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Peec AI MCP Server
for AI Agents

Connect your AI agent to StackOne's Peec AI MCP server and give it 36 MCP tools out of the box. Auth, tool execution, and security all managed.

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Peec AI MCP Server
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36 Agent Actions

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

Authentication

Agent Tool Authentication

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

Agent Auth →

Security

Agent Protection

Every Peec AI 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 Peec AI call.

Tools Discovery →

What is the Peec AI MCP Server?

A Peec AI MCP server lets AI agents read and write Peec AI data through the Model Context Protocol — Anthropic's open standard for connecting LLMs to external tools. StackOne's Peec AI MCP server ships with 36 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 Peec AI MCP Tools

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

Actions

  • Create Actions

    Record work the customer decided to do themselves as actions, in the same shape as the ones Peec generates, so they sort, filter and move through the same statuses. The kind picked decides an action's type and group. The linked topic and prompts are resolved against the project, and an item naming one it does not hold lands in `rejected` on its own — the batch never fails as a whole.

  • List Actions

    The project's actions, the ones Peec generated and the ones the customer wrote themselves alike: what to do, on which page or site, and how much impact it is expected to have if it is done. `created_by` says which of the two an action is, and only a customer's own can be deleted or reshaped. Filters combine with AND, except `targets`, `groups`, `archetypes` and `platforms`, which combine with OR across the four. The action detail endpoint carries a single action's steps, brief, evidence and impacted prompts.

  • Get Action

    One action in full: what to do, the fields the customer owns on it — notes, priority, assignee, due date, tags and repeat — the steps to work through, the content brief when there is one with its sections, the competitor demand behind it, the prompts it affects and the terms competitors use on the topic. A site audit action carries no brief and no opportunity, and its description and steps are the whole fix. An id this project has no published action for is a 404, whether it belongs to another project, to an action type this API does not publish for the project, or to nothing at all. A content optimisation's article, suggestions and score are not returned here, and the Customer API publishes no endpoint that returns them.

  • Update Actions

    Rewrite the fields a customer owns, on actions they wrote or ones Peec generated. An edit is stored beside the generated wording rather than in it, so a regeneration cannot discard it. Only a customer's own action can change shape: the type and group of a generated one say what Peec found, and an item trying to move them lands in `rejected`. A field the item leaves out keeps what it held; `null` clears the ones that can be cleared, and an item naming no field at all is rejected rather than recorded as an edit. Ids the project publishes no action under are returned in `skipped` — the batch never fails as a whole. A content optimisation is reachable only on a project with content optimisation enabled; elsewhere its id is returned in `skipped`.

  • Delete Actions

    Remove actions the customer wrote themselves. A generated action reads here exactly as a missing one does and lands in `skipped`: those are declined or dismissed through the status endpoint instead, so the list keeps a record of what Peec suggested and the customer turned down.

Brands

  • Create Brand

    Create a new brand within a project

  • List Brands

    List the brands of a project

  • Update Brand

    Update a brand within a project. Changes to name, regex, or aliases trigger a background recalculation of metrics. While recalculation is in progress, further updates to these fields are blocked (409 Conflict) until it completes.

  • Delete Brand

    Delete a brand within a project.

Brand Social Channels

  • List Brand Social Channels

    List the social channels configured for a project, optionally filtered to one brand.

  • Set Brand Social Channels

    Replace the complete list of social channels configured for a brand. Pass an empty list to remove them all.

Categories

  • Create Categories

    Create product categories. Each item takes a `name` and an optional `parent_id` (omit or null for a top-level category). Items apply in order. Returns per-item results { created, rejected } — the batch never fails as a whole. Rejection reasons: parent_not_found, name_conflict (a sibling already has that name).

  • List Categories

    List a project's product categories. Each row carries its full `path` and its `parent_id`, so the flat list reconstructs the category tree. Use the returned `id` as a `category_id` when creating, updating, or assigning products.

  • Update Categories

    Rename and/or reparent categories by id. Each item sets `name` (rename), `parent_id` (move; null = promote to top level), or both — at least one is required. A combined edit is applied move-then-rename. Returns per-item results { updated, rejected } — the batch never fails as a whole. Rejection reasons: not_found, name_conflict, invalid_move (would nest a category inside itself).

