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TARGET Checklist MCP server

by blackswancausallabs.com·com.blackswancausallabs/target-mcp·v0.2.0

Score how completely a target-trial-emulation study reports what the TARGET guideline requires.

B82/100grade B
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TARGET Checklist MCP tools (11, 2 write)

write = sends, deletes, buys or posts

Read from the package source without running it. The installed server may list more.

  • aggregate_corpus

    Roll up many assessments into per-item completeness rates plus coverage denominators (supplement-retrieval status, full-text availability, evidence- resolution rate). Pass an array of assessment objects as `assessments`, or use_session=true to aggregate every assessment produced this session. The completeness rates are only as valid as the sample and are not yet gold-standard calibrated.

  • assess_manuscript

    Assess a manuscript against all applicable TARGET leaves in one batched pass. `document` is a path, raw text, or a text_sha256 returned by parse_manuscript.

  • build_coding_sheet

    Emit blank per-paper coding sheets for human gold-standard coding: one row per applicable leaf with its intent and verdict boundaries and empty verdict/evidence/note fields. blind=true (default) withholds the instrument's verdict so coders are not anchored — use blind coding for the primary reference standard. Pass `assessments` or use_session=true.

  • get_checklist

    Return the encoded TARGET checklist spec: 39 scoreable leaf subitems (grouped into the 21 published items) with intent, assessor notes, signal terms, pairing links, and applicability rules. Pass a version id to pin; omit for the current default.

  • parse_manuscript

    PRIMARY entry point: parse a manuscript into a SectionMap with character-offset, source-tagged section spans.

  • parse_pmcid

    CONVENIENCE / BATCH entry point: retrieve an open-access article from Europe PMC by PMCID and parse it. Reach for this in the corpus/batch case (no file in hand) or to auto-fetch an open-access paper's supplement; for a single manuscript you were given, use parse_manuscript instead. Fetches JATS main text plus (if available) PMC-hosted supplementary files, merged into one source-tagged SectionMap.

  • render_checklist

    Render a finalized assessment as the completed TARGET checklist form: the 21 published items (39 leaf rows, in checklist order) with the published item wording and a Location-reported column derived from the resolved evidence spans. The default enriched view adds, per leaf, the instrument's verdict and the verbatim evidence quote that justifies the location; view='official' strips to the published

  • render_checklist_docxwrite action

    Write the completed TARGET checklist to a Word (.docx) file and return it. Same content as render_checklist (published item wording + a Location-reported column from the resolved evidence spans; enriched view adds verdict and evidence), serialized as a submission-ready landscape table, one per manuscript section. Pass a full `assessment` object or the `manuscript_id` of one assessed this session.

  • render_checklist_html

    Render the completed TARGET checklist as a self-contained HTML page — same content as render_checklist, styled to the Black Swan Causal Labs identity (canvas ground, one aligned table, verdict pills), viewable in any browser on any host. Returns the HTML string in `html` (show it inline or publish it as an artifact) and the same bytes as base64 in `content_base64` (decode and save as an .html file

  • submit_scaffold_verdictswrite action

    Scaffold-mode completion: validate verdicts produced by the calling agent (leaf coverage, verdict vocabulary, mandatory verbatim evidence quotes resolved to spans) and return the stamped assessment TOGETHER WITH the finished, rendered deliverable. `items` is the verdict array from the forced tool call — one object per leaf with `id`, `verdict`, `confidence`, `rationale`, and (for reported/partial)

  • validate_against_gold

    Compute per-leaf agreement between instrument assessments and human gold-standard codings: raw agreement, Cohen's kappa, Gwet's AC1, and binary (reported-vs-rest) sensitivity/specificity with the human coding as the reference standard, plus a span-keyed disagreement list for adjudication. Agreement is reported PER LEAF; the pooled figure is orientation only. Both arguments are arrays of assessment

Public scan report

scanner v0.1.10 · 2026-11-01 · same rubric, same numbers if you re-run it

no findings
  • Code scan13 source files scanned25/25
  • –Live reliabilityno gateway calls yet and no remote to proben/a
  • –Tool poisoningtools not inspected (local package is not executed); not countedn/a
  • Auth qualitylocal package, no credentials required12/15
  • Maintenancelast push 71 days ago12/15
  • Maintainer identitynamespace and repository owner differ4/10
Overall 82/100. Components that don't apply are left out of the denominator. Any critical finding is an F.RubricAppeal a findingJSON

How TARGET Checklist MCP compares

Against every graded MCP server, from our own scans and uptime checks. Updated after each scan run.

Safety score
82/100
higher than 63% of 48,255 servers
Tools
11
the typical server has 7
Tools that change things
18%
typical: 0%. Fewer write tools means less an agent can break
Downloads a week
30
more than 18% of 12,191 servers with a package

What its tools let an agent do: 9 read · 2 change

Install TARGET Checklist MCP in Claude Code, Cursor or VS Code

claude mcp add target-mcp -- uvx target-mcp
Add to Cursor

What the publisher says

From the TARGET Checklist MCP repository's README, as published. We do not edit it. Read it on GitHub

TARGET Checklist MCP Server

<!-- mcp-name: com.blackswancausallabs/target-mcp -->

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:

subitems, encoded as versioned YAML (targetmcp/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).

  • Spec layer — the 21 published items decomposed into **39 scoreable leaf

source-tagged section spans (main vs supplement:), 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).

  • Ingestion layer — PDF/text/docx → SectionMap with character-offset,

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).

  • Retrieval layer — the corpus/batch and convenience path: fetch

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).

  • Assessment layer — batched, single-pass scoring of all applicable leaves.

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). renderchecklistdocx writes the same as a submission-ready Word file (render.py, render_docx.py).

  • Rendering layer — render_checklist projects a finalized assessment onto

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.

  • Corpus & validation layers — aggregate_corpus rolls many assessments

The primary manuscript flow is parsemanuscript (parse the file you were given, with supplements= when available) → assessmanuscript → submitscaffoldverdicts → renderchecklist (or renderchecklistdocx). Supporting tools: parsepmcid (corpus/batch or OA-supplement fetch), getchecklist (introspect the spec), aggregatecorpus, buildcodingsheet, and validateagainstgold.

  • Composition layer — FastMCP server (server.py) exposing eleven tools.

Not yet built (see design doc): assessitem, checkemulationcoherence, exportidentifiability_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

Shortened. The full README is on GitHub.

Nothing above is checked by us. What we check is on the safety report.

Grade history

  • 2026-11-01downgradeA → Bscore 82

TARGET Checklist MCP: common questions

Is TARGET Checklist MCP server safe?
Mostly: it is graded B (82/100). Read the TARGET Checklist MCP safety report
How do I install TARGET Checklist MCP?
It runs on your machine. Copy the Claude Code, Cursor, VS Code or Claude Desktop config from the install section.
Does TARGET Checklist MCP need an API key?
Not as far as the registry entry and our scan can tell: no credentials are declared or required.
Is TARGET Checklist MCP maintained?
The last commit was 73 days ago (2026-08-23). The latest release is v0.2.0.
How does TARGET Checklist MCP compare with other MCP servers?
safety score 82/100 (higher than 63% of 48,255 servers); tools 11 (the typical server has 7); tools that change things 18% (typical: 0%. Fewer write tools means less an agent can break). Full comparison.

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