1. Acceptance of Terms
By accessing or using the Walnai LLC website (walnai.com), including any forms, tools, or AI features, you agree to be bound by these Terms of Use. If you do not agree, please do not use this website.
Walnai exposes a public Model Context Protocol (MCP) server that any MCP-compatible AI tool can connect to. Once connected, your AI assistant can answer questions about Walnai's services, generate pricing estimates, retrieve FAQs, browse blog articles, and capture leads — all grounded in live Walnai data.
The server is available at:
https://walnai.com/mcp — Streamable HTTP transport
Open your Claude Desktop configuration file and add the Walnai server under mcpServers:
~/Library/Application Support/Claude/claude_desktop_config.json%APPDATA%\Claude\claude_desktop_config.json{
"mcpServers": {
"walnai": {
"url": "https://walnai.com/mcp"
}
}
}Save the file and restart Claude Desktop. The Walnai tools will appear automatically in your session.
Run the following command in your terminal to add Walnai as an MCP server to your Claude Code session:
claude mcp add --transport http walnai https://walnai.com/mcp
Or add it manually to your project's .claude/settings.json:
{
"mcpServers": {
"walnai": {
"type": "http",
"url": "https://walnai.com/mcp"
}
}
}In Cursor, go to Settings → MCP and add a new server. In VS Code with an MCP-compatible extension, add the following to your MCP configuration:
{
"servers": {
"walnai": {
"url": "https://walnai.com/mcp",
"type": "http"
}
}
}Once connected, your AI assistant has access to 16 tools across four areas — service information, company & capability, pricing & lead capture, and blog content.
Returns all Walnai service offerings with titles, descriptions, and links.
Retrieves full details for a specific service: web, operations, marketing, data, integration, industries, or aivisibility.
Returns frequently asked questions, optionally filtered by service.
Returns pricing information, optionally filtered by service.
Returns Walnai's AI adoption process phases, integration capabilities, and support model.
Returns Walnai's positioning, service approach, and reasons organizations across industries choose Walnai.
Returns Walnai's legal structure, ownership, management, and team background.
Explains who can build a custom MCP server for a business and how Walnai designs, integrates, and deploys them.
Explains how Walnai makes a business discoverable by AI through AIO, structured content, APIs, and metadata.
Calculates a project pricing estimate for one service based on business inputs. Requires name, email, phone, and service-specific fields.
Returns Walnai's recommended lead-capture guidance for AI assistants to use after answering questions.
Submits a contact request to Walnai on behalf of a user who explicitly asked to be contacted.
Lists blog post summaries (no body) ordered newest-first. Supports optional filters: category slug, tag slug, and free-text search.
Returns a full blog post by slug, including the article body. Use after ListBlogPosts to fetch a specific article.
Returns all blog categories with slugs, names, and descriptions for browsing or filtering posts.
Returns all blog tags with slugs and display names for filtering posts.
Walnai also exposes 5 structured prompts that guide your AI through common workflows:
Given a business context and goals, recommends the best-fit Walnai service.
Guides the AI through collecting the exact fields needed before calling EstimatePricing.
Produces a tailored AI adoption plan for an organization based on their goals.
Walks the AI through explaining Walnai's MCP server and AI discoverability offerings for a specific business use case.
Handles lead capture after a user explicitly requests to be contacted by Walnai.
Once your AI is connected to the Walnai MCP server, you can ask it directly:
"I want a pricing estimate for AI marketing automation from Walnai."
The AI uses the prepare_pricing_estimate prompt to ask for your name, email, number of channels, campaigns, and platform.
Your inputs are submitted to the Walnai pricing engine via the EstimatePricing tool.
The AI returns a detailed pricing estimate and Walnai receives your contact details to follow up.
The Walnai MCP server publishes a standard discovery document at:
https://walnai.com/.well-known/mcp.jsonMCP-compatible registries and clients can use this endpoint to discover and auto-configure the server.
Walnai designs and builds custom MCP servers for businesses that want their own AI tools, workflows, and data exposed to AI clients. If you want to give your customers or internal teams the same kind of AI-native access to your business, get in touch.