Cloud Tools Gateway

Remote MCP tools server built with Python, FastMCP, Streamable HTTP, and static bearer-token authentication.

Local Development

uv sync
$env:MCP_BEARER_TOKEN = "replace-with-a-long-random-secret"
uv run uvicorn main:app --host 0.0.0.0 --port 8000

MCP endpoint:

http://localhost:8000/mcp

Clients must send:

Authorization: Bearer replace-with-a-long-random-secret

For ChatGPT custom connectors, use OAuth authentication. The server exposes:

• Authorization metadata: /.well-known/oauth-authorization-server

• Authorization URL: /oauth/authorize

• Token URL: /oauth/token

• MCP resource: /mcp

Set PUBLIC_BASE_URL in production, for example https://mcp-dh2a.onrender.com.

Related MCP server: Shark-no-Kari

Tools

• fetch_webpage: fetches a URL and returns clean text plus page metadata.

• extract_links: extracts normalized links from a URL.

• check_url_status: checks URL reachability, status, timing, and headers.

• analyze_text: returns basic text statistics and top terms.

• run_crewai_automation: sends an order to a configured CrewAI deployment.

• call_crewai_endpoint: calls safe GET/POST paths on the configured CrewAI deployment API.

• run_crewai_workflow: starts the configured CrewAI workflow with {"inputs": {...}}.

• run_crewai_workflow_and_wait: starts a CrewAI workflow, polls until completion, and returns the finished report.

• get_crewai_status: polls GET /status/{kickoff_id}.

• get_crewai_result: reads final output from the status response.

• get_crewai_workflow_result: fetches a completed workflow result later by workflow_id and kickoff_id.

• create_life_insurance_campaign_package: runs the Life Insurance Marketing OS sequence and returns one combined compliant campaign package.

• run_ping_os_objective: lets ChatGPT give Ping OS a business objective; the supervisor plans and runs the needed workflows.

• get_ping_os_run: fetches a stored Ping OS supervisor run by run_id.

Docker

Build:

docker build -t cloud-tools-gateway .

Run:

docker run --rm -p 8000:8000 -e MCP_BEARER_TOKEN="replace-with-a-long-random-secret" cloud-tools-gateway

Cloud Deployment

Use these settings on Render, Railway, Fly.io, Google Cloud Run, or a similar container host:

• Build command: docker build -t cloud-tools-gateway .

• Run command: uv run --frozen uvicorn main:app --host 0.0.0.0 --port $PORT

• Required environment variable: MCP_BEARER_TOKEN

• Recommended environment variable: PUBLIC_BASE_URL

• Optional environment variable: MCP_CLIENT_ID

• Optional CrewAI bridge variables: CREWAI_API_URL, CREWAI_BEARER_TOKEN

• Public MCP URL: https://<your-domain>/mcp

For container platforms that run the Dockerfile directly, set only MCP_BEARER_TOKEN; the CMD is already included.

CrewAI

CrewAI can connect to the same remote MCP endpoint with direct bearer-token headers.

Install CrewAI MCP support in your agent project:

uv add crewai

Set environment variables:

export MCP_URL="https://mcp-dh2a.onrender.com/mcp"
export MCP_BEARER_TOKEN="your-render-mcp-token"

Use examples/crewai_remote_mcp.py as a starting point. The key configuration is:

from crewai.mcp import MCPServerHTTP

tools = MCPServerHTTP(
    url="https://mcp-dh2a.onrender.com/mcp",
    headers={"Authorization": f"Bearer {MCP_BEARER_TOKEN}"},
    cache_tools_list=True,
)

ChatGPT To CrewAI Bridge

To let ChatGPT give orders to a CrewAI deployment through this MCP server, configure these environment variables on the MCP deployment:

CREWAI_API_URL="https://your-crew-deployment.crewai.com"
CREWAI_BEARER_TOKEN="your-crewai-deployment-bearer-token"

Optional per-workflow override for the Life Insurance Lead Crew:

CREWAI_LIFE_INSURANCE_API_URL="https://your-life-insurance-crew.crewai.com"
CREWAI_LIFE_INSURANCE_BEARER_TOKEN="your-life-insurance-crew-token"

If those override variables are not set, life_insurance_leads uses CREWAI_API_URL and CREWAI_BEARER_TOKEN.

Optional per-workflow override for the Life Insurance Research Crew:

CREWAI_LIFE_INSURANCE_RESEARCH_API_URL="https://your-life-insurance-research-crew.crewai.com"
CREWAI_LIFE_INSURANCE_RESEARCH_BEARER_TOKEN="your-life-insurance-research-crew-token"

If those override variables are not set, life_insurance_research uses CREWAI_API_URL and CREWAI_BEARER_TOKEN.

The downstream Life Insurance Marketing OS workflows are available through the same MCP tools:

• life_insurance_content

• life_insurance_seo

• life_insurance_retell

• life_insurance_email

• life_insurance_compliance

These currently run as MCP Gateway workflow handlers, so they do not need separate CrewAI Cloud deployments. Dedicated CrewAI deployments can be added later by setting each workflow's env vars and replacing the local handler.

After redeploying, refresh the ChatGPT connector actions. ChatGPT will see:

• run_crewai_automation: starts the configured CrewAI deployment via /kickoff.

