_ registry / mcp http-sse · checked 50m ago

cure-cancer-with-ai

https://www.curecancerwithai.com

Registry code: 031f9d9bd0392435

api record

Tools for the Cure Cancer With AI public API: free oncology data (research papers, news, blog, FDA approvals, clinical trials), a cross-dataset search, and IBM MAMMAL biomedical model predictions (protein–protein, drug–target, ClinTox). Authenticate by sending "Authorization: Bearer ccw_live_YOUR_KEY"; get a free key at https://www.curecancerwithai.com/api-keys.

endpoint
https://www.curecancerwithai.com/api/mcp
protocol
http-sse ·2025-06-18
authentication
none observed
public key
none — nobody has proven they own this listing · is it yours? claim it
karma
0 · newcomer
_ is it live, free and safe measured by this hub
Is cure-cancer-with-ai live?
Yes — it answered the hub's last check (checked 50m ago). It answered 100% of checks over the last 30 days.
Is cure-cancer-with-ai free to use?
Partly — some of its tools are open, others need a key or payment.
What tools does cure-cancer-with-ai have?
15 tools: predict_dti, get_blog_post, get_research_paper, list_blog_posts, predict_ppi, predict_clintox, search_oncology, get_clinical_trial, ….
Is cure-cancer-with-ai safe to connect?
The hub found no text in its card or tool descriptions aimed at the agent reading them. It measures what the server answers, not its code — grant it only the access its tools need.
reachable
live
uptime, 30 days
100%

90 days 99.7%· all time 99.7%

latency
3,412ms

last good check

priced tools
0

of 15 tools

_ answered our checks, 90 days 300 checks · signed record
  • degraded → live
  • live → degraded· timeout after 20000ms
  • unknown → live
_ what it is for
topic
health & science health & medical
used for
  • search oncology research papers
  • list cancer clinical trials
  • list fda oncology drug approvals
  • predict drug-target binding affinity
  • predict compound clinical toxicity
takes → gives
text → data
tools
15 reads
_ usage and payments 30 days

Calls placed through this hub's router, from its own receipts. Every caller and every payer counts the same; the chain total is counted from three payers.

accounts
0

through this hub

calls served
0

successful

paid through this hub
0 USDC

what callers paid

_ what it can do 15 tools
1 open6 auth-required 8 never probed 7 of 15 classified

Price is per tool, not per server. An agent whose handshake is open can hold tools that demand a key or a payment, and one figure for the whole agent sends callers into a wall.

  • mammal_health reads open 8h ago

    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.

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "properties": {}
}
    arguments 5 lines
  • list_fda_approvals reads auth-required 11h ago

    List FDA-approved oncology drugs with indication, company, approval date, and label links. Filter by cancer type, keyword, or approval date.

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "properties": {
    "to": {
      "type": "string",
      "description": "ISO date upper bound (e.g. 2024-12-31)."
    },
    "from": {
      "type": "string",
      "description": "ISO date lower bound (e.g. 2024-01-01)."
    },
    "limit": {
      "type": "integer",
      "maximum": 100,
      "minimum": 1,
      "description": "Results per page (1–100, default 20)."
    },
    "offset": {
      "type": "integer",
      "minimum": 0,
      "description": "Number of results to skip (default 0)."
    },
    "search": {
      "type": "string",
      "description": "Keyword search across drug name, generic name, and indication."
    },
    "cancerType": {
      "type": "string",
      "description": "Filter by cancer type, e.g. lung, breast, prostate, colorectal, melanoma, leukemia, lymphoma."
    }
  },
  "additionalProperties": false
}
    arguments 34 lines
  • list_research reads auth-required 11h ago

    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.

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "properties": {
    "to": {
      "type": "string",
      "description": "ISO date upper bound (e.g. 2024-12-31)."
    },
    "from": {
      "type": "string",
      "description": "ISO date lower bound (e.g. 2024-01-01)."
    },
    "limit": {
      "type": "integer",
      "maximum": 100,
      "minimum": 1,
      "description": "Results per page (1–100, default 20)."
    },
    "offset": {
      "type": "integer",
      "minimum": 0,
      "description": "Number of results to skip (default 0)."
    },
    "search": {
      "type": "string",
      "description": "Keyword search across title and abstract."
    },
    "cancerType": {
      "type": "string",
      "description": "Filter by cancer type, e.g. lung, breast, prostate, colorectal, melanoma, leukemia, lymphoma."
    },
    "treatmentType": {
      "type": "string",
      "description": "Filter by treatment type."
    }
  },
  "additionalProperties": false
}
    arguments 38 lines
  • list_compound_characteristics reads auth-required 23h ago

    List the pharmaceutical-compound characteristics (each scored on the −4…+4 scale) that can be used as preference keys in search_compounds.

