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.


Can I install Skills and Agents into my AI coding tool from the terminal?

Yes — the maindala CLI lets you install Skills and Agents directly into Claude Code, Cursor, GitHub Copilot, Windsurf, OpenClaw, or any plain markdown file, all from your terminal.

1. Get a personal API key

Go to your Profile and generate a personal API key (mk_...). Then save it:

npx maindala login mk_your_api_key_here

2. Install a Skill

# Auto-detects your tool from project files (CLAUDE.md, .cursor/, etc.)
npx maindala install linkedin-connection-request

# Or specify a format explicitly
npx maindala install linkedin-connection-request --format cursor

3. Install an Agent

npx maindala install agent/stock-analyst-pro --format claude

Supported formats

  • claude — appends to CLAUDE.md (Claude Code)
  • cursor — writes .cursor/rules/<slug>.mdc
  • copilot — appends to .github/copilot-instructions.md
  • windsurf — appends to .windsurfrules
  • openclaw — writes .openclaw/skills/<slug>.md
  • raw — writes <slug>.md in the current directory

Re-running the same install updates the existing block in-place (idempotent). The CLI is published as maindala on npm — no global install needed with npx.


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?

There are several ways to deploy an Agent depending on which AI platform you use.

Option 1 — mAIndala website (works with any platform)

  1. Find an Agent in the Agents catalog and open its detail page.
  2. Click Deploy. This downloads a JSON bundle containing the full Agent definition, all Skill prompts, and frameworkHints for Claude, CrewAI, and LangGraph.
  3. Follow the framework hint for your platform to wire up the Agent in your harness.

Option 2 — Claude Code

Add the mAIndala MCP server to your project's .claude/settings.json:

{
  "mcpServers": {
    "maindala": {
      "type": "streamableHttp",
      "url": "https://mcp.maindala.com/mcp"
    }
  }
}

Then ask Claude: "Use the mAIndala MCP server to deploy the Stock Analyst Agent and set it up for LangGraph." Claude will call the Deploy Agent tool and return the full bundle.

Option 3 — Claude Desktop

Add the following to your Claude Desktop config at ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows), then restart Claude Desktop:

{
  "mcpServers": {
    "maindala": {
      "type": "streamableHttp",
      "url": "https://mcp.maindala.com/mcp"
    }
  }
}

Once connected, you can say: "Search mAIndala for stock analysis agents and deploy the best one."

Option 4 — ChatGPT, Gemini, or Grok

These platforms don't yet support remote MCP servers directly from their chat UI. Instead, download the Agent bundle from mAIndala and paste the prompts manually:

  • ChatGPT — deploy the Agent from the mAIndala website to get the bundle, then create a Custom GPT using the system prompt from the first Skill node. For API users, the openai-tool format gives you a ready-to-use tool schema.
  • Gemini — copy the Agent's node prompts into a Gem (Gemini's custom AI) or paste them as context at the start of your conversation. Vertex AI users can integrate via the gemini format bundle.
  • Grok — paste the Agent's prompts into a custom instruction or system message. The raw format bundle gives you clean, platform-agnostic prompt text.

Platform-specific bundles are available via the mAIndala API: GET /agents/[slug]/package?format=openai-tool (or gemini, grok, claude, crewai, langgraph).


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, Skills, and Agents shared with a team or community. A Group admin hand-picks which services appear in the catalog for their members, and which Skills and Agents are available for download — when you browse as a member of an active Group, you see only what 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 API keys grant access to the group's curated Skills and Agents — no individual install or deploy record required for curated items.
  • Group admins manage services, skills, agents, members, and API keys 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 mAIndala on behalf of a Group. It unlocks three things at once:
  • Filtered MCP catalog — all MCP Gateway tool calls (list_services, search_services, etc.) automatically filter to the group's curated service list.
  • Skill downloadsGET /skills/[slug]/package succeeds for any Skill the group admin has curated, without needing an individual install record.
  • Agent packagesGET /agents/[slug]/package succeeds for any Agent the group admin has curated, without needing an individual deploy record.

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, skills, and agents.


How do I curate Skills and Agents for my Group?

Group admins can hand-pick which Skills and Agents are available to their members from the group admin page (/groups/[slug]/admin):

  • Open the Skills tab, search the Skills catalog by name, and click Addnext to any Skill you want to curate.
  • Open the Agents tab and do the same for Agents.
  • Curated items appear in the list below the search — click the trash icon to remove any entry.

Once a Skill or Agent is curated, any member holding a Group API key can download its package via the CLI or API without needing to individually install or deploy it first:

# Install a group-curated Skill
MAINDALA_API_KEY=mg_... npx maindala install financial-analysis

# Install a group-curated Agent
MAINDALA_API_KEY=mg_... npx maindala install agent/stock-analyst-pro

This is designed for teams that want to standardise on a set of approved AI capabilities and distribute them to members without requiring each person to manage their own installs.


Where can I find more MCP servers and resources?

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