Skip to main content

The #1 agentic semantic tool search: 91.6% first-try accuracy on S1 Search Bench • Explore Tool Discovery →

Live 32 Actions

Parallel MCP Server
for AI Agents

Connect your AI agent to StackOne's Parallel MCP server and give it 32 MCP tools out of the box. Auth, tool execution, and security all managed.

Start Free Book Demo Read Docs →
Parallel logo
Parallel MCP Server
Built by StackOne StackOne
DrataGPLocalyzeFlipMindtoolsScreenloop

Coverage

32 Agent Actions

Create, read, update, and delete across Parallel — and extend your agent's capabilities with custom actions.

MCP Actions → Build Actions →

Authentication

Agent Tool Authentication

Per-user OAuth in one call. Your Parallel MCP server gets session-scoped tokens with zero credentials stored on your infra.

Agent Auth →

Security

Agent Protection

Every Parallel tool response scanned for prompt injection in milliseconds — 88.7% accuracy, all running on CPU.

Prompt Injection Defense →

Performance

Max Agent Context. Min Cost.

Free up to 96% of your agent's context window to enhance reasoning and reduce cost, on every Parallel call.

Tools Discovery →

What is the Parallel MCP Server?

A Parallel MCP server lets AI agents read and write Parallel data through the Model Context Protocol — Anthropic's open standard for connecting LLMs to external tools. StackOne's Parallel MCP server ships with 32 pre-built actions, fully extensible via the Connector Builder — plus managed authentication, prompt injection defense, observability, and agent execution runtime. Connect it from MCP clients like Claude Desktop, Claude Code, Cursor, Goose, and VS Code, or from agent frameworks like OpenAI Agents SDK, LangChain, and Vercel AI SDK.

All Parallel MCP Tools

Every action from Parallel's API, ready for your agent. Create, read, update, and delete — scoped to exactly what you need.

Task Runs

  • Create Task Run

    Create a new task run for structured web research

  • Retrieve Task Run

    Retrieve a task run by ID

Task Groups

  • Create Task Group

    Create a new task group for batch task execution

  • Retrieve Task Group

    Retrieve a task group by ID

Monitors

  • Create Monitor

    Create a web content monitor

  • List Monitors

    List monitors with optional filtering

  • Retrieve Monitor

    Retrieve a single monitor by ID

  • Update Monitor

    Update an existing monitor

Other (24)

  • Add Runs To Task Group

    Add task runs to an existing task group

  • Create FindAll Run

    Create a FindAll run to discover entities matching specific conditions

  • Add Enrichment To FindAll Run

    Add enrichment to extract structured data from FindAll matches

  • Search

    Perform a web search with AI-powered relevance filtering

  • Retrieve Task Run Input

    Retrieve the input of a task run

  • Retrieve Task Run Result

    Retrieve the result of a completed task run

  • Retrieve Task Group Run

    Retrieve a specific run within a task group

  • Retrieve FindAll Run Status

    Retrieve the status of a FindAll run

  • Retrieve FindAll Run Result

    Retrieve results of a FindAll run

  • Get FindAll Run Schema

    Retrieve the schema of a FindAll run

  • List Monitor Events

    List events detected by a monitor

  • Extract

    Extract content from one or more URLs

  • Chat Completions

    Generate a chat completion with web research capabilities

  • Stream Task Run Events

    Stream execution events for a task run

  • Stream Task Run Events (Beta)

    Stream execution events for a task run using the beta endpoint with enhanced event data

  • Fetch Task Group Runs

    Fetch runs from a task group as an SSE stream

  • Stream Task Group Events

    Stream execution events for a task group

  • Stream FindAll Events

    Stream events for a FindAll run

  • Cancel FindAll Run

    Cancel an active FindAll run

  • Extend FindAll Run

    Extend the match limit of a FindAll run

  • Fast Entity Search

    Perform a fast synchronous entity search

  • Ingest FindAll Run

    Create a FindAll run from a natural language objective

  • Trigger Monitor Run

    Manually trigger a monitor execution

  • Cancel Monitor

    Cancel an active monitor

Set Up Your Parallel MCP Server in Minutes

One endpoint. Any framework. Your agent is talking to Parallel in under 10 lines of code.

