Complete reference of all MCP tools exposed by the Golems ecosystem. Use these in Claude Code via .mcp.json configuration.

Setup

The three daemon MCPs (brainlayer, voicelayer, cmuxlayer) run as persistent macOS LaunchAgents. Other MCP servers are configured in .mcp.json:

{
  "brainlayer": {
    "command": "brainlayer-mcp"
  },
  "voicelayer": {
    "command": "voicelayer-mcp"
  },
  "golems-glm": {
    "command": "bun",
    "args": ["run", "packages/shared/src/glm/mcp-server.ts"]
  }
}

Additional MCP servers (supabase, exa, context7, whatsapp-mcp) are configured globally in ~/.claude/.mcp.json.

Then in Claude Code: /tools or use @brainlayer in any prompt.

All MCP Servers

Email Tools (golems-email)

email_getRecent

Get recent emails from the last N hours, filtered by minimum score.

Parameters:

• hours (number, default: 24) — How many hours back to look
• minScore (number, default: 0) — Minimum email score (5=notable, 7=important, 10=urgent)

Returns: Array of emails with subject, sender, score, category, received time

Example:

Get all urgent emails (score 10) from the last 24 hours

email_search

Search emails by keyword in subject or sender.

Parameters:

• query (string, required) — Search term (subject or sender)
• limit (number, default: 20) — Max results to return

Returns: Matching emails with scores and context

Example:

Search for emails from "Microsoft" or about "interview"

email_subscriptions

Get subscription summary: monthly spend, active services, changes.

Parameters: None

Returns:

• Total monthly spend (USD)
• List of active services
• New subscriptions this month
• Cancelled subscriptions

Example:

What's my current monthly subscription spend?

email_urgent

Get urgent emails (score 10) that haven't been notified yet.

Parameters: None

Returns: List of unnotified urgent emails requiring immediate action

Example:

Are there any urgent emails I haven't seen yet?

email_stats

Quick email statistics for the last 24 hours.

Parameters: None

Returns:

• Total email count
• Urgent count (score 10)
• Breakdown by category (job, interview, subscription, etc.)

email_getByGolem

Get emails routed to a specific domain golem.

Parameters:

• golem (enum, required) — Target golem
  • recruitergolem — Job and interview emails
  • tellergolem — Subscription and payment emails
  • claudegolem — Tech updates and urgent items
  • emailgolem — General/newsletter emails
• hours (number, default: 24) — How many hours back

Returns: Emails routed to the specified golem with routing reason

email_draftReply

Generate a reply draft to an email.

Parameters:

• subject (string, required) — Original email subject
• from (string, required) — Original sender email
• snippet (string) — Email preview/body
• category (string) — Email category (interview, job, subscription, etc.)
• intent (enum, required) — Reply intent
  • accept — Accepting offer/position
  • decline — Declining politely
  • interested — Expressing interest, ask for details
  • followup — Following up on previous conversation
  • acknowledge — Thanking them, acknowledging message
• customNote (string) — Optional custom text to prepend

Returns: Generated reply draft (template-based, not LLM-generated)

Financial Tools (golems-email — via TellerGolem)

teller_monthlyReport

Generate monthly spending report.

Parameters:

• month (string, default: current) — Month in YYYY-MM format

Returns:

• month — Requested month
• totalSpend — Total USD spent
• byCategory — Breakdown by IRS Schedule C category
  • advertising, insurance, office, software, education, travel, meals, professional-services, other
• byVendor — Breakdown by vendor/service
• subscriptionCount — Active services this month

Example:

What did I spend on software subscriptions in 2026-01?

teller_taxSummary

Generate annual tax report for a year.

Parameters:

• year (number, default: current) — Tax year (e.g., 2026)

Returns:

• year — Requested year
• totalDeductible — Total IRS-deductible expenses
• byCategory — Each category with:
  • total — Total for category
  • items — Line items (vendor + amount)

Example:

Generate my 2025 tax report for Schedule C

Job Tools (golems-jobs)

jobs_getRecent

Get recently scraped jobs from the latest results file.

Parameters:

• limit (number, default: 20) — Max results to return

Returns: Latest jobs with company, title, and URL

jobs_search

Search jobs by keyword (title, company, or description).

Parameters:

• query (string, required) — Search term

Returns: Matching scraped jobs without scores

jobs_getHot

Get hot job matches (score 8+).

Parameters: None

Returns: Hot matches with company, title, score, reason, and URL

jobs_watchlist

Get jobs from watchlist companies.

Parameters: None

Returns: Jobs from companies in the watchlist

jobs_stats

Quick job statistics.

Parameters: None

Returns:

• Total jobs scraped
• Total jobs seen
• Total batches
• Hot/warm/cold counts

Memory Tools (brainlayer)

BrainLayer provides persistent memory across Claude Code sessions — semantic search over 284K+ indexed conversation chunks using bge-large-en-v1.5 embeddings (1024 dims) and sqlite-vec. BrainBar daemon (209KB native Swift binary) provides real-time indexing via hooks.

brainlayer_search

Semantic + keyword hybrid search across all past Claude Code sessions.

Parameters:

• query (string, required) — Natural language search query
• project (string, optional) — Filter by project path (e.g., -Users-etanheyman-Gits-golems)
• content_type (enum, optional) — Filter by type: ai_code, stack_trace, user_message, assistant_text, file_read, git_diff
• source (enum, optional) — Filter by source: claude_code, whatsapp, youtube, all
• tag (string, optional) — Filter by enrichment tag (e.g., bug-fix, authentication)
• intent (enum, optional) — Filter by intent: debugging, designing, configuring, discussing, deciding, implementing, reviewing
• importance_min (number, optional) — Minimum importance score (1-10)
• num_results (number, default: 5) — Max results

Returns: Matching chunks with content, score, project, and timestamp

brainlayer_context

Get surrounding conversation context for a search result.

