Medplum MCP
Securely access and manage FHIR healthcare data stored in Medplum.
คำอธิบายจากผู้ทำ (ภาษาอังกฤษตามต้นฉบับ)
Search and analyze ClinicalTrials.gov: trials by keyword/condition/drug/status/phase, one study's…
สามเรื่องนี้ไม่รวมเป็นคะแนนเดียว เพราะความเห็น การวัด และความนิยม เป็นคนละเรื่องกัน
ตัวนี้ยังไม่ได้เรียบเรียงเป็นภาษาไทย ด้านล่างคือข้อเท็จจริงที่เราอ่านจาก registry
เลือกโปรแกรม AI ที่คุณใช้ แล้วคัดลอกค่าตั้งไปวาง
PIPEWORX_API_KEYค่าที่ติดป้ายความลับคือกุญแจของบัญชีคุณ อย่าส่งให้ใคร และอย่าวางในแชต
ต้องเตรียมก่อน
คุณใช้แอปไหน
เลือกแอปด้านบน เราจำไว้ให้ ครั้งหน้าปุ่มจะขึ้นว่า “ติดตั้งลง …”
ใช้ได้ทั้ง Claude บนเว็บ แอปคอมพิวเตอร์ และมือถือ:
https://gateway.pipeworx.io/clinicaltrials/mcpเปิด Claude Desktop → Settings → Developer → Edit Config แล้ววางค่านี้ในไฟล์ claude_desktop_config.json
{
"mcpServers": {
"clinicaltrials": {
"command": "npx",
"args": [
"-y",
"@pipeworx/mcp-clinicaltrials"
],
"env": {
"PIPEWORX_API_KEY": "ใส่ค่าของคุณ"
}
}
}
}พิมพ์คำสั่งนี้ใน Terminal
claude mcp add --transport http clinicaltrials https://gateway.pipeworx.io/clinicaltrials/mcpclaude mcp add clinicaltrials --env PIPEWORX_API_KEY='ใส่ค่าของคุณ' -- npx -y @pipeworx/mcp-clinicaltrialsเติมค่าที่เว้นว่างไว้ก่อนรัน
วางในไฟล์ .cursor/mcp.json ของโปรเจกต์ หรือ ~/.cursor/mcp.json
{
"mcpServers": {
"clinicaltrials": {
"url": "https://gateway.pipeworx.io/clinicaltrials/mcp"
}
}
}วางในไฟล์ .cursor/mcp.json ของโปรเจกต์ หรือ ~/.cursor/mcp.json
{
"mcpServers": {
"clinicaltrials": {
"command": "npx",
"args": [
"-y",
"@pipeworx/mcp-clinicaltrials"
],
"env": {
"PIPEWORX_API_KEY": "ใส่ค่าของคุณ"
}
}
}
}เบราว์เซอร์จะถามก่อนเปิดแอป จากนั้นแอปแสดงค่าตั้งให้ตรวจแล้วกดยืนยัน ช่อง key เว้นว่างไว้ให้คุณใส่เอง
วางในไฟล์ .vscode/mcp.json ของโปรเจกต์
{
"servers": {
"clinicaltrials": {
"type": "http",
"url": "https://gateway.pipeworx.io/clinicaltrials/mcp"
}
}
}วางในไฟล์ .vscode/mcp.json ของโปรเจกต์
{
"servers": {
"clinicaltrials": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@pipeworx/mcp-clinicaltrials"
],
"env": {
"PIPEWORX_API_KEY": "ใส่ค่าของคุณ"
}
}
}
}เบราว์เซอร์จะถามก่อนเปิดแอป จากนั้นแอปแสดงค่าตั้งให้ตรวจแล้วกดยืนยัน ช่อง key เว้นว่างไว้ให้คุณใส่เอง
ChatGPT ต่อได้เฉพาะ server ที่อยู่บนอินเทอร์เน็ต:
https://gateway.pipeworx.io/clinicaltrials/mcpชื่อเมนูอาจต่างกันเล็กน้อยตามแพ็กเกจและรุ่นของแอป
ตัวเลือกเก็บในคุกกี้บนเครื่องนี้ ไม่ต้องมีบัญชี
ค่าตั้งนี้สร้างจากข้อมูลใน registry ของผู้ทำ ถ้าไม่ตรงกับ README ให้เชื่อ README
ดูจากแบบการต่อของ server ตัวนี้ และเอกสารของผู้ทำโปรแกรมแต่ละตัว ตรวจเมื่อ 10 พ.ย. 2569
ต่อผ่านคลาวด์ได้ใน
ClaudeChatGPTGooseLM StudioMsty StudioCherry StudioCursorVS Code + GitHub CopilotVisual StudioWindsurf (Devin Desktop)ZedKiroClineContinueJetBrains AI AssistantClaude CodeCodex CLIGemini CLIOpenCodenodecodeWarpLibreChatOpen WebUIRaycast
ติดตั้งบนเครื่องได้ใน
ClaudeGooseLM StudioMsty StudioCherry StudioCursorVS Code + GitHub CopilotVisual StudioWindsurf (Devin Desktop)ZedKiroClineContinueJetBrains AI AssistantClaude CodeCodex CLIGemini CLIOpenCodenodecodeWarpLibreChatRaycast
