mcpbeat

Cure Cancer With AI MCP Server

io.github.hifarrer/cure-cancer-with-ai
answering

Cure Cancer With AI is answering right now. Last checked 7 min ago. It exposes 15 tools. Last commit 12 Jun 2026.

Free oncology data (research, trials, FDA approvals, news) plus IBM MAMMAL biomedical predictions.

Uptime history 40 hours of history
40 hours agonow
100.0%
Uptime 24h
91 of 91 checks
15
Tools
read from the server
511 ms
Response time
average over 24h
0
Stars
last commit 12 Jun 2026

Connect this server

Endpoint below is the one we actually reach during checks — not the one copied from a README. Last verified 7 min ago.

run in your terminal
claude mcp add cure-cancer-with-ai --transport http https://www.curecancerwithai.com/api/mcp
~/Library/Application Support/Claude/claude_desktop_config.json
{
  "mcpServers": {
    "cure-cancer-with-ai": {
      "url": "https://www.curecancerwithai.com/api/mcp"
    }
  }
}
~/.codex/config.toml
[mcp_servers.cure-cancer-with-ai]
url = "https://www.curecancerwithai.com/api/mcp"
.cursor/mcp.json
{
  "mcpServers": {
    "cure-cancer-with-ai": {
      "url": "https://www.curecancerwithai.com/api/mcp"
    }
  }
}
.vscode/mcp.json
{
  "mcpServers": {
    "cure-cancer-with-ai": {
      "url": "https://www.curecancerwithai.com/api/mcp"
    }
  }
}

Available tools 15

Read directly from the server with tools/list, grouped by what they act on. If a tool disappears, we record the date.

predict
predict_clintox
Predict clinical-trial toxicity for a compound using IBM MAMMAL. Returns pred 1 (toxic / likely to fail trials) or 0 (not toxic) plus a raw score. Inference is CPU-bound and may take up to ~60s.
predict_dti
Predict drug–target binding affinity as pKd (−log10 Kd; higher = stronger binding) using IBM MAMMAL. Inference is CPU-bound and may take up to ~60s.
predict_ppi
Predict the binding-affinity class for a pair of proteins using the IBM MAMMAL biomedical foundation model. Returns label "1" (interacting) or "0" (non-interacting). Inference is CPU-bound and may take up to ~60s.
blog
get_blog_post
Fetch a single blog post by slug, including the full article content.
list_blog_posts
List editorial blog articles (excerpts). Use get_blog_post for full content. Filter by category, cancer-type tag, or keyword.
clinical
get_clinical_trial
Fetch a single clinical trial by NCT id (or internal id), including eligibility criteria and locations.
list_clinical_trials
List clinical trials from public registries (conditions, status, intervention type). Filter by condition, status (e.g. RECRUITING), or keyword.
research
get_research_paper
Fetch a single research paper by its internal id or PubMed id.
list_research
List peer-reviewed oncology research papers ingested from PubMed (abstracts, authors, journal, plain-language summaries). Filter by cancer type, treatment type, keyword, or publication date.
compound
list_compound_characteristics
List the pharmaceutical-compound characteristics (each scored on the −4…+4 scale) that can be used as preference keys in search_compounds.
compounds
search_compounds
Find pharmaceutical compounds two ways: by example drugs you already know (fuzzy-matched), or by setting target characteristics on a −4…+4 scale. Provide exactly one of `examples` or `preferences`. Use list_compound_characteristics for the available preference names.
fda
list_fda_approvals
List FDA-approved oncology drugs with indication, company, approval date, and label links. Filter by cancer type, keyword, or approval date.
mammal
mammal_health
Check whether the IBM MAMMAL prediction model is loaded and ready. No API key required. Call this before predict_* tools if a prior prediction timed out.
news
list_news
List curated cancer news articles aggregated from trusted sources. Filter by cancer type, keyword, or published date.
oncology
search_oncology
Search a keyword across every Cure Cancer With AI dataset at once — research papers, news, blog posts, FDA approvals, and clinical trials — with results grouped by type. Use this first for broad discovery, then fetch a single record by id/slug/nctId for full detail.

Endpoints

URLTransportStateLatencyChecked
https://www.curecancerwithai.com/api/mcp streamable-http answering 350 ms 7 min ago

Cure Cancer With AI — questions

Answers built from our own checks of this server.

What can Cure Cancer With AI do?
It exposes 15 tools, read directly from the server on our last check. Among them: get_blog_post, get_clinical_trial, get_research_paper, list_blog_posts, list_clinical_trials, list_compound_characteristics and 9 more. The full list with descriptions is on this page — we take it from the server itself via tools/list, not from a README. How MCP servers expose tools in the first place →
What is Cure Cancer With AI mostly used for?
Its tools cluster around predict, clinical and research. That is what this server is built to work with — the grouping comes from the actual tool names, not from a category we assigned.
Is Cure Cancer With AI working right now?
We send a real MCP handshake every 15 minutes. Over the last 24 hours 91 of 91 checks got a reply (100.0%), average response time 511 ms. The bar chart above shows every period we have measured.
How do I connect Cure Cancer With AI?
Copy the ready config from this page — we generate it for Claude Code, Claude Desktop, Codex, Cursor and VS Code, each with the file path that client actually reads. It is a remote server, so there is nothing to install — the client connects to the address.
Does Cure Cancer With AI need an API key?
No. Cure Cancer With AI completed a full MCP handshake with us as an anonymous client and listed its tools without asking for anything. All 15 of them are readable on this page. This is what we observed, not what the docs claim.
How fast is Cure Cancer With AI?
It answers our handshake in 511 ms on average, which is faster than 21% of all working MCP servers we measure. That is on the slow side — worth knowing if the tool sits inside an interactive loop. The comparison comes from our own checks across the whole registry, every 15 minutes.
Is Cure Cancer With AI open source?
Yes — it is published under the NOASSERTION licence and 0 stars on GitHub. The source link is on this page, so you can read exactly what it does with your data before you connect it.