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MCP server providing a durable, fault-tolerant write surface for coding agents

Project description

resilient-write

PyPI version Python 3.12+ License: MIT Tests arXiv

An MCP server that gives coding agents a durable, fault-tolerant write surface so they can keep making forward progress when a tool call is blocked by a content filter, a size cap, or an opaque transport error.

Why this exists

Coding agents today write through a single tool like Write or edit_file. When that tool rejects a payload (for any reason), the failure mode is usually:

  1. The agent loses the content it was trying to write (it only exists in model memory).
  2. There is no structured error, so the agent can't reason about what to change.
  3. Retries thrash because the agent re-sends the exact same rejected content.
  4. Any downstream step that depended on the file is now operating on half-broken state.
  5. No handoff mechanism exists, so a fresh agent or sibling agent has to re-derive the context.

This project addresses those five failure modes as five orthogonal layers.

Layered architecture (sixteen MCP tools, one convention file)

Layer Tool name What it does Catches
L0 rw.risk_score Static pre-flight classifier over draft content (regexes + length + binary heuristics). Returns a numeric risk score plus a list of detected patterns. Secret-shaped strings, long lines, encoding issues before they hit the sink.
L1 rw.safe_write Transactional write: temp file + hash verify + atomic rename + journal append. Half-written files, lost prior content, no audit trail.
L2 rw.chunk_write Write one numbered chunk to a session directory via safe_write; idempotent retries. Single-chunk failures without losing prior chunks.
L2 rw.chunk_append Auto-incrementing chunk write — detects the highest index and writes index+1. Misnumbered chunks; lets the agent stream sections without tracking indices.
L2 rw.chunk_compose Concatenate a session's chunks in order and write the result through safe_write. Any single-call write that is too large or too risky; allows incremental progress with rollback.
L2 rw.chunk_reset Destructively wipe an in-progress chunk session. Stale session state after an abandoned compose.
L2 rw.chunk_status Report which chunk indices are present and what total was declared. Missing or duplicate chunks before compose.
L2 rw.chunk_preview Dry-run compose — returns concatenated content without writing to disk. Pre-write validation; pair with rw.validate to catch errors before commit.
L3 rw.typed_error (schema) Specification for structured tool errors {reason_hint, detected_patterns, suggested_action, retry_budget}. Wraps safe_write and chunk_compose. Opaque tool-harness errors that agents cannot reason about.
L4 rw.scratch_put Store raw material out-of-band, content-addressed by SHA-256. Cases where the content legitimately does not belong in the workspace.
L4 rw.scratch_ref Look up a scratchpad entry by hash or label without returning content. Verify what is stored before deciding to surface it.
L4 rw.scratch_get Retrieve raw content by hash (disableable via $RW_SCRATCH_DISABLE_GET). Controlled retrieval; supports write-only mode.
L5 rw.handoff_write Write a HANDOFF.md continuity envelope (YAML front-matter + body). Reports drift warnings. Cross-agent and cross-session continuity when a task is interrupted.
L5 rw.handoff_read Parse a HANDOFF.md envelope and return structured front-matter plus body. Picking up where another agent left off.
rw.journal_tail Inspection helper — last N rows of the L1 write journal, with optional filters. Debugging write history and audit.
rw.validate Format-aware syntax validator (LaTeX, JSON, Python, YAML). Structural errors (unbalanced braces, bad parses) before they reach disk.
rw.analytics Journal analytics — write counts, timing, hot paths, session summaries. Understanding agent write patterns and diagnosing performance issues.

Layers can be adopted independently. The minimum useful install is L1 + L5.

Install

pip install resilient-write

Or run directly:

uvx resilient-write

MCP client configuration

Add to your Claude Code, Cursor, or Codex MCP config:

{
  "mcpServers": {
    "resilient-write": {
      "command": "uvx",
      "args": ["resilient-write"],
      "env": {
        "RW_WORKSPACE": "/path/to/your/project"
      }
    }
  }
}

See docs/INSTALL.md for full setup instructions for all clients.