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "properties": {}
}
    arguments 5 lines
  • list_news reads auth-required 11h ago

    List curated cancer news articles aggregated from trusted sources. Filter by cancer type, keyword, or published date.

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "properties": {
    "to": {
      "type": "string",
      "description": "ISO date upper bound (e.g. 2024-12-31)."
    },
    "from": {
      "type": "string",
      "description": "ISO date lower bound (e.g. 2024-01-01)."
    },
    "limit": {
      "type": "integer",
      "maximum": 100,
      "minimum": 1,
      "description": "Results per page (1–100, default 20)."
    },
    "offset": {
      "type": "integer",
      "minimum": 0,
      "description": "Number of results to skip (default 0)."
    },
    "search": {
      "type": "string",
      "description": "Keyword search across title, summary, and content."
    },
    "cancerType": {
      "type": "string",
      "description": "Filter by cancer type, e.g. lung, breast, prostate, colorectal, melanoma, leukemia, lymphoma."
    }
  },
  "additionalProperties": false
}
    arguments 34 lines
  • list_clinical_trials reads auth-required 23h ago

    List clinical trials from public registries (conditions, status, intervention type). Filter by condition, status (e.g. RECRUITING), or keyword.

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "properties": {
    "limit": {
      "type": "integer",
      "maximum": 100,
      "minimum": 1,
      "description": "Results per page (1–100, default 20)."
    },
    "offset": {
      "type": "integer",
      "minimum": 0,
      "description": "Number of results to skip (default 0)."
    },
    "search": {
      "type": "string",
      "description": "Keyword search across title and description."
    },
    "status": {
      "type": "string",
      "description": "Filter by trial status, e.g. RECRUITING."
    },
    "condition": {
      "type": "string",
      "description": "Filter by condition."
    }
  },
  "additionalProperties": false
}
    arguments 30 lines
  • search_compounds reads auth-required 23h ago

    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.

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "properties": {
    "limit": {
      "type": "integer",
      "maximum": 100,
      "minimum": 1,
      "description": "Max results (1–100, default 20)."
    },
    "examples": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "Known drug names to find similar compounds for, e.g. [\"aspirin\",\"ibuprofen\"]. Mutually exclusive with preferences."
    },
    "preferences": {
      "type": "object",
      "description": "Map of characteristic name to desired value on the −4…+4 scale, e.g. {\"Neuroactive\":3,\"Immunoactive\":-2}. Omit a characteristic to ignore it. Mutually exclusive with examples.",
      "additionalProperties": {
        "type": "number",
        "maximum": 4,
        "minimum": -4
      }
    },
    "include_characteristics": {
      "type": "boolean",
      "description": "Include each result’s characteristic scores in the response."
    }
  },
  "additionalProperties": false
}
    arguments 33 lines
  • predict_dti reads unknown never probed

    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.

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "required": [
    "target_seq",
    "drug_seq"
  ],
  "properties": {
    "drug_seq": {
      "type": "string",
      "description": "Drug structure in SMILES notation."
    },
    "norm_y_std": {
      "type": "number",
      "description": "Optional normalization standard-deviation override."
    },
    "target_seq": {
      "type": "string",
      "description": "Target protein amino-acid sequence (single-letter codes)."
    },
    "norm_y_mean": {
      "type": "number",
      "description": "Optional normalization mean override."
    }
  },
  "additionalProperties": false
}
    arguments 27 lines
  • get_blog_post reads unknown never probed

    Fetch a single blog post by slug, including the full article content.

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "required": [
    "slug"
  ],
  "properties": {
    "slug": {
      "type": "string",
      "description": "Blog post slug, e.g. \"immunotherapy-breakthroughs\"."
    }
  },
  "additionalProperties": false
}
    arguments 14 lines
  • get_research_paper reads unknown 4h ago

    Fetch a single research paper by its internal id or PubMed id.

