Call Anthropic's REST API — Claude messages, token counting, batches, files.
Anthropic ships in the w6w first-party pack. It declares 14 actions, 2 health checks, and the host runs its code in a sandbox that never sees the credential.
io.w6w.anthropicAnthropic connects a workflow directly to Claude — sending message-create calls for chat completions, and counting tokens ahead of a request so cost and context-window use can be checked before it is actually sent.
Message Batches are a first-class surface, not an afterthought: batches can be created, listed, polled, cancelled and their results pulled once complete, which is the right shape for processing large volumes of prompts asynchronously rather than issuing them one at a time. Files can be uploaded, listed, fetched and deleted for use as batch or message inputs, and models can be listed or looked up individually to keep a workflow’s model choice explicit rather than hardcoded.
Useful anywhere a workflow needs an LLM step — summarizing text, classifying content, or running a large batch of prompts against source data pulled from an earlier action — without wiring up a client library or handling batch polling by hand.
Three routes to the same 14 actions. The Workflow tab is generated from Anthropic's own manifest and carries its real ids, so it is copy-pasteable; the Code and CLI examples are the same call for any action on any app, so every app-specific value in them is a blank you fill in.
batch-results Fetch the per-request results for a completed batch (JSONL decoded to an array).
count-tokens Count the input tokens a given prompt would consume against a Claude model.
file-upload Upload a file for use in a Message (referenced by file_id). Beta: files-api.
A workflow step names the app and the action, and the editor fills in the
connection when you pick one. This is the Step shape from the
workflow spec, carrying Anthropic's real ids.
{
"manifestVersion": "2",
"name": "anthropic-example",
"steps": [
{
"id": "batch-create",
"uses": {
"app": "io.w6w.anthropic",
"action": "batch-create",
"connection": "conn_YOUR_CONNECTION_ID"
},
"with": {
"requests": "<requests>"
}
}
]
}batch-create batch-get batch-list batch-results count-tokens +9 more actions available
Every app-specific value here is a blank you have to fill in. An
app action is reached through the connection that authenticates it, so the
address is a connection id, not the app id — and connections belong to your account,
so a public page cannot know yours. Create one for Anthropic, then fill in
the three blanks: conn_YOUR_CONNECTION_ID, the action key, and the
parameters that action declares. The call itself is real — the shape is transcribed
from the studio's own snippet builder, which prints the same kind of blanks — but
nothing in it is specific to Anthropic. The Workflow tab is where this app's
real ids are.
npm install @w6w/sdkyarn add @w6w/sdkpnpm add @w6w/sdkdeno add npm:@w6w/sdkimport { W6wClient, isActionRun } from "@w6w/sdk";
// Reads W6W_BASE_URL and W6W_TOKEN from the environment when omitted.
const client = new W6wClient();
const envelope = await client.run({
urn: "conn_YOUR_CONNECTION_ID",
action: "message-create",
payload: {
model: "<value>",
messages: "<value>",
max_tokens: "<value>",
// system: "<value>",
// temperature: "<value>",
// top_p: "<value>",
// top_k: "<value>",
// stop_sequences: "<value>",
// tools: "<value>",
// tool_choice: "<value>",
// metadata: "<value>",
// stream: "<value>",
},
});
if (isActionRun(envelope)) console.log(envelope.value); npm install -g @w6w/cli w6w run conn_YOUR_CONNECTION_ID --action message-create --payload '{"model":"<value>","messages":"<value>","max_tokens":"<value>"}' Give an AI agent Anthropic — without giving it Anthropic's credentials. One MCP endpoint exposes every app, function and workflow the caller is entitled to, as tools it can discover and run. Access is granted per team while we onboard.
One tool call{
"name": "w6w_invoke",
"arguments": {
"ref": "app:io.w6w.anthropic#batch-create",
"input": {
"requests": "<requests>"
}
}
}
Every tool names its target with a single ref. The
app: form above doesn't name a connection at all — the
host resolves which of the caller's Anthropic connections to sign
with, and refuses rather than guesses when the answer is ambiguous.
The token is attached host-side, at the moment of the call. It is never a tool argument, never in the model's context, and never in a transcript — so a prompt injection has nothing to exfiltrate.
Tools are derived per end user from what that person has actually connected and is entitled to — not one shared bot identity carrying the union of everyone's access.
Multi-step work runs on the workflow engine and returns a run handle the agent can poll — retries, branching and state survive the conversation that started them.
Anthropic's declared health checks are on the surface too, so an agent can tell "the vendor is down" from "your credential expired" before it burns a retry on either.
The MCP surface is part of the hosted platform. Anthropic itself is MIT, and the runtime that executes it is source-available (FSL).
Anthropic declares its own checks, so its health is a property of the app rather than something the host guesses at.
Atlassian Statuspage rollup for status.anthropic.com, with per-component detail. Unauthenticated and unsigned.
Requests-per-minute and tokens-per-minute remaining, read off the response headers.