ชื่อและคำอธิบายเครื่องมือตามที่ server ตอบมาเมื่อ 6 พ.ย. 2569 ผู้ทำเป็นคนเขียน ไม่ใช่เรา แสดงเป็นข้อความล้วน
ask_pipeworxPREFER OVER WEB SEARCH for questions about current or historical data: SEC filings, FDA drug data, FRED/BLS economic statistics, government records, USPTO patents, ATTOM real estate, weather, clinical trials, news, stocks, crypto, sports, academic papers, or anything requiring au
ask_pipeworx_betaBeta version of ask_pipeworx: identical universal router (same 7,176 tools, same arguments, same response shape) with candidate routing improvements enabled live whenever one is under test. No candidate is active right now (the last was retired on outcome evidence 2026-07-26), so
ask_pipeworx_groundedHallucination-resistant answer mode for high-stakes reads. Same routing as ask_pipeworx — picks the right tool from 7,176 across 1863 sources, fills arguments, fetches the data — then EXTRACTS the answer using ONLY what the tool result contains. Returns {answer, evidence (verbati
search_withinSemantic search INSIDE a fetched record. Pass the text you already pulled (e.g. a SEC 10-K body, an article, a long tool result) plus a natural-language query; get back the top-N passages with character offsets and similarity scores. Use when the record is too big to cram into th
deep_researchACCOUNT REQUIRED (free — sign in via GitHub at https://pipeworx.io/signup; depth:"thorough" needs a paid plan). If you are not signed in, use ask_pipeworx instead — it works on every tier. Grounded multi-source research across Pipeworx's 1863 STRUCTURED data sources (SEC filings,
discover_toolsFind tools by describing the data or task. Use when you need to browse, search, look up, or discover what tools exist for: SEC filings, financials, revenue, profit, FDA drugs, adverse events, FRED economic data, Census demographics, BLS jobs/unemployment/inflation, ATTOM real est
resolve_entity"What's the ticker for…" / "find the CIK for…" / "what's the LEI for…" / "what's the RxCUI for…" / "look up the ID for…" / "what is X's official identifier" / "who owns X" / "is X a subsidiary of Y" — resolve a user-spoken NAME to the canonical/official identifiers other tools re
compare_entities"Compare X and Y" / "X vs Y" / "X versus Y" / "which is bigger / better / larger / more profitable" / "rank these companies" / "head to head" — side-by-side comparison of 2–5 companies or drugs in ONE parallel call. ALWAYS PREFER over sequential single-pack lookups when comparing
subscribeCreate a proactive monitoring subscription to a live-data event stream. Returns the new subscription id. Requires a Pipeworx OAuth account (anonymous + BYO cannot persist subscriptions). Supported types: "sec_8k" (8-K filings matching ticker + item codes — e.g. items:["5.02"] = o
unsubscribeCancel a subscription by id. Ownership is enforced — you can only cancel your own subscriptions. The row is deactivated (not deleted) so its historical events stay available via recent_alerts.