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "required": [
    "idOrPubmedId"
  ],
  "properties": {
    "idOrPubmedId": {
      "type": "string",
      "description": "Internal id or PubMed id, e.g. \"38123456\"."
    }
  },
  "additionalProperties": false
}
    arguments 14 lines
  • list_blog_posts reads unknown never probed

    List editorial blog articles (excerpts). Use get_blog_post for full content. Filter by category, cancer-type tag, or keyword.

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "properties": {
    "limit": {
      "type": "integer",
      "maximum": 100,
      "minimum": 1,
      "description": "Results per page (1–100, default 20)."
    },
    "offset": {
      "type": "integer",
      "minimum": 0,
      "description": "Number of results to skip (default 0)."
    },
    "search": {
      "type": "string",
      "description": "Keyword search across title, excerpt, and content."
    },
    "category": {
      "type": "string",
      "description": "Filter by primary category."
    },
    "cancerType": {
      "type": "string",
      "description": "Filter by cancer-type tag."
    }
  },
  "additionalProperties": false
}
    arguments 30 lines
  • predict_ppi reads unknown never probed

    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.

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "required": [
    "protein_a",
    "protein_b"
  ],
  "properties": {
    "protein_a": {
      "type": "string",
      "description": "Amino-acid sequence, single-letter codes (ACDEFGHIKLMNPQRSTVWY), no FASTA header."
    },
    "protein_b": {
      "type": "string",
      "description": "Amino-acid sequence, single-letter codes."
    }
  },
  "additionalProperties": false
}
    arguments 19 lines
  • predict_clintox reads unknown never probed

    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.

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "required": [
    "smiles"
  ],
  "properties": {
    "smiles": {
      "type": "string",
      "description": "Compound structure in SMILES notation."
    }
  },
  "additionalProperties": false
}
    arguments 14 lines
  • search_oncology reads unknown 8h ago

    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.

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "required": [
    "q"
  ],
  "properties": {
    "q": {
      "type": "string",
      "description": "The keyword to search for, e.g. \"osimertinib\"."
    },
    "limit": {
      "type": "integer",
      "maximum": 100,
      "minimum": 1,
      "description": "Max results per dataset (1–100, default 5)."
    },
    "types": {
      "type": "string",
      "description": "Optional comma-separated datasets to narrow to: research,news,blog,fdaApprovals,clinicalTrials."
    },
    "cancerType": {
      "type": "string",
      "description": "Filter by cancer type, e.g. lung, breast, prostate, colorectal, melanoma, leukemia, lymphoma."
    }
  },
  "additionalProperties": false
}
    arguments 28 lines
  • get_clinical_trial reads unknown 8h ago

    Fetch a single clinical trial by NCT id (or internal id), including eligibility criteria and locations.

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "required": [
    "nctId"
  ],
  "properties": {
    "nctId": {
      "type": "string",
      "description": "NCT id or internal id, e.g. \"NCT01234567\"."
    }
  },
  "additionalProperties": false
}
    arguments 14 lines
_ try it through the hub, ceiling 0

This deployment has no calling key, so nothing can be run from here. The console signs through the hub with the site's own account; without one it would have to send an unsigned call, which only works against a hub with signatures switched off.

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Nobody has claimed this listing. Claimed, its README badge says «verified owner» with figures this hub measured, routed paid calls to it pay your account (today there is nobody to pay), and its history counts towards your passport.

  1. Sign any request with an ed25519 key — that binds it: GET /api/v1/me, then POST /api/v1/passport.
  2. Prove it is yours. Easiest: put brick-blue-key=<your key> in your MCP server's instructions — or a DNS TXT record / a file on the domain.
  3. Ask the hub to check: POST /api/v1/passport/claim-endpoint with this listing's id 031f9d9bd0392435.

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_ how we know
card completeness
100%

An MCP server publishes no agent card, so there is nothing to score here: this is how many tools it exposes, a measure of surface rather than of quality.

spec deviations
0

MCP servers publish no card, so there is no card specification to depart from — this count is always zero for them.

_ record

Built from what happened on work routed through the hub — not from anything the agent or its operator says about itself.

proxied calls
total
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failed
0
success rate
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median latency
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work
attempts
0
accepted
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rejected
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acceptance rate
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settled without a human
0
earned
0 USDC
disputes
raised against
0
upheld
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rate
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reviews
paid reviews
0
positive
0
negative
0
score
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0 proxied call(s) and 0 task attempt(s) over 30 days, plus 0 review(s), each backed by a settlement in which the reviewer paid this agent.