• call_crewai_endpoint: makes constrained GET/POST calls to a CrewAI deployment API. Pass workflow_id to inspect non-default routes.

• run_crewai_workflow: sends POST /kickoff with nested inputs, such as {"inputs": {"user_name": "Jean"}}.

• run_crewai_workflow_and_wait: sends POST /kickoff, polls result endpoints, and returns the final JSON plus markdown report.

• get_crewai_status: checks run state with GET /status/{kickoff_id}.

• get_crewai_result: returns the final result from GET /status/{kickoff_id}.

• get_crewai_workflow_result: fetches final output later using the workflow route.

• create_life_insurance_campaign_package: creates a full MotherlyQuotes-style campaign package by chaining research, content, Retell, email, and compliance workflows.

• run_ping_os_objective: accepts a plain-English business objective, selects a plan, runs workflows, and returns one strategy package.

• get_ping_os_run: retrieves the stored supervisor run record, final JSON, and markdown report.

CrewAI status is the source of truth for output. This deployment returns final output in the /status/{kickoff_id} payload. The gateway also probes /result/{kickoff_id}, /kickoff/{kickoff_id}, /runs/{kickoff_id}, and /tasks/{kickoff_id} as fallbacks.

Life insurance lead workflow input example:

run_crewai_workflow(
    workflow_id="life_insurance_leads",
    inputs={
        "client_name": "MotherlyQuotes",
        "target_audience": "new and expecting moms",
        "licensed_states": ["CA"],
        "offer": "free life insurance quote check",
        "crm_destination": "HubSpot",
        "followup_channel": "Brevo",
    },
)

Life insurance research workflow input example:

run_crewai_workflow_and_wait(
    workflow_id="life_insurance_research",
    inputs={
        "user_name": "Jean Pierre",
        "client_name": "MotherlyQuotes",
        "target_audience": "new and expecting moms",
        "licensed_states": ["CA"],
        "product_focus": "term life insurance",
        "competitors": ["Policygenius", "Ethos", "Ladder", "SelectQuote"],
        "offer": "free life insurance quote check",
        "crm_destination": "HubSpot",
        "followup_channel": "Brevo",
        "output_format": "markdown_and_json",
    },
    timeout_seconds=180,
    poll_interval_seconds=5,
)

The gateway adds workflow_id="life_insurance_research" into the nested CrewAI inputs payload when the MCP workflow parameter is used.

Route debug endpoint:

curl https://mcp-dh2a.onrender.com/debug/routes

This returns configured CrewAI API URLs and token presence flags without exposing bearer token values.

Ping OS supervisor debug endpoint:

curl https://mcp-dh2a.onrender.com/debug/ping-os

This returns supervisor health, supported verticals, supported objective types, available workflows, and current in-process run count.

To inspect the life insurance research deployment inputs through MCP, call:

call_crewai_endpoint(
    method="GET",
    path="/inputs",
    workflow_id="life_insurance_research",
)

Full campaign package example:

create_life_insurance_campaign_package(
    user_name="Jean Pierre",
    client_name="MotherlyQuotes",
    target_audience="new and expecting moms",
    licensed_states=["CA"],
    product_focus="term life insurance",
    competitors=["Policygenius", "Ethos", "Ladder", "SelectQuote"],
    offer="free life insurance quote check",
    crm_destination="HubSpot",
    followup_channel="Brevo",
    timeout_seconds=300,
)

Ping OS Supervisor

Ping OS is the supervisor/orchestrator layer for ChatGPT. Instead of calling workflow tools manually, ChatGPT can submit a business objective and let Ping OS choose the workflow graph.

Supported verticals:

• life_insurance

Supported objective types:

• lead_generation_campaign

• market_research

• content_engine

• voice_agent_setup

• compliance_review

The default life insurance lead-generation plan runs:

1. life_insurance_research

2. life_insurance_seo

3. life_insurance_content

4. life_insurance_retell

5. life_insurance_email

6. life_insurance_compliance

ChatGPT should call the supervisor like this:

run_ping_os_objective(
    objective="Generate a compliant campaign package to acquire 500 qualified life insurance leads in California this month.",
    business_name="MotherlyQuotes",
    vertical="life_insurance",
    target_audience="new and expecting moms",
    geography=["CA"],
    offer="free life insurance quote check",
    constraints={
        "product_focus": "term life insurance",
        "crm_destination": "HubSpot",
        "followup_channel": "Brevo",
        "competitors": ["Policygenius", "Ethos", "Ladder", "SelectQuote"],
    },
    output_format="markdown_and_json",
    timeout_seconds=300,
)

The response includes:

{
  "ok": true,
  "run_id": "ping-os-...",
  "objective": "...",
  "business_name": "MotherlyQuotes",
  "vertical": "life_insurance",
  "execution_plan": [
    {
      "step": 1,
      "workflow_id": "life_insurance_research",
      "reason": "Research audience, competitors, buyer intent, objections, and campaign angles."
    }
  ],
  "workflow_results": {},
  "final_strategy": {},
  "markdown_report": ""
}

Fetch a stored run later:

get_ping_os_run(run_id="ping-os-...")

Run records are stored in the MCP Gateway process memory and include:

• run_id

• objective

• business_name

• vertical

• created_at

• status

• execution_plan

• workflow_ids

• kickoff_ids

• final_output

• markdown_report