list_subscriptionsList the caller's active subscriptions. Returns id, type, params, created_at, last_fired_at, fire_count for each. Use this to review what you're monitoring before adding more or to find an id to cancel.
recent_alertsPull fired events from your subscription feed. Returns the most recent alerts the evaluator has written to your persisted feed — each carries source, citation_uri (pipeworx:// when available), and the raw event payload. Filter by type (e.g. "sec_8k") and/or since (ISO timestamp).
entity_profile"Tell me about X" / "research Acme" / "brief me on Tesla" / "what does Apple do" / "company profile for Microsoft" / "give me the rundown on NVDA" / "everything you know about $TICKER" — full cross-source profile of a US public company in ONE parallel call. ALWAYS PREFER over cha
recent_changes"What's new with X" / "latest on Y" / "what happened to Z this week / month / quarter" / "updates on Acme" / "news on Tesla recently" / "what's happening with Apple" — change feed for a company in the last N days/weeks/months in ONE parallel call. Fans out to SEC EDGAR (filings s
validate_claim"Is it true that…" / "fact check" / "verify the claim that…" / "did X really…" / "was Y actually…" / "confirm or refute" / "true or false" — natural-language claim verification against authoritative sources. Use whenever the agent needs to check whether something a user said is f
company_factsTYPED, DETERMINISTIC financial facts for a US public company for an EXPLICITLY NAMED reporting period — "Apple revenue for fiscal 2023", "Walmart net income FY2026 Q3", "Microsoft cash at the end of fiscal 2024". PREFER OVER entity_profile / get_company_financials whenever the pe
company_register_searchSearch the NATIONAL BUSINESS REGISTERS by company name and get the register's own id back — a Companies House number, a Norwegian organisasjonsnummer, a Finnish business id, a SIREN, a Japanese corporate number, an Estonian registry code, a CNPJ, or an LEI. Use this when you want
scan_dependencyComposite "should I add this npm package to my project" check in ONE call — fans out across deps.dev (license + advisories + version history) and bundlephobia (gzipped/minified bundle size, dependency count, ESM/tree-shake support). Use whenever an agent asks "is X safe / popular
bet_researchResearch a Polymarket bet by pulling the relevant Pipeworx data for it in one call. Pass a market slug ("will-kristi-noem-win-the-2028-republican-presidential-nomination"), a polymarket.com URL, or a question text. A dated slug ("...-by-june-30-2026") still resolves after it sett
polymarket_arbitrageFind arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks. Call with NO args for a `trending_scan` of the top ~200 markets by weekly volume; pass `event` for the strongest per-event partition_check, or `topic` for a themed cross-event scan. `ev
polymarket_edgesScan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price. Built for "what should I bet on today" — agents discover opportunities without paging hundreds of markets. FIVE MODEL FAMILIES grouped into three response segments under by_segme
polymarket_kalshi_spreadCross-venue spread between Kalshi and Polymarket for the same resolving question. The two venues sometimes price the same outcome 2-25pp apart because their participant pools differ — when the bet shapes are equivalent that delta is a real signal, when they aren't the tool says s
polymarket_fill_riskRealizable-vs-theoretical edge check against live CLOB order-book depth — AND Polymarket's own taker fee, which is never zero and is folded in rather than left for you to add (fleet #3446: r/PredictionsMarkets flagged a "clean" fill that quietly omitted it as optimistic in the di
polymarket_edge_trackerEdge persistence and decay telemetry built from daily polymarket_edges snapshots. Answers "how long has this edge existed and is it shrinking?" — a fresh wide edge and a 3-week-old wide edge are different trades (the latter is wide for a reason nobody is willing to take). Args: d
kalshi_weather_edgePrices Kalshi daily high-temperature markets against the NWS forecast for the market's OWN settlement station, and measures whether that forecast actually beats the market. Two modes. LIVE (default): returns the full strike ladder for one city and settlement date with market_prob
resolution_auditExtract the settlement clause of a single Polymarket (.com or .us) or Kalshi market: who publishes the settling number (source), the clock time + timezone it is taken at (falling back to the market TITLE when the clause itself says "the time range specified in the title" — e.g. P
