AI Chat Hooks

Chat hooks let you intercept the LLM pipeline at two points: before the prompt reaches the model and after the model returns a response. Use them to enforce compliance, brand voice, or analytics requirements without forking the chat infrastructure.

Pre-hook#

The pre-hook runs after the reader's question is captured and before the LLM is called. It can:

Strip PII (emails, account IDs, tokens) from the user's text.
Inject extra context — current plan, account tier, locale.
Rephrase the question to match canonical terminology.
Block the request entirely with a structured error.

// Example pre-hook payload
{
  question: "What's the price for team@acme.com?",
  context: { plan: "pro_plus", locale: "en" }
}

Post-hook#

The post-hook runs after the LLM produces an answer and before it streams to the reader. It can:

Redact sensitive information that leaked into the response.
Append a disclaimer or a CTA.
Reformat code blocks, links, or headings.
Log the final pair to your own analytics store.

Use cases#

Scenario	Hook
Compliance (GDPR, HIPAA)	Pre + post redaction
Brand voice enforcement	Post-hook style rewrite
Internal analytics	Post-hook mirror to your DB
A/B prompt testing	Pre-hook prompt mutation

MCP tools#

Manage hooks from Claude Code or any MCP client:

set_chat_hooks          # register pre/post-hook URLs and secrets
test_chat_hook          # dry-run a hook against sample data
get_chat_system_prompt  # inspect the current system prompt

Hooks are HTTPS endpoints. Docsbook signs every call with an HMAC SHA-256 signature in the X-Docsbook-Signature header, the same scheme used by webhooks.

Pricing#

Chat hooks are a PRO+ feature ($59/month).

AI Chat — The pipeline the hooks plug into.
MCP Server — Configure hooks remotely from your editor.