplate, using either an Express-like API or experimental decorators.

Agentic AI instruction encoding. 86.8% smaller than JSON. Inference-free decode by table lookup. 342 opcodes, 26 namespaces, three conformant SDKs. Confirmed over LoRa mesh radio. Apache 2.0 with express patent grant.