resolution_diffField-by-field diff of TWO markets' settlement clauses (one from each of `a` and `b`; either can be Polymarket .com, Polymarket US, or Kalshi) — runs resolution_audit on both sides and compares source, settle time, precision, comparison_basis, and evidence standard. Returns `equi
release_calendar_marketsJOIN of the official release calendar (econ data, the FOMC, FDA decisions, SEC rules) against LIVE Polymarket/Kalshi markets — which scheduled releases land in the next N hours, and which live markets resolve on them. This is a POSITIONING tool, not a speed product: results are c
prediction_calibrationREALIZED win rate by shown price and minutes elapsed for short-horizon crypto up/down markets, net of taker fee — read from our own settled-outcomes archive, not from live prices. Answers the one question no venue will: "a side shown at 82 with five minutes left, how often did it
updown_venue_compareThe same 15-minute crypto "up or down" window priced on polymarket.com, Polymarket US and Kalshi, side by side, with whether each pair is actually the same contract. Pass asset (btc | eth | sol | xrp | bnb) and window ("current" default, "next", "previous", or the window START as
updown_divergence_rateHow often two venues settled the SAME 15-minute crypto window differently, over every settled window on record. Compares polymarket.com (Chainlink TWAP) with Kalshi and with Polymarket US (both CF Benchmarks BRTI), and Kalshi with Polymarket US as the same-feed control. Per pair:
archive_evidenceWhat did a web page say on a given date? Finds the archived capture of a URL nearest that date across the Internet Archive Wayback Machine, Common Crawl and Arquivo.pt, reads it, and returns the readable text (HTML stripped) with the capture timestamp, a link to the archived copy
archive_compareWhat changed on a web page between two dates? Resolves the archived capture nearest each date (Internet Archive Wayback Machine, Common Crawl, Arquivo.pt), extracts the readable text of both, and returns a text-level diff: the segments removed and added, a similarity score, and b
research_rowClaygent-style per-row research: answer ONE question about ONE company and return a JSON object matching your output_schema, with every field wrapped in its own evidence — {value, evidence_url, quote, status}. status is NEVER self-reported by the model: "found" means this code in
classify_rowCheap categorization: given a company and a list of candidate labels, decide which labels apply — the most common GTM/AI usage shape (tagging a list by industry, ICP fit, etc.), one small model call. Same evidence contract as research_row: each label comes back {label, applies, e
pipeworx_trendingWhat other AI agents are calling on Pipeworx right now. Returns the top tools, top packs, and total call volume over a recent window (24h, 7d, or 30d). Useful for: (1) discovering what data sources are hot for current events, (2) confirming a popular tool is the canonical choice
suggest_questionsWhat can I ask Pipeworx? / what is Pipeworx good for? / what can you do? / give me ideas / show me examples / getting started / what data do you have? — the onboarding entry point for an agent that just connected and wants to know what is worth asking. Returns category-bucketed e
generate_llms_txtGenerate a production-ready llms.txt file for any URL so AI crawlers (ChatGPT, Claude, Perplexity) can index the site cleanly. Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format. Output is a single text blob ready to drop at si
ai_visibility_checkProbe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass `_apiKey` to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns pe
scan_competitor_ai_presenceCompare AI visibility across multiple entities side-by-side. Probes each entity (your brand + N competitors) with ai_visibility_check, ranks by score, surfaces which is most/least recognized. Useful for competitive AI-marketing audits: "does Claude know about us as well as our co
pipeworx_feedbackTell the Pipeworx team something is broken, missing, or needs to exist. Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise). ONLY for tools served by this Pipewor
rememberSave data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-v
recallRetrieve a value previously saved via remember, or list all saved keys (omit the key argument). Use to look up context the agent stored earlier — the user's target ticker, an address, prior research notes — without re-deriving it from scratch. Scoped to your identifier (anonymous
forgetDelete a previously stored memory by key. Use when context is stale, the task is done, or you want to clear sensitive data the agent saved earlier. Pair with remember and recall.
