The CRM engine you can
trust an AI agent with

Install and verify

# Node 20+, zero runtime dependencies
npm install -g fullstackgtm
fullstackgtm doctor --json        # expect node.ok: true

# prove the whole pipeline with zero credentials (deterministic per --seed)
fullstackgtm audit --demo --json

# credentials come from env — never echo tokens into argv; login reads stdin only
HUBSPOT_ACCESS_TOKEN=... fullstackgtm audit --provider hubspot --save

Machine-readable docs: INSTALL_FOR_AGENTS.md (deterministic install-and-verify with expected outputs) and the package llms.txt documentation map. Headless? FSGTM_NO_BROWSER=1 makes login flows print verification URLs instead of opening a browser.

Full functionality inventory

The core loop

snapshot (versioned canonical export, --since, --archive) · audit (12 built-in deterministic rules, plus custom rules, --rules, --fail-on) · suggest (derive values for requires_human_* placeholders from snapshot evidence, with confidence + reasons) · plans list/show/approve/reject · apply (writes only explicitly approved operation ids) · report (client-ready markdown/HTML deliverable) · diff (snapshot drift, --fail-on-new-findings) · merge (combine snapshots across systems) · bulk-update (governed generic writes: filtered dry-run plans, filters re-verified per record at apply time, cross-record guards) · dedupe (one merge operation per duplicate group, deterministic survivor selection) · reassign (governed ownership handoffs) · fix (one-shot plan from a single audit rule) · rules · doctor

Calls → evidence

call parse (any transcript dialect → evidence-quoted insights; LLM with your own key, or --deterministic free baseline) · call score (coaching rubric, evidence-quoted per dimension) · call link (which deal was this call about, with confidence + reason) · call plan (next steps → the same governed plan lifecycle)

The create gate

resolve account|contact|deal — call before ANY record creation. Exit 0 = safe to create, 2 = exists or ambiguous: do not create. Identity keys match the audit/merge engines exactly; names alone are never identity.

Governed enrichment

enrich append/refresh/ingest/status — pull from Apollo or ingest Clay exports (CSV or webhook), matched deterministically to existing records. Fill-blanks-only plans: enrichment never overwrites a populated field, and every value flows through the same dry-run → approve → apply gate as any other write.

Scheduled re-audits

schedule add/list/remove/enable/disable/run/install/uninstall/status — recurring runs from a read/plan-side allowlist of commands. Scheduling never auto-approves: unattended runs accumulate proposals for human review, never writes.

The market map

market init/capture/classify/worksheet/observe/fronts/axes/report/refresh — vendors × claims as reviewable config, content-addressed page captures, intensity readings with every quoted span verified character-for-character against the stored capture, deterministic front states, PCA-derived axes. Agents can classify directly: worksheet returns claims + judging rules + page texts; submit via observe.

MCP server

fullstackgtm-mcp exposes fullstackgtm_audit, fullstackgtm_rules, fullstackgtm_suggest, fullstackgtm_apply (requires explicit approved ids), fullstackgtm_resolve, fullstackgtm_call_parse, fullstackgtm_market_worksheet, fullstackgtm_market_observe over stdio.

Contracts you can rely on

Exit codes 0/1/2 (success / error / gate or findings threshold) · stable hash ids for findings and operations · --demo --seed for credential-free CI · credential ladder: --token-env → ambient env → stored login → broker pairing · --profile / FULLSTACKGTM_PROFILE multi-org isolation · BYOK LLM (ANTHROPIC_API_KEY / OPENAI_API_KEY) with a deterministic free mode for every LLM feature · providers: HubSpot (read/write), Salesforce (read/write), Stripe (read-only)

The benchmark (CRM-Ops Bench)

Open-source eval harness in the repo: mock HubSpot with REST-fidelity hazards (pagination, search-index lag, concurrent drift), deterministic graders over final state + the server mutation log, CuP and τ-bench pass^k metrics. npm run smoke needs no API keys. Latest results: every framework-equipped arm beats raw on CuP for all six models tested — results and methodology.

Safety invariants (not beta, never change)

Audits are read-only · writes are approval-gated (--approve on specific operation ids) · human decisions are refused, not guessed (requires_human_* placeholders) · quoted evidence is verified verbatim against its source.

Add to your agent

The MCP server is plain stdio — it works in any MCP client. And because the engine is CLI-first with --json everywhere, agents without MCP support can drive it directly from a shell.

Agent skill (any skills-aware agent)

npx skills add fullstackgtm/core

Installs SKILL.md — the compact operating guide for driving the CLI safely.

Claude Code

claude mcp add fullstackgtm -e HUBSPOT_ACCESS_TOKEN=pat-... -- \
  npx -y -p fullstackgtm -p @modelcontextprotocol/sdk -p zod fullstackgtm-mcp

Codex

codex mcp add fullstackgtm --env HUBSPOT_ACCESS_TOKEN=pat-... -- \
  npx -y -p fullstackgtm -p @modelcontextprotocol/sdk -p zod fullstackgtm-mcp

opencode

// opencode.json
{
  "mcp": {
    "fullstackgtm": {
      "type": "local",
      "command": ["npx", "-y", "-p", "fullstackgtm", "-p", "@modelcontextprotocol/sdk", "-p", "zod", "fullstackgtm-mcp"],
      "environment": { "HUBSPOT_ACCESS_TOKEN": "pat-..." }
    }
  }
}

pi

pi's philosophy is CLI tools over MCP — which is exactly what this is. Point pi at INSTALL_FOR_AGENTS.md and it can drive the CLI directly. Prefer MCP? The pi-mcp-adapter reads the standard mcp.json format:

// ~/.pi/agent/mcp.json (or /.pi/mcp.json)
{
  "mcpServers": {
    "fullstackgtm": {
      "command": "npx",
      "args": ["-y", "-p", "fullstackgtm", "-p", "@modelcontextprotocol/sdk", "-p", "zod", "fullstackgtm-mcp"],
      "env": { "HUBSPOT_ACCESS_TOKEN": "pat-..." }
    }
  }
}