Usage

Basic: write a file safely

The agent calls rw.safe_write instead of raw Write. The file is written atomically (temp → fsync → verify → rename) and logged to the audit journal.

rw.safe_write(path="src/main.py", content="print('hello')", mode="create")
→ {"ok": true, "path": "src/main.py", "sha256": "a1b2c3...", "bytes": 14}

Pre-flight risk check

Before writing content that might contain tokens or credentials, run rw.risk_score:

rw.risk_score(content="Bearer sk-ant-oat01-AAAA...")
→ {"ok": true, "score": 0.82, "verdict": "high",
   "detected_patterns": [{"kind": "api_key", ...}],
   "suggested_actions": [{"action": "redact", "targets": ["api_key"]}]}

If the verdict is high, redact the flagged patterns before writing.

Large files: chunked writes

For files over ~5KB, build them section by section:

rw.chunk_append(session="my-report", content="# Introduction\n...")
rw.chunk_append(session="my-report", content="# Methods\n...")
rw.chunk_append(session="my-report", content="# Results\n...")

# Preview before committing
rw.chunk_preview(session="my-report")

# Validate syntax
rw.validate(content=<preview_content>, format_hint="latex")

# Compose the final file
rw.chunk_compose(session="my-report", output_path="report.tex", cleanup=true)

If a chunk fails mid-session, prior chunks are already on disk — just retry the failing one.

Handling errors

Every failure returns a structured envelope the agent can branch on:

{
  "ok": false,
  "error": "blocked",
  "reason_hint": "content_filter",
  "detected_patterns": ["api_key"],
  "suggested_action": "redact",
  "retry_budget": 2
}

The agent reads suggested_action and acts accordingly — no guesswork, no blind retries.

Sensitive content: scratchpad

Store raw credentials or PII out-of-band instead of writing them to the workspace:

rw.scratch_put(content="sk-ant-oat01-real-key-here", label="captured-token")
→ {"ok": true, "sha256": "5c9a3b...", "dedup": false}

The .resilient_write/ directory is gitignored. Reference the hash in your report instead of the raw value.

Session handoff

Before ending a session (or when blocked), save state for the next agent:

rw.handoff_write(envelope={
  "task_id": "my-report",
  "status": "partial",
  "agent": "claude-opus-4-6",
  "summary": "Sections 1-3 complete, section 4 blocked on content filter",
  "next_steps": ["Redact api_key patterns in section 4", "Retry chunk 4"],
  "last_good_state": [{"path": "report.tex", "sha256": "4b0c12..."}]
})

A fresh agent calls rw.handoff_read to pick up where you left off.

Making agents prefer rw.* tools automatically

Drop a CLAUDE.md (for Claude Code) or .cursorrules (for Cursor) in your project root. The server also sends MCP-level instructions at initialization, telling compatible clients to prefer rw.* tools over raw writes. See the included CLAUDE.md for the recommended content.

Status

  • Architecture document
  • Per-layer specs
  • JSON schemas (spec/errors.schema.json)
  • Reference Python implementation (all six layers, 16 tools)
  • Test suite (186 tests, all green)
  • Published MCP config snippets (docs/INSTALL.md)
  • Published to PyPI

Citation

If you use resilient-write in your research, please cite:

@article{agyemang2026resilientwrite,
  title   = {Resilient Write: A Six-Layer Durable Write Surface for LLM Coding Agents},
  author  = {Owusu Agyemang, Justice and Kponyo, Jerry John and Amponsah, Elliot and Addo Boakye, Godfred Manu and Obour Agyekum, Kwame Opuni-Boachie},
  journal = {arXiv preprint arXiv:2604.10842},
  year    = {2026},
  url     = {https://arxiv.org/abs/2604.10842}
}

Origin

This project was spun out of a concrete failure observed while producing an LLM-CLI telemetry analysis report. A Write tool call was silently rejected when the draft contained redacted-looking credential strings; the agent recovered only after five retries and a hand-written chunked-append workaround. The five layers here correspond to the five things that would have caught that failure before it wasted cycles. See docs/SCENARIOS.md for the full postmortem.

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