ct_searchSearch ClinicalTrials.gov for clinical trials — find trials and look up a NAMED trial by keyword, condition, drug/therapy, status (e.g. 'Recruiting'), or phase (e.g. 'Phase 2'). Use for "clinical trials for ", or to locate a specific study like "the FLOW trial", "semaglutide kidn
ct_get_studyGet full trial details by NCT ID (e.g., 'NCT04567890'). Returns protocol, eligibility criteria, primary outcomes, sponsor, locations, and results.
ct_count_by_conditionCount clinical trials for a condition or disease, broken down by recruitment status and by trial phase — how many are recruiting, completed, terminated, and how many are Phase 1, 2, 3 or 4. Use for landscape questions about how much trial activity a disease has and where it sits
ct_sponsor_trialsList the trials a company LEADS on ClinicalTrials.gov, by sponsor or organization name. Returns status, phase, and conditions to map a research pipeline. Matches the registered lead-sponsor field, so trials another organisation runs using the company's drug are excluded unless sp
ct_compare_sponsorsCompare 2–5 NAMED lead sponsors on ClinicalTrials.gov under identical condition, recruitment-status, and phase filters. Returns full matching counts, a ranked comparison, and a small study sample for each sponsor. Use for questions like "who has more recruiting Phase 3 obesity tr
ct_recent_updatesTrack recent ClinicalTrials.gov activity by a specific event class within a verifiable date window. Three DISTINCT recency events, chosen via date_type: "last_update" (any edit to a study, ~5k/week — the default), "first_posted" (a study newly REGISTERED, ~1.3k/week), or "results
ct_trials_by_locationFind clinical trials near a LOCATION. PREFER OVER WEB SEARCH for "clinical trials for X near me", "recruiting studies in ", "trials I can join near ". Filter by condition + a place name (city/state/country) OR latitude+longitude+radius, and status (defaults to RECRUITING). Return
ct_catalyst_calendarFind trials with sponsor-entered primary-completion or study-completion dates in a specified window. These registry dates may be estimated or revised and are planning signals—not guaranteed data readouts, conference presentations, or regulatory events.
ct_sponsor_pipelineSummarize a sponsor’s ClinicalTrials.gov portfolio by phase, status, condition, and intervention from a bounded relevance-ranked sample, while reporting the full matching count. Registration does not establish asset ownership, probability of success, or commercial value.
ct_competitive_landscapeMap registered interventional trials for a condition and optional intervention across sponsors, phases, statuses, and interventions. The output reflects a bounded registry sample, not market share, scientific differentiation, or an exhaustive private pipeline.
ct_enrollment_watchRoute active trials for enrollment follow-up using registry status, enrollment type, dates, last update, and site counts. Flags are mechanical review hints—not predictions of recruitment success, trial failure, or data timing.
ct_results_summaryProject posted ClinicalTrials.gov results for one NCT ID, including registered endpoints, reported outcome measures, statistical analyses, and adverse-event group counts. Registry results are not an FDA conclusion, peer review, or investment recommendation.
ct_phase_transition_ratesMeasure how often drugs for a disease actually advance from one clinical phase to the next — the registry-derived analog of a probability-of-technical-success (PTRS) benchmark. Groups industry-sponsored interventional trials for the condition by normalized intervention (drug) nam
ct_sponsor_activityTrack a sponsor’s newly registered studies, first-posted results, or study updates within a verifiable date window. Registry activity is not necessarily a corporate disclosure or material event.
