Skip to main content
Glama

headlo-mcp

MCP server for Headlo — gives Claude Desktop and Claude Code access to your Headlo collections, records, pages, and components.

Use it as a standalone server, or install as a library to add your own tools and intercept built-in ones.

Quickstart

Claude Desktop

Edit claude_desktop_config.json (Mac: ~/Library/Application Support/Claude/, Windows: %APPDATA%\Claude\):

{
  "mcpServers": {
    "headlo": {
      "command": "npx",
      "args": ["headlo-mcp"],
      "env": {
        "HEADLO_API_TOKEN": "hl_pat_your_token_here"
      }
    }
  }
}

Restart Claude Desktop. You should see the Headlo tools available.

Claude Code

claude mcp add headlo -e HEADLO_API_TOKEN=hl_pat_your_token_here -- npx headlo-mcp

Remote MCP (no install)

If you don't need to extend the server, point Claude directly at the Headlo API:

claude mcp add headlo https://api.headlo.com/v1/mcp --header "Authorization: Bearer hl_pat_your_token_here"

Related MCP server: GigAPI MCP Server

Extending the server

Install as a library to add your own tools or intercept built-in ones:

npm install headlo-mcp @modelcontextprotocol/sdk

Add custom tools

import { createHeadloServer } from 'headlo-mcp'
import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js'
import { z } from 'zod'

const server = createHeadloServer()

// your own tool alongside the built-ins
server.tool('my_crm_lookup', 'Look up a contact in my CRM', { email: z.string() }, async ({ email }) => {
  const contact = await myCRM.find(email)
  return { content: [{ type: 'text', text: JSON.stringify(contact) }] }
})

await server.connect(new StdioServerTransport())

Intercept built-in tool calls

Pass a wrap map to run code before or after any built-in tool:

import { createHeadloServer } from 'headlo-mcp'
import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js'

const server = createHeadloServer({
  wrap: {
    // validate before creating a record, sync to CRM after
    create_record: async (args, next) => {
      await mySchema.validate(args)
      const result = await next(args)
      await myCRM.push(result)
      return result
    },

    // log every CAP session to your analytics
    cap_capture_expert: async (args, next) => {
      const result = await next(args)
      await myAnalytics.track(args.cap_id, result)
      return result
    },
  }
})

await server.connect(new StdioServerTransport())

next(args) calls the original Headlo handler. You can modify args before passing them, or modify the result before returning it.


Environment variables

Variable

Required

Description

HEADLO_API_TOKEN

Yes

Your Headlo API token (hl_pat_...)

HEADLO_API_URL

No

API base URL (default: https://api.headlo.com)

HEADLO_ANON_KEY

No

Anon key for public tool calls


Built-in tools

Context

Tool

Description

get_context

List all sites and workspaces — call this first

Collections

Tool

Description

list_collections

List all collections in a workspace

create_collection

Create a new collection with a schema

update_collection

Update a collection label or schema

Records

Tool

Description

list_records

List records in a collection (includes drafts)

get_record

Get a single record by ID

create_record

Create a new record

update_record

Update a record (partial — only provided fields change)

delete_record

Delete a record

Pages

Tool

Description

list_pages

List all pages for a site

create_page

Create a new page

update_page

Update a page title, layout, or status

Modules

Tool

Description

list_modules_editor

List all modules on a page

create_module

Add a module to a page slot

update_module

Update a module's record, options, or template

delete_module

Remove a module from a page

Public

Tool

Description

list_page_modules

List modules on a page (anon auth)

get_module

Get a single module (anon auth)

CAP (Context Accumulation Protocol)

CAP is Headlo's expert intake system — structured question flows backed by pre-researched knowledge that produce better AI answers than generic prompts.

Tool

Description

create_expert_protocol

Create a new CAP with intake questions and knowledge probes for a question on the Ask network

cap_capture_expert

Run a CAP as a live multi-turn intake session — call with empty message to start, pass answers in subsequent calls

cap_edit_expert

Conversationally edit an existing CAP — add/remove questions, edit options

Running a CAP session (multi-turn):

// Turn 1 — load the CAP (empty message)
const t1 = await cap_capture_expert({ cap_id: 'abc123def456', message: '' })
// → { response: "Question 1 of 3: ...", session_state: {...}, complete: false }

// Turn 2 — answer the first question, pass session_state back
const t2 = await cap_capture_expert({ cap_id: 'abc123def456', message: 'Downtown', session_state: t1.session_state })
// → { response: "Question 2 of 3: ...", session_state: {...}, complete: false }

// Turn 3 — final answer
const t3 = await cap_capture_expert({ cap_id: 'abc123def456', message: 'Coffee shops', session_state: t2.session_state })
// → { response: "...", complete: true, answer: "..." }

Intercepting CAP calls with middleware:

const server = createHeadloServer({
  wrap: {
    cap_capture_expert: async (args, next) => {
      const result = await next(args)
      // log every completed session to your analytics
      if (result.complete) await myAnalytics.track(args.cap_id, result)
      return result
    },
  }
})

Onboarding

Tool

Description

setup_agency

Create a new agency/workspace

create_site

Create a new site

save_branding

Save logo and brand color

get_onboarding_progress

Get current onboarding step


License

Elastic License 2.0 — © Headlo

Source available. Free for internal use. Production self-hosting requires a commercial license.

