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# Connect to your AI tools (MCP)

How to reach your KnitKnot workspace from Claude, ChatGPT, or any MCP-capable client — so you can query your benchmark and act on it without leaving your assistant.

KnitKnot exposes a curated view of your workspace through the **Model Context Protocol (MCP)** — an open standard that lets AI assistants and agent tools call external services. Connect it to ask about your AI presence, inspect issues and their evidence, or work with playbooks from inside the tool you already use.

The server lives at `https://mcp.knitknot.ai/mcp/`.

## Connect a client

The [console](https://app.knitknot.ai) has a dedicated **connect** page that walks you through setup for MCP-capable clients like Claude. There are two ways to authenticate:

  • - **Sign in with your account** — the standard OAuth flow. Approve the connection once and your client is linked to your workspace, using the same identity and permissions you have in the console.
  • - **API key** — for scripts, agents, or clients that can't do an interactive sign-in, generate a key and pass it as a bearer token. Treat the key like a password; it carries your workspace access.

Either way, the connection is scoped to your workspace — the assistant sees what you'd see.

## What you can do through it

Customer-facing MCP tools are controlled by an explicit allowlist. At a high level you can:

  • - **Query your presence** — pull the competitive overview and score trend for the authorized workspace.
  • - **Work the competitive picture** — list competitors and inspect an individual competitor record.
  • - **Inspect gaps** — list issues, open the evidence behind an issue, and retrieve source intelligence.
  • - **Plan coverage** — list demand topics and prompts, inspect a topic or opportunity, and review prompt coverage.
  • - **Work playbooks** — list and open playbooks, then update a supported playbook status.
  • - **Orient an agent** — retrieve workspace context before it starts a task.

MCP and the console use the same authorized workspace data. A supported playbook status change made through an assistant appears in the console.

## When to use it

Reach for MCP when your workflow already lives in an AI assistant — for example, to ask which issue needs attention, bring source evidence into research, or move a playbook into progress. For dense review, configuration, benchmark execution, and publishing, the console is still the richer surface.

Next: back to [running benchmarks](/docs/run-and-schedule-benchmarks/), or read how gaps become [issues and playbooks](/docs/issues-and-playbooks/).

Raw mirror of this content: https://knitknot.ai/docs/connect-to-ai-tools.md. Site-wide summary: /llms.txt · full content: /llms-full.txt

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Connect to your AI tools (MCP)

How to reach your KnitKnot workspace from Claude, ChatGPT, or any MCP-capable client — so you can query your benchmark and act on it without leaving your assistant.

Updated

KnitKnot exposes a curated view of your workspace through the Model Context Protocol (MCP) — an open standard that lets AI assistants and agent tools call external services. Connect it to ask about your AI presence, inspect issues and their evidence, or work with playbooks from inside the tool you already use.

The server lives at https://mcp.knitknot.ai/mcp/.

Connect a client

The console has a dedicated connect page that walks you through setup for MCP-capable clients like Claude. There are two ways to authenticate:

  • Sign in with your account — the standard OAuth flow. Approve the connection once and your client is linked to your workspace, using the same identity and permissions you have in the console.
  • API key — for scripts, agents, or clients that can’t do an interactive sign-in, generate a key and pass it as a bearer token. Treat the key like a password; it carries your workspace access.

Either way, the connection is scoped to your workspace — the assistant sees what you’d see.

What you can do through it

Customer-facing MCP tools are controlled by an explicit allowlist. At a high level you can:

  • Query your presence — pull the competitive overview and score trend for the authorized workspace.
  • Work the competitive picture — list competitors and inspect an individual competitor record.
  • Inspect gaps — list issues, open the evidence behind an issue, and retrieve source intelligence.
  • Plan coverage — list demand topics and prompts, inspect a topic or opportunity, and review prompt coverage.
  • Work playbooks — list and open playbooks, then update a supported playbook status.
  • Orient an agent — retrieve workspace context before it starts a task.

MCP and the console use the same authorized workspace data. A supported playbook status change made through an assistant appears in the console.

When to use it

Reach for MCP when your workflow already lives in an AI assistant — for example, to ask which issue needs attention, bring source evidence into research, or move a playbook into progress. For dense review, configuration, benchmark execution, and publishing, the console is still the richer surface.

Next: back to running benchmarks, or read how gaps become issues and playbooks.