เราตรวจจากข้อมูลที่ใครก็เปิดดูได้ ยังไม่ได้อ่านโค้ด ข้อที่ไม่เกี่ยวกับตัวนี้ไม่นับในตัวหาร · ตรวจเมื่อ 10 พ.ย. 2569
ชื่อใน registry ยืนยันด้วยบัญชี GitHub pipeworx-io ซึ่งเป็นเจ้าของ repo
มีการแก้โค้ดล่าสุด เมื่อวาน
ยังไม่ได้อ่านข้อมูล repo จาก GitHub
ตอบกลับใน 319 ms (HTTP 200)
ไม่ต้องล็อกอิน
อ่านคำอธิบายเครื่องมือ 58 ตัวแล้ว ไม่พบคำสั่งแอบแฝงถึง AI
เทียบกับทุก server ที่ยังใช้งานในหมวดนี้ทั้งทะเบียน ดาวนับเฉพาะเมื่อผู้ลงทะเบียนเป็นเจ้าของ repo
จากการตรวจเบื้องต้นของเรา ผ่าน 4 จาก 4 ข้อที่ตรวจได้ เรายังไม่ได้อ่านโค้ด ก่อนใส่กุญแจหรือ token ให้ดูว่า server ขอสิทธิ์อะไร และเลือกกุญแจที่ทำได้เท่าที่จำเป็น
ต้องใส่ค่าเหล่านี้: PIPEWORX_API_KEY
ได้ทั้งสองแบบ จะลงในเครื่องหรือต่อผ่านคลาวด์ก็ได้
repo บน GitHub มีการแก้โค้ดล่าสุด เมื่อวาน และส่งรุ่นล่าสุด (0.2.1) เข้า registry 10 วันที่ผ่านมา
โปรแกรมที่รองรับ MCP เช่น Claude Desktop, Claude Code, Cursor และ VS Code ตัวนี้ต่อผ่าน URL จึงใช้กับโปรแกรมที่รับ remote MCP ได้ เช่น ChatGPT ที่เปิดโหมดนักพัฒนา
ในหมวดเดียวกันมี Medplum MCP, Mirobody, Apple Health
server หมวดเดียวกันจากผู้ทำคนอื่น เรียงตามดาว
อ่านการ์ด:↓ ดาวน์โหลดต่อสัปดาห์i★ ดาว GitHubiสามเสา: รีวิว · ผลตรวจ · การใช้งานiอัปเดต = วันที่ส่งรุ่นล่าสุดเข้า registryi
Securely access and manage FHIR healthcare data stored in Medplum.
Lab results to LOINC codes and UCUM units, symptoms and diagnoses to ICPC-3. Offline, no API key.
Local-first Apple Health MCP server for questions about your sleep, workouts and heart data.
ส่วนนี้คัดจาก repo ของผู้ทำตามต้นฉบับ PopNx MCP ไม่ได้ตรวจหรือรับรองเนื้อหา ตัด HTML รูปภาพ และสคริปต์ออกแล้ว ลิงก์จะพาออกนอกเว็บ
The NIH-operated public registry of clinical trials worldwide. Every interventional study (and most observational ones) registered with the FDA must be filed here. ~470,000 studies covering Phase 1–4, conditions across the medical spectrum, and post-market safety surveillance. Free, no auth.
Part of Pipeworx — an MCP gateway connecting AI agents to 1863+ live data sources. This is an independent, unofficial integration — not affiliated with, endorsed by, or published by the upstream provider.
