Parallel AI Task MCP

Live

API KEY

ORCHESTRATION

AI

Parallel AI Task MCP lets your agent dispatch multiple sub-tasks concurrently and collect results in a single context. Your orchestration agent can fan out research, analysis, or data retrieval across parallel workers and merge outputs.

  • Acts as the user: Access and write actions stay tied to the Parallel AI Task MCP account that authorized the agent.
  • Credentials stay vaulted: AES-256, resolved at request time, never in LLM context.
  • Scoped before every call: Permissions enforced. 90-day audit trail.
Parallel AI Task MCP
agent · Acme Q3
Run
Research the top 5 competitor pricing pages and summarize each in parallel.
S
parallel_tasks_run
4.2s
Orchestration agent
All 5 tasks completed in 4.2s. Competitor A: usage-based, $0.02/call. Competitor B: seat-based, $49/seat. Competitor C: tiered, free to $299/mo. Competitor D: enterprise-only. Competitor E: open source, self-hosted.
Sources: 5 competitor pages, parallel
parallelaitaskmcpmcp
5 tasks
18:29
Message Claude...

Tools your orchestration agent reaches for on Parallel AI Task MCP, scoped per user.

CALL ANY TOOL
Fan out tasks in parallel, poll individual results, cancel running tasks, and retrieve batch outputs.
parallel_tasks_run
Run parallel tasks
Dispatch an array of task definitions to run concurrently and wait for results.
Parameters
Name
Type
Required
Description
tasks
array
Required
Array of task objects: each has type and input
timeout_ms
integer
Optional
Max wait time in milliseconds
parallel_task_get
Get task result
parallel_tasks_list
List tasks
parallel_task_cancel
Cancel task
parallel_tasks_results
Get batch results
Build your Agent
Drop the toolkit in, point it at the user, and your orchestration agent can use Parallel AI Task MCP from the first run.
import { ScalekitClient } from "@scalekit-sdk/node";
import { DynamicStructuredTool } from "@langchain/core/tools";
import { createReactAgent } from "@langchain/langgraph/prebuilt";
import { z } from "zod";

const sk = new ScalekitClient(envUrl, clientId, clientSecret);

const { tools } = await sk.tools.listScopedTools("user_123", {
filter: { connectionNames: ["parallelaitaskmcp"], toolNames: ["parallel_tasks_run", "parallel_task_get", "parallel_tasks_list"] },
pageSize: 100,
});

const lcTools = tools.map((t) => new DynamicStructuredTool({
name: t.tool.definition.name,
description: t.tool.definition.description,
schema: z.object({}).passthrough(),
func: async (args) => {
const { data } = await sk.tools.executeTool({
toolName: t.tool.definition.name,
identifier: "user_123",
params: args,
});
return JSON.stringify(data);
},
}));

const agent = createReactAgent({ llm, tools: lcTools });
import { ScalekitClient } from "@scalekit-sdk/node";
import OpenAI from "openai";

const sk = new ScalekitClient(envUrl, clientId, clientSecret);
const openai = new OpenAI();

const { tools } = await sk.tools.listScopedTools("user_123", {
filter: { connectionNames: ["parallelaitaskmcp"], toolNames: ["parallel_tasks_run", "parallel_task_get", "parallel_tasks_list"] },
pageSize: 100,
});

const llmTools = tools.map((t) => ({
type: "function",
function: {
name: t.tool.definition.name,
description: t.tool.definition.description,
parameters: t.tool.definition.input_schema,
},
}));

const resp = await openai.responses.create({
model: "gpt-4o", input: prompt, tools: llmTools,
});
import { ScalekitClient } from "@scalekit-sdk/node";
import Anthropic from "@anthropic-ai/sdk";

const sk = new ScalekitClient(envUrl, clientId, clientSecret);
const anthropic = new Anthropic();

const { tools } = await sk.tools.listScopedTools("user_123", {
filter: { connectionNames: ["parallelaitaskmcp"], toolNames: ["parallel_tasks_run", "parallel_task_get", "parallel_tasks_list"] },
pageSize: 100,
});

const llmTools = tools.map((t) => ({
name: t.tool.definition.name,
description: t.tool.definition.description,
input_schema: t.tool.definition.input_schema,
}));

const msg = await anthropic.messages.create({
model: "claude-sonnet-4-6", max_tokens: 1024,
tools: llmTools,
messages: [{ role: "user", content: prompt }],
});
import { Agent } from "@google/adk/agents";
import {
MCPToolset, StreamableHTTPConnectionParams,
} from "@google/adk/tools/mcp";

const toolset = new MCPToolset({
connectionParams: new StreamableHTTPConnectionParams({
url: "https://mcp.scalekit.com/parallelaitaskmcp",
headers: { Authorization: `Bearer ${userScopedToken}` },
}),
});

