mAIndala

Frequently Asked Questions

Everything you need to know about MCP services, Skills, Agents, and Groups on mAIndala.

What is MCP (Model Context Protocol)?

MCP is an open standard developed by Anthropic that lets AI assistants (like Claude) securely connect to external tools, data sources, and services. Think of it as a universal plug — instead of every AI model needing its own custom integration for every tool, MCP provides one consistent interface that any compliant AI agent can use.

Why was MCP created?

Before MCP, connecting an AI to external data meant building one-off integrations for each combination of model and tool. This created an M×N problem — M models × N tools = enormous duplication. MCP collapses this to M+N: build a server once, and any MCP-compatible AI can use it. Anthropic open-sourced the spec in late 2024 to foster a shared ecosystem.

How does MCP work?

MCP follows a client–server model:
  • MCP Server — exposes capabilities (tools, resources, prompts) over a defined protocol.
  • MCP Client — the AI host application (e.g. Claude Desktop, an IDE plugin) that connects to servers.
  • Transport — communication can happen over stdio (local process), HTTP with SSE, or WebSocket.

The AI discovers what tools a server offers, then calls them by name with structured arguments — much like a function call — and receives structured results it can reason over.


What can MCP servers do?

MCP servers can expose three types of capabilities:
  • Tools — callable functions (e.g. search the web, run a SQL query, send an email).
  • Resources — readable data sources the AI can access (e.g. files, database rows, API responses).
  • Prompts — reusable prompt templates the AI or user can invoke.

Which AI models and apps support MCP?

MCP is supported by Claude (via Claude Desktop and the API), Cursor, Zed, Sourcegraph Cody, and a growing list of third-party AI applications. Because it is an open standard, any developer can add MCP client support to their application. The official MCP site lists official SDKs for TypeScript, Python, Java, Kotlin, and C#.

What is the difference between MCP and function calling / tool use?

Function calling (as seen in the OpenAI or Anthropic APIs) is a model-level feature — you define tools inline in your API request and the model decides when to call them. MCP is a transport and discovery layer on top of that concept. With MCP, tools live on separate servers that can be reused across many models and applications, versioned independently, and discovered dynamically. MCP servers typically surface their tools to the AI via the same underlying mechanism as function calling, but the ecosystem and reusability are far broader.

Is MCP secure? Can I trust MCP servers?

Security is the responsibility of both the server operator and the user:
  • MCP servers run with the permissions you grant them — a file-system server only accesses paths you configure.
  • Always review what a server claims to do before connecting your AI agent to it.
  • Prefer servers from known, reputable providers or open-source projects you can audit.
  • The MCP spec includes an authorization framework (OAuth 2.1) for servers that need user-delegated access.

On mAIndala, community ratings and reviews help surface trustworthy servers. Look for high-rated, well-reviewed listings before connecting.


How do I run an MCP server locally?

Most MCP servers are distributed as npm packages or Python packages. A typical setup looks like:
# Install via npm
npx -y @modelcontextprotocol/server-filesystem /path/to/dir

# Or add to Claude Desktop config (~/.claude/claude_desktop_config.json)
{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "/Users/you/projects"]
    }
  }
}

After restarting Claude Desktop the new tools will appear automatically in the conversation.


What transports does MCP support?

MCP currently supports three transports:
  • stdio — the server is a local subprocess; the client communicates over stdin/stdout. Best for local tools.
  • HTTP + SSE — the server runs as an HTTP service using Server-Sent Events for streaming. Best for remote or shared servers.
  • Streamable HTTP — a newer, stateless variant that works over plain HTTP POST/GET without a persistent SSE connection.

How do I build my own MCP server?

The quickest way is to use an official SDK:
# TypeScript
npm install @modelcontextprotocol/sdk

# Python
pip install mcp

Define your tools with a name, description, and JSON schema for their inputs. The SDK handles the protocol framing. Official quickstart guides are on modelcontextprotocol.io. Once your server is live, you can submit it to mAIndala so the community can discover it.


What is the difference between an MCP server and an AI agent?

An MCP server is a passive capability provider — it waits for the AI to call its tools and returns results. An AI agent is the active decision-maker — it connects to MCP servers, chooses which tools to call, and takes actions to achieve a goal. The agent is typically the LLM plus an orchestration layer (e.g. Claude Desktop, LangChain, or a custom harness).

Are MCP servers the same as plugins or GPT actions?

They serve a similar purpose — extending an AI with external capabilities — but are architecturally different. ChatGPT plugins and GPT Actions are tied to OpenAI's ecosystem. MCP is a vendor-neutral open standard; any AI application can implement an MCP client. MCP also offers richer primitives (resources, prompts) beyond simple tool calls.

What are Skills on mAIndala?