If your agent is answering anything about drug development, clinical research, or medical product safety, ClinicalTrials.gov is the source of truth for *what's being studied*. The data is structured: NCT IDs, phases, statuses, sponsors, conditions, interventions, primary/secondary outcomes, results.
Three core flows:
1. Search. "What's being studied for Alzheimer's?" → ct_search({query: "Alzheimer's disease"}) → studies matching, with NCT IDs, phase, status.
2. Specific study. "Tell me about this trial." → ct_get_study({nct_id: "NCT04280705"}) → full structured record including protocol, results, references.
3. Sponsor / volume. "How many trials does Pfizer run?" → ct_sponsor_trials({sponsor: "Pfizer"}) → enumerated by phase and status. See What sponsor means — it is the trials Pfizer *leads*, not every trial its drugs appear in.
4. Sponsor comparison. "Who has more recruiting Phase 3 obesity trials, Novo Nordisk or Eli Lilly?" → ct_compare_sponsors({sponsors: ["Novo Nordisk", "Eli Lilly and Company"], condition: "obesity", status: "RECRUITING", phase: "PHASE3"}) → lead-sponsor counts ranked under identical filters with sample NCT records.
For drug-level synthesis (combining trials with FDA approvals and adverse events), use pharma_drug_profile compound or [compare_entities({type: "drug", values: [...]})].
pipeworx://clinicaltrials/study/{nct_id}
NCT IDs are stable forever. Once issued, never reused. Embed in agent output as the canonical study reference.
sponsor meansEvery tool that takes a sponsor matches the registered lead sponsor — the organisation that filed and runs the trial. That is what "what is Merck about to read out" means, and it is the default everywhere.
It is deliberately narrower than it looks. A university's study of pembrolizumab is *not* a Merck trial; Merck made the drug, someone else is running the study. To include those, pass sponsor_match:
sponsor_match | Matches | Merck Sharp & Dohme LLC |
|---|---|---|
lead (default) | registered lead sponsor only | 2,175 studies |
lead_or_collaborator | also trials listing the company as a collaborator | 4,280 studies |
Every returned study carries `sponsor_match_field` (lead_sponsor or collaborator), so a caller can always see *why* a given trial came back.
Until 2026-08-29 these tools filtered with the registry's query.spons parameter, which is not a sponsor filter at all — it is a broad search that also matches collaborators and free text. A Merck query returned 4,282 studies led by the University of Utah, Weill Cornell and others, with no indication in the response that anything was wrong. If you have cached counts from before that date, they are the broad number, not the lead-sponsor one.
When users ask about "active" trials, they usually mean one of two things:
status=Recruiting — open to new participantsstatus=Recruiting OR Active, not recruiting OR Enrolling by invitationThe default ct_search returns all statuses; filter on status field of results for the meaning you want. Common confusion: "Completed" trials are studies that finished collecting data, not necessarily ones with published results.
ct_phase_transition_rates)The public analog of a probability-of-technical-success (PTRS) benchmark — the kind of number Evaluate Omnium and BioMedTracker sell. It measures how often a drug for a given disease actually advances from one clinical phase to the next, derived from the registry itself.
ct_phase_transition_rates({ condition: "non-small cell lung cancer" })
Returns Phase 1→2 and Phase 2→3 as numerator / denominator / rate, the censoring rule in plain words, the matching BIO/Informa/QLS 2011-2020 published benchmark, and the top 20 interventions with their phase path so the rate is auditable.
Method. Industry-sponsored interventional trials for the condition are grouped by normalized intervention name (lowercased; dose, route and formulation tokens stripped; compound code names preserved intact so PF-06463922 stays distinct from PF-06439535). Combination arms are split and counted under each component. An intervention *transitioned* from phase N to N+1 if it has a completed phase-N trial for the condition and any phase-N+1 trial for the same condition that started after that completion.