Built by Headlo.

Available Tools

25 tools
cap_capture_expertA

Run an existing CAP (Context Accumulation Protocol) as a live intake session. Pass cap_id and an empty message to load and start the first question. Answer each question in subsequent calls. On completion returns a synthesized answer built from the CAP knowledge pairs. Returns { response, session_state, complete, answer? } as JSON.

Inputs: cap_id (required), message, session_state.

cap_edit_expertA

Conversationally edit an existing CAP — add questions (with AI-generated knowledge probes), remove questions, or edit multiple-choice options. Pass cap_id and empty message to load. Returns { response, session_state, complete, cap_id? } as JSON.

Inputs: cap_id (required), message, session_state.

create_collectionC

Create a new collection

Inputs: label (required), workspace_id (required), collection_id (required), schema.

create_expert_protocolB

Create a CAP (Context Accumulation Protocol) for a question on the Headlo Ask network. Before writing any knowledge probes, always do a web search to gather current, specific facts. Each probe answer must contain 4–6 specific facts sourced from your research.

Inputs: bounty_id (required), questions (required).

create_moduleC

Add a module to a page slot

Inputs: page_id (required), slot_id (required), template_id (required), collection_id, +2 more.

create_pageC

Create a new page

Inputs: page_id (required), site_id (required), layout_id (required), title.

create_recordC

Create a new record in a collection

Inputs: data (required), collection_id (required), status, workspace_id.

create_siteB

Create a new site under the active agency

Inputs: name (required).

delete_moduleB

Remove a module from a page

Inputs: confirm (required), module_id (required).

delete_recordD

Delete a record

Inputs: confirm (required), record_id (required), collection_id (required), workspace_id.

get_contextA

Get all sites and workspaces for the active agency — call this first to orient yourself

Inputs: no named parameters.

get_moduleB

Get a single module with its records (public, anon key auth)

Inputs: module_id (required).

get_onboarding_progressA

Get the current onboarding step and completion status

Inputs: no named parameters.

get_recordA

Get a single record by ID with all field values

Inputs: record_id (required), collection_id (required), workspace_id.

list_collectionsA

List all collections for the active workspace

Inputs: workspace_id.

list_modules_editorB

List all modules on a page (editor auth)

Inputs: page_id (required).

list_page_modulesA

List all modules on a page with their records (public, anon key auth)

Inputs: page_id (required).

list_pagesB

List all pages for a site

Inputs: site_id (required).

list_recordsC

List records in a collection (includes drafts)

Inputs: collection_id (required), limit, offset, status, +1 more.

save_brandingC

Save branding details for the active agency

Inputs: logo_url, primary_color.

setup_agencyA

Create a new agency/workspace during onboarding

Inputs: name (required).

update_collectionB

Update a collection label or schema

Inputs: collection_id (required), label, schema.

update_moduleB

Update a module — change its record, options, or template

Inputs: module_id (required), template_id, template_options, collection_record_id.

update_pageC

Update a page title or layout

Inputs: page_id (required), title, status, layout_id.

update_recordA

Update a record — partial update, only provided fields are changed

Inputs: record_id (required), collection_id (required), data, status, +1 more.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    TDQS

    C2.8/5.0

    Scored across 25 tools

    Disambiguation4/5

    Most tools have a clear and distinct resource+action purpose (e.g., create_record vs update_record, list_pages vs create_page). The main confusion risk is between list_page_modules and list_modules_editor, which differ only by auth context, and possibly get_module vs list_page_modules, though the singular/plural distinction helps.

    Naming Consistency3/5

    The dominant pattern is verb_noun (get_context, create_site, update_record), but there are notable deviations: 'cap_capture_expert' and 'cap_edit_expert' use a prefix and less predictable structure, and 'list_modules_editor' is inconsistent with 'list_page_modules'. Overall still readable but not uniform.

    Tool Count4/5

    25 tools is at the high end but well-justified given the server covers multiple domains: onboarding, sites, pages, collections, records, modules, and expert protocols. Each tool serves a distinct purpose, though the count feels slightly heavy for a single server.

    Completeness2/5

    There are significant lifecycle gaps: no delete or update for sites, no delete for pages, no delete for collections, and no get_collection or update_site. Records and modules have full CRUD, but the broader content model lacks essential operations that agents would likely need.

    Maintenance

    ActivityInactive
    ResponsivenessNo recent community issues

    Related MCP Connectors

    Related MCP Servers