MCP Clients

Claude Desktop setup guide
Claude Code setup guide
Cursor setup guide
Goose setup guide

Agent Frameworks

Claude Desktop
{
  "mcpServers": {
    "stackone": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote@latest",
        "https://api.stackone.com/mcp?x-account-id=<account_id>",
        "--header",
        "Authorization: Basic <YOUR_BASE64_TOKEN>"
      ]
    }
  }
}

Platform Resources

MCP Code Mode: Keeping Tool Responses Out of Agent Context

Anthropic's code_execution processes data already in context. Custom MCP code mode keeps raw tool responses in a sandbox. 14K tokens vs 500.

11 min

Comparing BM25, TF-IDF, and Hybrid Search for MCP Tool Discovery

Benchmarking BM25, TF-IDF, and hybrid search for MCP tool discovery across 916 tools. The 80/20 TF-IDF/BM25 hybrid hits 21% Top-1 accuracy in under 1ms.

10 min

Indirect Prompt Injection Defense for MCP Tools: A Technical Guide

MCP tools that read emails, CRM records, and tickets are indirect prompt injection vectors. Here's how we built a two-tier defense that scans tool results in ~11ms.

12 min

Parallel MCP Server FAQ

Does StackOne have a Parallel MCP server?
Yes. StackOne offers a hosted Parallel MCP server with 32 pre-built actions, and every action is tested and QA'd by StackOne. Connect it to Claude, Cursor, and any other MCP client, or to any agent framework through the AI Action SDK. It ships with managed agent authentication, prompt injection defense, and tool discovery with server-side execution that preserve your agent's context window and keep reasoning performance.
Parallel MCP server vs direct API integration — what's the difference?
A Parallel MCP server and direct API integration serve different use cases. Direct API integration is for software-to-software — backend code calling Parallel. A Parallel MCP server is for AI agents — MCP clients like Claude and Cursor, plus framework agents built with OpenAI, LangChain, or Vercel AI — discovering and calling Parallel at runtime. StackOne provides both.
How does Parallel authentication work for AI agents?
Parallel authentication for AI agents works through a StackOne Connect Session. Create one via the dashboard or the SDK — you get an auth link and ready-to-paste config for Claude Desktop, Cursor, and other MCP clients. Your user authenticates their own Parallel account; StackOne handles token exchange, storage, and refresh. Credentials never reach the LLM, and each user is isolated via origin_owner_id.
Are Parallel MCP tools vulnerable to prompt injection?
Yes — Parallel MCP tools can be vulnerable to indirect prompt injection. Any tool that reads user-written content — documents, messages, tickets, records, or free-text fields — is a potential vector. StackOne Defender scans every tool response before it enters the agent's context — regex patterns in ~1ms, then a MiniLM classifier in ~4ms. 88.7% accuracy, CPU-only.
What is the context bloat of a Parallel agent and how do I avoid it?
Context bloat happens when Parallel tool schemas and API responses eat your Parallel agent's memory, preventing it from reasoning effectively. A single Parallel query can return a massive JSON response, and connecting multiple tools compounds the problem. Tools Discovery and Code Mode reduce context bloat — loading only relevant tools per query and keeping raw responses out of the agent's context.
Can I limit which actions my Parallel agent can access?
Yes — you can limit which actions your Parallel agent can access directly from the StackOne dashboard. Toggle actions on or off, or restrict them to specific accounts, with no code changes to your agent. Session tokens can be scoped to exact actions so if one leaks, exposure stays contained.
Can I create custom agent actions for my Parallel MCP server?
Yes — you can create custom agent actions for your Parallel MCP server using Connector Builder. It's an integration agent your coding assistant (Claude Code, Cursor, or Copilot) can invoke to research Parallel's API, generate production-ready connector YAML, test against the live API, and validate before you ship.
When should I NOT use a Parallel MCP server?
Skip a Parallel MCP server if your integration is purely software-to-software — direct Parallel API integration is simpler when no AI agent is involved. For deterministic, compliance-critical operations (financial transactions, regulatory reporting), direct API gives you predictable behavior without agent-driven decision-making. MCP shines when AI agents need to dynamically discover and call Parallel actions at runtime.
What AI frameworks and AI clients does the StackOne Parallel MCP server support?
The StackOne Parallel MCP server supports both. MCP clients (paste-and-go apps): Claude Desktop, Claude Code, Cursor, VS Code, Goose. Agent frameworks (code SDKs you build with): OpenAI Agents SDK, Anthropic, Vercel AI, Google ADK, CrewAI, Pydantic AI, LangChain, LangGraph, Azure AI Foundry.

Put your AI agents to work

All the tools you need to build and scale AI agent integrations, with best-in-class connectivity, execution, and security.

Start Free Book Demo