# JackHamr

**Canonical:** https://apis.io/providers/jackhamr-ai/  
**Website:** https://jackhamr.ai/  
**APIs profiled:** 0

JackHamr provides an AI Dev Team platform that builds, tests, reviews and ships software using specialist AI agents. Users can start for free with $30 credit, no card needed, and scale up to 16 cores and 64 GB RAM per agent. The service offers cloud‑hosted agents for roles like spec writing, development, QA, and product review, with one‑click deployment to custom domains. It includes pricing, use cases, principles, and extensive documentation.

## Kin Score — 19.4 / 100 (emerging)

Scored 2026-11-13 under rubric 0.28.0.

| Facet | Score |
|---|---|
| Discoverability | 50.0 |
| Contract Quality | 0.0 |
| Contract Governance | 0.0 |
| Operational Transparency | 0.0 |
| Developer Ergonomics | 31.0 |
| Access Clarity | 42.6 |

Regulatory layer — **Horizontal (data, software, accessibility, platform)**: 12.7 (matched via fallback).

## Agent readiness — 18.7 (agent-aware)

| Dimension | Value |
|---|---|
| Spec Presence | no |
| Agentic Access | no |
| Reversibility Documented | no |
| MCP Server | no |
| Auth Clarity | served |
| Idempotency | no |
| Error Semantics | no |
| OpenAPI Examples | no |
| Rate Limit Signal | no |
| Event Surface Described | no |
| Agent Skills | no |
| Well Known Catalog | no |
| Consent Identity | no |
| Agent Card | no |
| Dry Run Mode | no |
| Delegated Identity | served |
| Protected Resource Metadata | verified |
| Dynamic Client Registration | yes |
| Agentic Commerce | no |
| Pagination Ratio | na |
| Operation Distinctiveness | na |
| MCP Tool Quality | na |

## Access

Free — onboarding: unknown, pricing: free, trial: no (confidence: low).

## Security (1)

- **JackHamr Domain Security** — TLSv1.3 · HSTS · DMARC

## Plans (1)

- **JackHamr Plans Pricing**

## Tags

Artificial Intelligence, DevOps, Platform, Automation, Software-as-a-Service

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Profiled by [API Evangelist](https://apievangelist.com) and published on [APIs.io](https://apis.io/providers/jackhamr-ai/). Scores are computed from the provider's own public artifacts under a published rubric.