  • Delete Categories

    Delete categories by id. Each one's child categories and products move up to its parent, then it is removed. Returns per-item results { deleted, rejected } — the batch never fails as a whole. Rejection reasons: not_found, name_conflict (a child moving up would clash with an existing sibling name).

Products

  • Create Products

    Create products. Each item is independent: valid products are created and per-item failures are returned in `rejected`, so the batch never fails as a whole. Optional `category_ids` assigns the product to categories.

  • List Products

    List a project's products with headline metrics (mention_count, win_count, avg_position, avg_rating, visibility, share_of_voice) over the date range, filterable by category, merchant, brand, country, model channel, topic, and tag. Paginated.

  • Get Product

    Get one product's detail over a date range: headline metrics (visibility, win_rate, avg_position, avg_rating, mention_count) plus a delta for each against the immediately preceding equal-length period, the catalog metadata (brand, description, image, source, first-seen date), the effective price range with any overrides, and the median AI-mention price. Visibility is divided by the product's relevant-prompt chats, not all shopping chats. avg_rating is the mean 0–5 star rating across the product's AI mentions (null when none carried a rating). Returns data: null when the product is not in the project.

  • Update Products

    Update products (name, description, image, price overrides, categories). Each item is independent: items with nothing to apply (not found, no changes, duplicate id) are reported in `skipped`, and unsatisfiable changes (name conflict, unknown category) in `rejected`, so the batch never fails as a whole.

  • Delete Products

    Delete products. Ids that don't exist in the project are returned in `skipped`.

Other (16)

  • List Action Platforms

    The third-party sites a project has actions on, as values for the action list's `platforms` filter. A platform is whichever domain AI engines cite for this brand, so the set is per project and open-ended. Own-site actions carry no platform and are absent here.

  • List Custom URL Classifications

    List the custom URL classifications defined for a project. These complement the built-in classifications and can be assigned to URLs.

  • List Global Brands

    Search Peec's global brand catalog — the shared registry of real-world brands that products attach to via `global_brand_id`. Use it to find the `global_brand_id` for a brand when creating products. This is separate from `GET /brands`, which lists the brands a project tracks for AI-visibility reporting; the two use different ids. Primarily used with `search`. Pass `ownership` (`own`, `competitor`, or `all`) instead to list the project's shopping brands ranked by mentions, for use as shopping brand filters.

  • List Model Channels

    List the model channels a project's reports can be broken down and filtered by. A model channel is a stable identifier for an AI surface — openai-0 is ChatGPT UI, perplexity-1 the Perplexity API — that stays constant as the model behind it is upgraded or replaced, so filtering by channel keeps a continuous view of a surface where filtering by model fragments it across ids. The returned ids are what every model_channel_ids filter on this connector accepts.

  • List Models

    List the individual AI model versions a project has answers from. Peec documents this endpoint as deprecated in favour of List Model Channels, and a channel is the better filter for anything trending over time; it remains the way to resolve the model_ids filter that Get Shopping Attributes accepts, which takes model versions rather than channels.

  • Get Shopping Attributes

    The LLM-extracted attribute comparison grid for a product (scope=product) or the whole catalog (scope=overview), split by tab into characteristics, facts, and dimensions. Columns are competing brands (compare_by=brand) or competing products (compare_by=product, scope=product only). Deltas are computed against an explicit comparison window (previous_start_date/previous_end_date) or the equal-length window immediately before.

  • Get Shopping Summary

    Return aggregate shopping metrics over a date range. Delta fields compare against an explicit previous window or the auto-derived previous period.

  • Get Shopping Trend

    Return a product or brand shopping time series over a date range. Requires bucket and exactly one of product_ids or brand_ids.

  • List Shopping Performance

    List ranked product or category shopping performance over a date range.

  • List Shopping Demand

    List top shopping queries, web-search fan-out queries, or query terms over a date range.

  • List Merchants

    List the merchants (sellers) whose product offers surfaced in AI answers, ranked over a date range: mentions, share of voice against the other merchants, buy-box win rate, average position, and average star rating, each with a delta against the previous window. Scope the population with category_ids or product_ids to get per-category or per-product seller breakdowns.

