Skip to main content

MCP Tools

The Model Context Protocol (MCP) enables AI agents to access external tools and integrations. Strongly AI ships with a catalog of more than 130 pre-built MCP servers covering databases, APIs, productivity tools, and more, plus support for your own custom MCP servers.

How It Works​

Each organization has a single MCP tool hub that serves every enabled MCP server's tools to your workflow agents:

  1. You enter an MCP server's configuration (API keys, URLs) and deploy it -- its tools are registered with your organization's tool hub
  2. Enabled tools become available to agents through the MCP Tools Provider node
  3. During a run, the agent decides which tools to call and the calls are executed with the configuration you stored
  4. Registrations persist -- your enabled MCPs survive platform restarts

Available MCP Servers​

Search & Web​

  • Brave Search -- web, image, video, news search
  • DuckDuckGo -- privacy-focused search (no API key)
  • Exa -- AI-powered semantic search
  • Firecrawl -- web scraping and data extraction
  • Google Translate -- translation for 100+ languages

Development & DevOps​

  • GitHub -- repos, issues, PRs, code search (26 tools)
  • Git -- repository operations
  • Docker Hub -- image search, tags, repositories
  • SonarQube -- code quality analysis
  • Postman -- API testing and management
  • CircleCI -- CI/CD pipelines
  • Netlify / Render -- deployment platforms

Databases​

  • Redis -- key-value, hashes, lists, sets
  • Elasticsearch -- full-text search and analytics
  • MongoDB -- document CRUD, aggregation
  • Neo4j -- graph database Cypher queries
  • SingleStore -- distributed SQL
  • Couchbase -- NoSQL document database
  • CockroachDB -- distributed SQL

Productivity​

  • Atlassian -- Jira, Confluence, Bitbucket (16 tools)
  • Notion -- pages, databases, search
  • Todoist -- task management
  • Google Tasks / Microsoft To Do -- task lists

Communication​

  • Mailgun -- transactional email
  • LinkedIn -- post content, profile
  • Webex / Mattermost / RocketChat / Matrix -- messaging

Finance & Commerce​

  • Stripe -- payments, customers, subscriptions
  • Coinbase -- cryptocurrency data
  • Razorpay -- payment processing
  • Xero -- accounting

AI & Data​

  • Wolfram Alpha -- computational knowledge
  • DeepL -- neural machine translation
  • ElevenLabs -- text-to-speech
  • OpenWeather -- weather data and forecasts

Monitoring​

  • Grafana -- dashboards, alerts, data sources
  • Dynatrace -- APM monitoring

Utilities​

  • HTTP Fetch -- generic web requests (no API key)
  • RSS -- feed parsing (no API key)
  • Time -- timezone operations
  • Playwright -- browser automation

...and many more. Browse the full catalog on the Workflow Tools page (/workflow-tools).

Configuring an MCP Server​

  1. Navigate to the Workflow Tools page (/workflow-tools). It lists all MCP servers and custom tools, with counts and a type filter
  2. Open a server's Details page and enter its required configuration (API keys, URLs)
  3. Save, then Deploy -- this registers the server's tools with your organization's tool hub. The Details page has a logs tab to follow progress
  4. Use Enable / Disable on the card menu to toggle a registered server on or off without redeploying
  5. Use the server's tools in workflows via the MCP Tools Provider node

The card menu also supports Clone (copy a server so you can vary its configuration) and Delete.

Using MCP Tools in a Workflow​

The MCP Tools Provider node (Operators category, node type mcp-tools-provider) supplies MCP tools to agent nodes:

  1. Drag an MCP Tools Provider node onto the canvas
  2. In its configuration, select the MCP server from the dropdown of deployed servers (each entry shows its Running / Not Deployed status). An additional multi-select lets you attach more servers to the same provider
  3. Connect the provider's output to the agent node's tools connector (bottom of the agent node)
  4. The agent autonomously calls the provided tools as needed during a run

The node outputs the aggregated tool list (tools), the number of servers queried (serverCount), and the total tool count (toolCount). When you deploy the workflow, the platform resolves the selected servers, registers them with your organization's tool hub, and populates the provider with the live tool definitions automatically.

Adding Custom MCP Servers​

You can add your own MCP server from the Workflow Tools page:

  1. Click to create a new tool and choose the MCP server type
  2. Upload a ZIP or TAR bundle containing mcp.py at its root (requirements.txt and README.md are optional)
  3. Save and Deploy. Uploading new code later requires a version bump

mcp.py must declare its tools and an execute entry point:

# mcp.py
import httpx
from typing import Dict, Any

TOOLS = [
    {"name": "my_tool", "description": "Does something useful",
     "inputSchema": {"type": "object", "properties": {"query": {"type": "string"}}, "required": ["query"]}},
]

async def execute(tool_name: str, arguments: Dict[str, Any], config: Dict[str, str]) -> Dict[str, Any]:
    async with httpx.AsyncClient() as c:
        r = await c.get("https://api.example.com/search", params={"q": arguments["query"]})
        return {"content": [{"type": "text", "text": r.text}]}
  • TOOLS is the list of tool definitions (name, description, JSON Schema input)
  • execute(tool_name, arguments, config) handles every tool call; config carries the configuration values entered on the Details page
  • A bundle without mcp.py, or an mcp.py without a TOOLS = [...] declaration, is rejected at upload/deploy time