TARGET Checklist MCP Server

An MCP server that operationalizes the TARGET reporting guideline (TrAnsparent ReportinG of observational studies Emulating a Target trial; Cashin et al., JAMA/BMJ, September 2025) as a deterministic, provenanced scoring engine for target trial emulation (TTE) studies.

This is the executable TARGET: not a manuscript auditor that renders an opinion, but a measurement instrument that produces a structured, item-by-item, evidence-linked matrix with fixed model provenance, so a corpus-scale result is reproducible.

See ../target-mcp-server-design.md for the full design rationale.

Status — vertical slice

Built and proven end-to-end:

• Spec layer — the 21 published items decomposed into 39 scoreable leaf subitems, encoded as versioned YAML (target_mcp/specs/target-0.1.0.yaml) with own-words intent, verdict boundaries, signal terms, the 6x↔7x specification/emulation pairing, and applicability rules. Structurally validated on load (spec.py).

• Ingestion layer — PDF/text/docx → SectionMap with character-offset, source-tagged section spans (main vs supplement:<file>), protocol-table and flow-diagram detection, extractor version + text hash stamps, and whitespace-insensitive quote→span resolution. Supports multi-document bundles (main text + supplements) via build_bundle (ingest.py).

• Retrieval layer — the corpus/batch and convenience path: fetch open-access articles by PMCID from Europe PMC (JATS main text plus PMC-hosted supplementary files, merged into one bundle). Use this when there is no file in hand or to auto-fetch an OA paper's supplement; for a manuscript you were given, ingest the file directly (retrieve.py).

• Assessment layer — batched, single-pass scoring of all applicable leaves. Scaffold mode (default) is the path for reviewing a publication: the agent already in the loop is the scorer, so the server returns the exact prompt + schema for it to run, then validates the verdicts it submits. Judge mode is for the headless/batch case (no LLM in the loop) — the server makes its own pinned model call so a corpus run is reproducible and caller-independent. Both share one validation path that enforces leaf coverage, verdict vocabulary, and mandatory verbatim evidence resolved to spans with a source_document tag (assess.py).

• Rendering layer — render_checklist projects a finalized assessment onto the published TARGET checklist form (all 39 rows, verbatim item wording under CC BY-ND, a Location-reported column from the resolved evidence spans; the enriched view adds verdict + evidence). render_checklist_docx writes the same as a submission-ready Word file (render.py, render_docx.py).

• Corpus & validation layers — aggregate_corpus rolls many assessments into per-item completeness rates with coverage denominators (corpus.py); validate.py provides blind human coding-sheet generation and per-leaf agreement (raw, Cohen's κ, Gwet's AC1, sensitivity/specificity) against a gold standard.

• Composition layer — FastMCP server (server.py) exposing eleven tools. The primary manuscript flow is parse_manuscript (parse the file you were given, with supplements= when available) → assess_manuscript → submit_scaffold_verdicts → render_checklist (or render_checklist_docx). Supporting tools: parse_pmcid (corpus/batch or OA-supplement fetch), get_checklist (introspect the spec), aggregate_corpus, build_coding_sheet, and validate_against_gold.

Not yet built (see design doc): assess_item, check_emulation_coherence, export_identifiability_spec, publisher-site supplement retrieval (beyond the PMC-OA tier), better table extraction, and the separate materiality/design-risk layer.

Provenance stamped on every assessment

spec_version, resolved model id, temperature, prompt_hash, prompt_template_version, extractor_version, text_sha256, assessed_at, full_text_available, supplement_status, and a per-source documents list; each evidence item carries its resolved span, section, and source_document. A span is only meaningful alongside the extractor version and text hash, so they travel together.

Related MCP server: mcp-ai-detection

Reporting completeness, not a pass/fail verdict

The instrument scores how completely a manuscript reports what the checklist requires — not study quality, and it issues no pass/fail judgment. TARGET presents all 21 items as essential minimum items with no tiering, and this tool follows suit: the output is the 39 per-leaf verdicts with evidence and a completeness tally. (An earlier "critical floor" — a BSCL pass/fail overlay over six leaves — was removed in 2026-07-19 as off-message and confusing; see docs/DECISIONS.md.) Do not present a verdict matrix as TARGET compliance.