  • List Prompts

    List the prompts of a project

  • List Tags

    List the tags of a project. Tags include user-created tags and Peec-managed system tags (is_system=true). Each tag carries a `group`: for system tags this is the mutually-exclusive dimension (branding or intentType); for user tags it is the user-defined group name (or null). Pass `group` to filter to a single user-defined group. System tags cannot be edited or deleted.

  • List Topics

    List the topics of a project

  • Update Action Status

    Move actions to a status by id: accept, reject, complete, delete, or send one back to `PENDING`. Ids the project publishes no action under are returned in `skipped` — the batch never fails as a whole. An action that is already deleted reads as absent unless the key is a platform admin one. A content optimisation is reachable only on a project with content optimisation enabled; elsewhere its id is returned in `skipped`.

  • Set Action Step Completion

    Record which of an action's steps are done. Each entry names one action and either sets `completed` for every step it holds, or lists the steps to write; a step in turn either sets `completed` for all of its options or names them individually, each with its own value, so one call can tick some and untick others. An action is all or nothing: anything Peec cannot apply leaves every step on it untouched and comes back under `skipped` or `rejected`, so a partly-written action is never a state you have to read back. Other actions in the same call are unaffected. The action's own status is never changed by this endpoint, including when every step on it is complete. An action that is already deleted reads as absent unless the key is a platform admin one. A content optimisation is reachable only on a project with content optimisation enabled; elsewhere its id is returned in `skipped`.

Peec AI AI Agent Use Cases

Connect your AI agent to Peec AI and help your team scale the marketing operations they run by hand today.

Lead Nurture Sequences

Use StackOne to connect your AI agent to your marketing automation, CRM, and email tools to automate lead nurture email sequences.

View
HubSpotSalesforceActiveCampaignKlaviyoSendGridMailchimpLemlistGmail
Campaign Performance Reports

Use StackOne to connect your AI agent to your marketing automation, email, and analytics platforms to automate campaign performance reporting.

View
HubSpotMailchimpActiveCampaignKlaviyoSalesforceBrazeSendGridMarketo
Content Repurposing

Use StackOne to connect your AI agent to your document stores, email platforms, and messaging tools to automate content repurposing and distribution.

View
Google DriveConfluenceNotionMailchimpSendGridSlackGmailMicrosoft Teams

Set Up Your Peec AI MCP Server in Minutes

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

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>"
      ]
    }
  }
}

Check More marketing MCP Servers

Peec AI MCP Server FAQ

Does StackOne have a Peec AI MCP server?
Yes. StackOne offers a hosted Peec AI MCP server with 36 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.
Peec AI MCP server vs direct API integration — what's the difference?
A Peec AI MCP server and direct API integration serve different use cases. Direct API integration is for software-to-software — backend code calling Peec AI. A Peec AI 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 Peec AI at runtime. StackOne provides both.
How does Peec AI authentication work for AI agents?
Peec AI 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 Peec AI account; StackOne handles token exchange, storage, and refresh. Credentials never reach the LLM, and each user is isolated via origin_owner_id.
Are Peec AI MCP tools vulnerable to prompt injection?
Yes — Peec AI 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 Peec AI agent and how do I avoid it?
Context bloat happens when Peec AI tool schemas and API responses eat your Peec AI agent's memory, preventing it from reasoning effectively. A single Peec AI 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 Peec AI agent can access?
Yes — you can limit which actions your Peec AI 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 Peec AI MCP server?
Yes — you can create custom agent actions for your Peec AI MCP server using Connector Builder. It's an integration agent your coding assistant (Claude Code, Cursor, or Copilot) can invoke to research Peec AI's API, generate production-ready connector YAML, test against the live API, and validate before you ship.
When should I NOT use a Peec AI MCP server?
Skip a Peec AI MCP server if your integration is purely software-to-software — direct Peec AI 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 Peec AI actions at runtime.
What AI frameworks and AI clients does the StackOne Peec AI MCP server support?
The StackOne Peec AI 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.