TARGET Checklist MCP server
Score how completely a target-trial-emulation study reports what the TARGET guideline requires.
0 stars30 downloads/wk
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TARGET Checklist MCP tools (11, 2 write)
write = sends, deletes, buys or postsRead from the package source without running it. The installed server may list more.
aggregate_corpusRoll 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_manuscriptAssess 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_sheetEmit 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_checklistReturn 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_manuscriptPRIMARY entry point: parse a manuscript into a SectionMap with character-offset, source-tagged section spans.
parse_pmcidCONVENIENCE / 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_checklistRender 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 actionWrite 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_htmlRender 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 actionScaffold-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_goldCompute 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
- 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
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
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.