Skills are reusable prompt packages — a named capability with a curated set of prompts that tell an AI agent exactly how to perform a specific task. Think of a Skill like a recipe: it bundles the instructions an AI needs to, for example, "research a company", "draft a cold email", or "summarize a legal document".
  • Skills are authored by community members and published to the Skills catalog.
  • Each Skill has a category, tags, and a short description so you can find the right one quickly.
  • Prompts inside a Skill are gated — you must install a Skill before you can view or use its prompts.
  • Authors can publish Skills as draft (private), active (public), or deprecated.

How do I install and use a Skill?

  1. Browse or search the Skills catalog to find a Skill that fits your task.
  2. Open the Skill detail page and click Install. This creates an install record tied to your account.
  3. Once installed, the full prompt set is revealed — copy the prompts into Claude, your AI IDE, or any agent harness.
  4. You can also access installed Skills programmatically via the mAIndala MCP Gateway's install_skill and get_skill tools.

After using a Skill you can leave a star rating to help the community discover the best ones.


How do I create my own Skill?

Any logged-in user can author a Skill:
  1. Go to Create a Skill and fill in the name, category, and description.
  2. Add one or more prompts — give each a label (e.g. "System prompt", "Step 1: Research") and the prompt text.
  3. Save as draft to keep it private, or publish as active to make it discoverable by everyone.
  4. Edit or deprecate your Skill at any time from its detail page.

What are Agents?

An Agent is a multi-step AI workflow blueprint composed of Skills. Where a Skill handles one task, an Agent orchestrates a workflow — a sequence of Skills connected by routing rules. For example, a "Lead Research Agent" might chain a "Company Research" Skill → "Contact Finder" Skill → "Outreach Draft" Skill, with conditional branching depending on what each step finds.
  • Each Agent has nodes (Skills assigned a role in the workflow) and routes (edges between nodes with conditions like always, on_success, or on_failure).
  • One node is marked as the entry point — where execution begins.
  • Agents are deployed, not installed — deploying exports a full JSON bundle with all prompts and a framework hint section for Claude, CrewAI, or LangGraph.

How do I deploy an Agent?

  1. Find an Agent in the Agents catalog and open its detail page.
  2. Click Deploy. This downloads a JSON export bundle containing the full Agent definition, all constituent Skill prompts, and frameworkHints for your preferred orchestration framework.
  3. Use the bundle to set up the agent in Claude Projects, CrewAI, LangGraph, or any compatible harness — the hints section shows you exactly how to wire it up.

AI agents can also deploy Agents programmatically using the mAIndala MCP Gateway's deploy_persona tool.


What is the difference between a Skill and an Agent?

  • Skill — a single capability. One task, one prompt set. Used directly by a human or an agent to accomplish a specific thing.
  • Agent — a workflow. Multiple Skills stitched together with routing logic to achieve a broader goal automatically.

A Skill is a building block; an Agent is the finished assembly. You can think of Skills as functions and Agents as programs.


How do Skills and Agents relate to MCP servers?

MCP servers provide tool access — they let an AI call external APIs, read files, or query databases. Skills and Agents provide reasoning blueprints — they tell the AI how to think and what to saywhen using those tools.

A well-designed Agent might use an MCP server for data retrieval at one node and a Skill's prompt to interpret and format the results at the next. Together they form a complete agentic system: MCP handles the "hands", Skills and Agents handle the "brain".


What are Groups on mAIndala?

Groups are curated collections of MCP services shared with a team or community. A Group admin hand-picks which services appear in the catalog for their members — when you browse as a member of an active Group, you see only the services your group has approved rather than the full public catalog of 2,250+ entries.
  • Public groups are discoverable in the Groups directory and open to anyone who wants to join.
  • Private groups are invite-only — members join via an email invite link sent by an admin.
  • Groups are useful for enterprises curating a vetted tool list, developer communities around a theme, or teams that want a focused, noise-free catalog.

How do I create or join a Group?

To create: Any signed-in user can go to Create a Group, choose a name and slug, set visibility (public or private), and add a description. You become the group admin automatically.

To join a public group: Browse the Groups directory, find a group, and click Join. No approval required for public groups.

To join a private group: An admin must invite you by email. Click the link in the invitation email to accept and become a member.


What does being in a Group do?

Once you join a Group and switch it active (via the Groups menu in the nav bar):
  • The Browse page filters to show only that group's curated service list instead of the full public catalog.
  • Searches and category filters operate within the curated list.
  • Group admins manage the service list and members from the group admin panel.
  • You can switch back to the full catalog at any time by clearing your active group from the Groups menu.

You can be a member of multiple groups and switch between them freely.


What is a Group API key?

A Group API key (prefixed mg_...) lets AI agents access the mAIndala MCP Gateway on behalf of a Group. When an agent includes the key as an Authorization: Bearer mg_... header, all MCP tool calls — list_services, search_services, get_service, and so on — automatically filter to that group's curated service list.

Group admins generate and revoke API keys from the API Keys tab on the group admin page (/groups/[slug]/admin). Each key shows its prefix and last-used timestamp. Use Group API keys when building agents that should only ever see your team's approved tools.


Where can I find more MCP servers and resources?

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