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ITHZ MCP is a local-first project memory layer for AI agents, developers, and technical teams. It stores project decisions, gates, risks, context packs, workflow notes, and checkpoints in a project-owned memory zone instead of hiding them in chat history or vendor-side memory.

The system helps AI-assisted projects stay auditable: agents can load bounded context, remember durable decisions, distinguish allowed and blocked claims, and leave explicit checkpoints after work. It is designed for teams that want safer agent collaboration, transparent project memory, redaction-aware workflows, and inspectable handoff trails while keeping source code in Git and project memory under local control.

ITHZ MCP is not a cloud sync product or a replacement for Git. It complements existing developer workflows by making agent memory deterministic, reviewable, and tied to the project itself.

ITHZ MCP se trouve dans Developer Documentation Tools, Digital Asset Management (DAM), AI Agent Builders catégories.

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Principales caractéristiques de ITHZ MCP

• Project ledger
  Keeps allowed claims, blocked claims, reviewer notes, and replication records explicit and inspectable.
• MCP host installers
  Includes setup paths and sample configs for Codex, Claude Code, Cursor, Antigravity, and generic MCP clients.
• Automatic checkpoints
  Lets agents leave a structured record of what changed, why it changed, what was verified, and what remains next.
• Native archive support
  Works with ITHZ memory zones and native archive workflows for inspectable project memory packaging.
• Large-repository intake
  Prunes generated outputs and noisy folders so large projects can build useful context without unnecessary clutter.
• Inspectable memory trail
  Makes project memory visible and reviewable instead of burying important decisions inside one-off conversations.
• Redaction-aware workflow
  Supports safer memory intake by keeping sensitive or inappropriate content out of durable project records.
• Project-owned agent memory
  Stores decisions, gates, risks, claims, workflow notes, and checkpoints in a local project memory zone.
• Deterministic context packs
  Gives AI agents bounded, relevant project context instead of relying on hidden chat history or blind full-repo scans.
• Read-only and write profiles
  Separates safe context loading from explicit memory writes, helping teams control what agents may update.

Avantages ITHZ MCP

• Complements Git
  Git remains the source-code history, while ITHZ MCP adds a separate layer for project memory.
• Improves auditability
  Decisions, gates, risks, claims, and checkpoints become inspectable records.
• Reduces context drift
  Agents can start from durable project memory instead of forgetting prior decisions between sessions.
• Helps large repositories
  Generated files and noisy outputs can be pruned from memory intake to keep context useful.
• Supports claim discipline
  Allowed and blocked claims help agents avoid repeating statements that are not approved.
• Keeps memory project-owned
  Important context stays with the project rather than being hidden inside vendor chat history.
• Speeds up agent onboarding
  New agent sessions can load bounded context packs before reading broad source trees.
• Improves human-agent handoff
  Developers can see what an agent did, why it did it, and what should happen next.
• Supports safer local workflows
  Memory can remain local-first and explicit instead of becoming uncontrolled cloud sync.
• Creates clearer project continuity
  The project keeps a durable trail of decisions, evidence, risks, and next steps across AI-assisted work.

Qui devrait utiliser ITHZ MCP ?

• Research teams
  Teams working with evolving evidence, hypotheses, publication boundaries, and claim discipline.
• AI workflow leads
  People designing how agents should read context, write memory, checkpoint work, and hand off safely.
• Engineering teams
  Teams that need shared decisions, risks, gates, and workflow context to survive across agent sessions.
• Platform engineers
  Engineers responsible for repeatable local-first developer workflows and safer AI tooling adoption.
• Technical founders
  Solo builders or small-team founders who want durable project memory without introducing a cloud knowledge base.
• Project maintainers
  Maintainers who want future AI runs to start from the actual project state instead of reconstructing everything.
• Technical reviewers
  Reviewers who need to inspect what was decided, what was verified, and which claims are allowed or blocked.
• Large-repo developers
  Developers working in repositories where repeated full scans are noisy, slow, or too expensive.
• AI-assisted developers
  Developers who use Codex, Claude Code, Cursor, or similar tools and need agents to remember project context safely.
• Organizations evaluating agents
  Companies testing agentic development workflows while needing auditability and control.

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