Parameters:

• chunk_id (string, required) — Chunk ID from a search result
• before (number, default: 3) — Chunks before target
• after (number, default: 3) — Chunks after target

Returns: The chunk plus surrounding conversation turns for full context

brainlayer_stats

Knowledge base statistics.

Returns: Total chunks, projects indexed, database size, content type breakdown

brainlayer_list_projects

List all indexed projects.

Returns: Project paths with chunk counts

brainlayer_file_timeline

Get the interaction timeline for a specific file across sessions.

Parameters:

• file_path (string, required) — File path or partial path (e.g., telegram-bot.ts)
• project (string, optional) — Filter by project
• limit (number, default: 50) — Max interactions

Returns: Chronological list of all sessions that read, edited, or wrote to the file

brainlayer_operations

Get logical operation groups for a session (read-edit-test cycles, research chains, debug sequences).

Parameters:

• session_id (string, required) — Session ID to query

Returns: Grouped operations with types and file lists

brainlayer_regression

Analyze a file for regressions — shows the last successful operation and all changes after it.

Parameters:

• file_path (string, required) — File path to analyze
• project (string, optional) — Filter by project

Returns: Last success point and subsequent changes

brainlayer_plan_links

Query plan-linked sessions — which plan/phase a session belongs to, or all sessions for a plan.

Parameters:

• session_id (string, optional) — Session ID to look up
• plan_name (string, optional) — Plan name to query
• project (string, optional) — Filter by project

Returns: Plan/phase associations for sessions

brain_store

Store a new memory chunk with tags and importance scoring.

Parameters:

• content (string, required) — Text content to store
• tags (array, optional) — Tags for categorization (e.g., ["decision", "6pm-mini"])
• importance (number, optional) — Importance score 1-10

Returns: Stored chunk ID and confirmation

brain_digest

Digest large content into structured knowledge — extracts entities, relations, and action items. Three modes: auto (detect content type), conversation (Claude Code sessions), document (reports, research).

Parameters:

• raw_content (string, required) — Raw text to digest
• mode (enum, optional) — auto, conversation, or document

Returns: Extracted entities, relations, and action items stored in BrainLayer

brain_entity

Query a specific entity by name — returns all known facts, relations, and mentions.

Parameters:

• name (string, required) — Entity name to look up

Returns: Entity details with relations and source chunks

brain_expand

Expand a search result with related knowledge — follows entity relations to surface connected information.

Parameters:

• chunk_id (string, required) — Starting chunk ID
• depth (number, default: 1) — How many relation hops to follow

Returns: Related chunks and entities

brain_tags

List all tags in the knowledge base with counts.

Returns: Tag list with frequency counts

brain_subscribe / brain_unsubscribe

Subscribe to real-time updates for a topic or entity. Notifications arrive via the pubsub system.

Parameters:

• topic (string, required) — Topic or entity name to watch

brain_update

Update an existing chunk's content, tags, or importance.

Parameters:

• chunk_id (string, required) — Chunk ID to update
• content (string, optional) — New content
• tags (array, optional) — Updated tags
• importance (number, optional) — Updated importance score

Returns: Updated chunk confirmation

Local LLM Tools (golems-glm)

GLM-4.7-Flash running locally via Ollama. Used for context reduction — summarize content before loading into Opus context.

glm_summarize

Summarize text to key points using local GLM model.

Parameters:

• text (string, required) — Text to summarize
• maxSentences (number, default: 3) — Maximum sentences in summary

Returns: Condensed summary of the input text

glm_score

Structured JSON classification/scoring using local GLM model.

Parameters:

• text (string, required) — Text to classify
• prompt (string, required) — Classification instructions
• schema (object, required) — JSON schema for output

Returns: Structured JSON matching the provided schema

Database Tools (supabase)

Full Supabase database access via the official @supabase/mcp-server-supabase server. Provides read/write access to all Golems tables.

Key capabilities: table listing, SQL queries, migrations, edge functions, type generation. See Supabase MCP docs for the full tool list.

Web Search Tools (exa)

Exa AI-powered web search via exa-mcp-server. Used by RecruiterGolem for company research and contact discovery.

Key capabilities: semantic web search, code context retrieval, company research.

Example Workflows

Find Urgent Items to Handle Now

1. Get urgent emails: email_urgent
2. For each urgent email about an interview:
   - Draft reply with intent="interested"
3. Check if jobs matched: jobs_getHot

Monthly Budget Review

1. Get subscription summary: email_subscriptions
2. Get monthly report: teller_monthlyReport
3. Check for payment failures: email_urgent
4. Identify cost-reduction opportunities

Interview Preparation

1. Get recent interview emails: email_getRecent with minScore=8
2. Search related job postings: jobs_search with company name
3. Check if company is in watchlist: jobs_watchlist
4. Draft followup emails

Tax Prep

1. Get annual tax report: teller_taxSummary for 2025
2. Review each category for accuracy
3. Identify missing categories
4. Export for accountant

Integration Notes

• Email tools use Supabase directly (cloud-first architecture)
• Job tools query Supabase golem_jobs and scrape_activity tables
• BrainLayer tools query local sqlite-vec database (284K+ chunks, FTS5 + vector hybrid search, faceted tag schema)
• GLM tools run locally via Ollama (no network, ~3-8s per call on M1 Pro)
• Scoring: Email scores 1-10 (10=urgent), Job scores 1-10 (8+=hot match)
• Categories: Email categories are semantic (job, interview, subscription, tech-update, newsletter, promo, social, other)

See individual golem documentation for deeper configuration and usage.