const agent = new Agent({
name: "agent", model: "gemini-2.0-flash",
tools: await toolset.getTools(),
});
Try these prompts
Paste any prompt into your agent to start using Parallel AI Task MCP.
Parallel execution
Copy the prompt
Copied
Run 5 research tasks in parallel for [topic].
Copy the prompt
Copied
Fan out [query] against [list of sources] simultaneously.
Copy the prompt
Copied
Get results for task batch [ids].
Copy the prompt
Copied
Cancel all running tasks.
Status & monitoring
Copy the prompt
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List all running tasks right now.
Copy the prompt
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Get status for task [id].
Copy the prompt
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How many tasks completed in the last hour?
Copy the prompt
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Which tasks failed today?
Orchestration
Copy the prompt
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Run competitive analysis on [domain list] in parallel.
Copy the prompt
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Research [topic] from 10 sources simultaneously.
Copy the prompt
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Summarize each of these [URLs] concurrently.
Copy the prompt
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Batch-enrich [company list] with data in parallel.
SEE HOW AUTH WORKS
Users authorize Parallel AI Task MCP once. Their credentials stay vaulted, every call is checked, and every action is logged.
1
Authorize
Your user connects
Parallel AI Task MCP
once. We tie it to their identity and the meetings they approved — no shared bot account, no org-wide access
Who:
user ‘A’
when:
Once per user
access:
Limited to user
2
Store
Their
Parallel AI Task MCP
token lives in a vault scoped to them. User A's meetings are never reachable by an agent acting for user B, even on the same connection
vault:
encrypted
scope:
per-user
tokens:
auto-refreshed
3
Resolve
When your agent calls a
Parallel AI Task MCP
tool, we fetch the right token server-side. It never touches your agent, never appears in the LLM context, never shows up in your logs
speed:
~40ms
check:
before every call
seen by:
nobody
4
Audit
Every
Parallel AI Task MCP
tool call is logged — who triggered it, which meeting was fetched, what came back. 90 days of history, tied to the user who authorized it
history:
90 days
export:
SIEM-ready
logged:
every call
Test other agents
Same per-user auth pattern across other orchestration agents and MCP connectors. Working code, live demos, fork what fits.
ENGINEERING
DevOps assistant agent
Triage GitHub incidents, open Linear tickets, and notify the on-call channel in Slack with context already attached.
ENGINEERING
Auto-release notes agent
Group merged GitHub PRs by feature, fix, or chore and publish release notes per tag. No manual changelog grooming.
Why Scalekit
Secure your agent's access. Connectors ship in minutes
Other connector libraries treat auth as a demo afterthought. Scalekit starts with user identity, scope enforcement, and audit.
01.
Shared tokens break per-user analytics
A shared token looks fine in a demo. In production every call looks like a service account. Scalekit resolves the real user credential so attribution, audit, and scope stay accurate.
// shared token
 audit → bot_service_account
 user_filter → broken

 // scalekit
 audit → user_abc
 scope → enforced ✓
02.
Authentication is not authorization
03.
Multi-tenancy is architectural
04.
Parallel AI Task MCP today. Others tomorrow.
“Our agents act across Salesforce, Gong, Google Drive, and more, on behalf of every customer. Scalekit behind the scenes meant we can keep adding tools without ever rebuilding how credentials or tool calling work.”
Venu Madhav Kattagoni
Head of Engineering / Von
FAQs
Frequently Asked Questions
Does the agent access Parallel AI Task MCP as the user or as a shared key?
As the user. Each workspace member authorizes once and Scalekit resolves their credential at request time. Audit logs attribute every action to that user, not a shared service account.
Where is the Parallel AI Task MCP api key stored?
In Scalekit's managed AES-256 token vault, namespaced per tenant. Refresh is automatic. Revocation is a single dashboard action. Tokens never appear in prompts, logs, or LLM context.
Can I limit what the agent is allowed to do in Parallel AI Task MCP?
Yes. Pass a tool name filter to listScopedTools so the orchestration agent only sees the subset you authorize. Pre-API-call scope checks block out-of-policy actions before the request reaches Parallel AI Task MCP.
What happens when a user revokes Parallel AI Task MCP access?
The connection is invalidated on the next tool call. Subsequent requests for that user fail closed with a clear error. Other users in the tenant remain unaffected. The event is logged for audit.
Can the agent share task results across users in the same workspace?
Only within the same API key scope. Task results are namespaced per key. Results from one user's key are inaccessible to another unless explicitly shared via your application layer.
Start in your coding agent
Up and running in one command
Install the Scalekit skill in your editor of choice. Connector, auth, tools, prompt, all wired up
Claude Code REPL
/plugin marketplace add scalekit-inc/claude-code-authstack
/plugin install agentkit@scalekit-auth-stack
Cursor Code REPL
# ~/.cursor/mcp.json
{
""mcpServers"": {
""parallelaitaskmcp"": {
""url"": ""https://mcp.scalekit.com/parallelaitaskmcp"",
""headers"": { ""Authorization"": ""Bearer $SCALEKIT_TOKEN"" }
}
}
}
Codex Code REPL
# ~/.codex/config.toml
[mcp_servers.parallelaitaskmcp]
url = ""https://mcp.scalekit.com/parallelaitaskmcp""
auth_env = ""SCALEKIT_TOKEN""
Copilot Code REPL
# .vscode/mcp.json
{
""servers"": {
""parallelaitaskmcp"": {
""url"": ""https://mcp.scalekit.com/parallelaitaskmcp"",
""type"": ""http""
}
}
}