Agenticflow MCP

AgenticFlow MCP

Do NOT install the legacy agenticflow-mcp standalone server repo. It is stale (last release lags the current platform by many versions) and is not the recommended integration path. Everything below uses the af CLI (install via npm install -g @pixelml/agenticflow-cli), which covers MCP operations comprehensively and stays in lockstep with platform changes.

MCP (Model Context Protocol) clients are the tool-provider layer that lets an agent read or write external systems — Google Docs, Google Sheets, Slack, Notion, GitHub, Apify, Gmail, Pinterest, YouTube, and many more. A workspace usually has many MCP clients already configured; your job is to pick the right one and verify it's safe before attaching to an agent.

Orient first

Before touching MCP clients, run:

af bootstrap --json

Extract auth.workspace_id + _links.mcp (the web UI URL for MCP management). Surface _links.mcp to the user: "Your MCP connections live at <_links.mcp> — you can add or re-authenticate providers there if any inspections fail." If data_fresh: false, the backend is degraded — don't mutate.

Discovery & health

af changelog --json           # What's new in the CLI since your last install
af context --json              # AI agent orientation, env vars, invocation guidance
af bootstrap --strict --json   # Health check — exits non-zero if degraded

af bootstrap returns an invocation block telling you the correct CLI binary to use. af bootstrap --strict exits non-zero when the backend is unhealthy, so CI/automation can abort before mutating against a degraded workspace.

The one pattern that matters: inspect before attach

Not all MCP clients are equal. There are two major families, and one of them breaks on writes.

af mcp-clients list --name-contains "google sheets" --fields id,name,is_authenticated --json
af mcp-clients inspect --id <mcp_client_id> --json
af mcp-clients get --id <mcp_client_id> --json   # Alias for inspect (v1.8.1+)

inspect classifies the tool set and returns a pattern field:

inspect also returns known_quirks[] — read it. If it contains a warning about configure_props_<tool>_props, you've been warned; do not attach that client for writes.

Attach shape (use --patch, not full-body update)

af agent update --agent-id <agent_id> --patch --body '{
  "mcp_clients": [
    {
      "mcp_client_id": "<id-from-inspect>",
      "run_behavior": "auto_run",
      "description": "Google Sheets for logging outputs",
      "timeout": 150,
      "tools": {
        "GOOGLESHEETS_SPREADSHEETS_VALUES_APPEND": {"allowed": true},
        "GOOGLESHEETS_VALUES_UPDATE": {"allowed": true},
        "GOOGLESHEETS_DELETE_SHEET": {"allowed": false}
      }
    }
  ]
}' --json

tools is a per-tool allow/block map. Block destructive ops (DELETE_*, CLEAR_*) unless the use case explicitly needs them. The CLI's --patch merges this over the current agent — existing MCP clients and other config are preserved.

Verify auth state (list vs. get can disagree)

af mcp-clients list returns a cached is_authenticated that can be stale. If something is off, reconcile:

af mcp-clients list --verify-auth --json
# For each row, also calls `get` and adds verified_is_authenticated + verified_auth_mismatch.
# N+1 call — use when debugging, not on every run.

If classification_reason in inspect is fetch_failed or unauthenticated, re-auth the client in the web UI at /workspaces/<ws>/mcp/<client_id> before attaching.

Smoke test after attach

af agent run --agent-id <agent_id> --message "Try writing 'hello' to row 1" --json
# Expect status: "completed" + a response that actually shows the write happened.
# If the model claims success without a real write, you're probably on a broken MCP — re-inspect.

On failures

• pattern: "pipedream" + write attempt → TOOL_CONFIGURATION_COMPLETED or configure_props_<tool>_props in the response. Switch to a Composio-backed client (many workspaces have both).
• mcp-clients inspect returns classification_reason: "fetch_failed" → the MCP provider's backend is down or the OAuth session expired. Open the web UI, re-auth, retry.
• Silent success (model says "done" but no actual write) → 99% of the time it's a Pipedream write quirk. Re-inspect, switch provider.