Install

The server and the optional orchestration skill ship together in this repo, but they activate through two separate mechanisms — installing one does not enable the other. Getting the files (clone/download) gives you both; then do the two activation steps below.

1. Install the server

git clone https://github.com/Black-Swan-Causal-Labs/target-mcp.git
cd target-mcp
python3 -m venv .venv
.venv/bin/pip install -e .

(Once published to PyPI: pip install target-mcp.)

Run it standalone (stdio transport) to smoke-test:

.venv/bin/target-mcp

2. Register the server with your MCP client

Add it to the client's MCP config with an absolute path. For Claude Desktop that file is ~/Library/Application Support/Claude/claude_desktop_config.json (macOS); for a Claude Code project use .mcp.json:

{
  "mcpServers": {
    "target-checklist": {
      "command": "/ABSOLUTE/PATH/TO/target-mcp/.venv/bin/python",
      "args": ["-m", "target_mcp.server"]
    }
  }
}

Restart / reconnect the client. Confirm it exposes 11 tools (a stale process may show fewer — respawn it). The server is now fully usable on its own: any MCP client can run parse → assess → submit → render in a single scaffold pass.

3. (Optional, Claude Code only) Activate the fan-out skill

.claude/skills/target-checklist-fanout/ encodes the parallel-subagent orchestration that scores the 39 leaves concurrently (~2 min vs ~20 min). It is an accelerator, not a dependency — it only works in clients that can spawn subagents (the Claude Code CLI, or the Claude Desktop Code/Cowork tabs; the plain Chat tab cannot, and non-Claude clients like Codex ignore it). Without it, everything still works via the single-pass fallback.

A Claude Code session discovers the skill when its working directory is this repo. To make it available in every session regardless of directory, copy it to the user scope:

mkdir -p ~/.claude/skills
cp -R .claude/skills/target-checklist-fanout ~/.claude/skills/

For the skill to actually run, all three must be present: the server (step 2), the skill (this step), and a subagent-capable client.

Judge mode (headless/batch, optional)

Judge mode needs ANTHROPIC_API_KEY in the environment. The pinned model is claude-sonnet-5 by default; override with TARGET_JUDGE_MODEL.

Batch / corpus runs

Scoring hundreds of papers is a headless job, not an interactive MCP call (a multi-hour tool call would blow the client's request timeout). Use the target-mcp-corpus CLI: it fetches + judges a list of PMCIDs concurrently, isolates per-paper failures, retries transient errors, and rolls up the aggregate.

# ids.txt: one PMCID per line (#-comments and blanks ignored)
ANTHROPIC_API_KEY=sk-... target-mcp-corpus ids.txt -o out/ -j 12

• -j/--workers bounds concurrency (default 8; raise toward your API rate limit). Judge is ~a few minutes/paper, so wall-clock ≈ papers / workers × per-paper — e.g. 300 papers at 12 concurrent ≈ 1–1.5h, versus ~10–20h serial.

• -o/--out-dir writes one JSON per assessment plus aggregate.json and summary.json (with per-paper stamps, verdict tallies, and any failures). Without -o, the aggregate prints to stdout.

• --model overrides the pinned judge model; --no-supplements skips supplement retrieval.

Judge mode records the resolved model id per paper, so a corpus aggregate is truthfully provenanced and caller-independent. Re-roll-up saved assessments any time with the aggregate_corpus MCP tool.

Tests

.venv/bin/python -m pytest tests/ -q

Covers spec validation, section mapping and quote resolution, the finalize + evidence-resolution path, evidence-required and coverage-mismatch guards, applicability rules, abstract-only full-text gating, corpus aggregation, the validation harness, checklist rendering (Markdown + .docx), and prompt-hash stability.

Licensing / commons hygiene

The encoded checklist is a re-encoding of item intent in own words plus assessor notes; it does not reproduce the TARGET checklist wording verbatim. The original checklist is © the TARGET group under CC BY-ND 4.0. When the Explanation & Elaboration document is released, leaves flagged e_e_pending: true should be re-checked and the spec bumped to a clean minor release. Do not copy E&E text verbatim.