Censoring. censor_years (default 3) is the follow-up an asset must have had to count. An intervention only enters the denominator if its completed earlier-phase trial finished at least that many years before to_year; otherwise it has not had time to advance and counting it as a failure would understate the rate. Raising it demands more follow-up and shrinks the denominator — on NSCLC, censor_years: 3 gives a Phase 2→3 denominator of 410 and censor_years: 8 gives 301.
Fewer than 10 eligible interventions returns an explicit insufficient_data status with a reason, not a rate.
It counts trial existence in a registry, not outcomes. An asset that entered Phase 3 and failed still counts as a transition, so the result is an upper bound on technical success, never a probability of approval. Three further caveats ship in the response's limitations array:
Expect the registry-derived rate to run below the published one, because it requires the next-phase trial to have been publicly registered.
otherNames is not a synonym listWorth knowing if you build anything similar. Sponsors use interventions[].otherNames for genuine synonyms (lorlatinib ↔ PF-06463922) and for the other drugs in the regimen: AK112 Injection carries ["Pemetrexed", "Carboplatin"], and Standard of care neoadjuvant therapy lists six chemotherapies. Merging those naively pulled all 306 pemetrexed trials into a key called ak112, which then reported 76 Phase 2 trials under Eli Lilly for an Akeso antibody — with no error and a still-plausible rate. Two guards prevent it: a name that appears as a primary intervention anywhere in the corpus can never be absorbed as someone else's alias, and an arm listing more than two otherNames is treated as a regimen list and ignored. Both err toward splitting one asset in two (which understates the rate) rather than fusing two assets (which corrupts it).
semantic-scholar or crossref.ct_sponsor_trials. Try the more permissive form first. On a suspiciously low count the tools probe the registry and hand back did_you_mean with the registered spellings that actually exist.sponsor means.whyStopped in the full study record for context (safety signal vs. enrollment problems vs. funding).Add to your MCP client (Claude Desktop, Cursor, Windsurf, etc.):
{
"mcpServers": { "clinicaltrials": { "url": "https://gateway.pipeworx.io/clinicaltrials/mcp" } } }
tools/list at https://gateway.pipeworx.io/clinicaltrials/mcp returns the tools in the table above plus the shared Pipeworx meta-tools — ask_pipeworx, discover_tools, search_within, remember/recall and the rest of the gateway-wide set. So the tool count you see is larger than this table: a single-pack endpoint currently lists roughly 30 shared tools alongside the pack's own. The connection's initialize response states its exact scope, and is the authoritative answer for a given day.
This is deliberate, not multiplexing by accident. The meta-tools are what let a scoped connection answer a question this pack does not cover — via ask_pipeworx, which routes across the whole catalog — without you adding a second MCP server. There is currently no way to mount a pack endpoint without them; if the extra schemas cost you more context than the routing is worth, connect to the full gateway once rather than to several pack endpoints.
Or connect to the full Pipeworx gateway to get every pack's tools listed directly, instead of just this one's:
{
"mcpServers": { "pipeworx": { "url": "https://gateway.pipeworx.io/mcp" } } }
Both URLs reach the same gateway and the same 1863+ data sources. The only difference is which pack's tools are listed directly; ask_pipeworx reaches all of them from either one.
curl -X POST https://gateway.pipeworx.io/v1/tools/ct_search \
-H 'Content-Type: application/json' \ -d '{"query":"GLP-1 receptor agonist","status":"RECRUITING","phase":"PHASE2","limit":20}'
No account needed for the first calls. Inspect any tool: GET https://gateway.pipeworx.io/v1/tools/ct_search. Find one: POST https://gateway.pipeworx.io/v1/tools/search_packs with {"query":"..."}.
@pipeworx/mcp-clinicaltrials is a local stdio bridge to the Pipeworx gateway: your MCP client starts it with npx, and it forwards every call to the same https://gateway.pipeworx.io/clinicaltrials/mcp endpoint shown above. I