MCP MeasureMCP Measure

인덱스 관측됨

Cure Cancer With AI

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

사용해야 할까요

품질 및 안전성

A
설명 품질
91%
스키마 완전성
87%
이름 품질
95%
오염 위험
100%
권한 일치
100%
프로토콜 준수
100%

발견 사항 (2)

  • LOWTool 'predict_ppi' description lacks action verbpredict_ppi에서
  • LOWTool 'predict_clintox' description lacks action verbpredict_clintox에서

도구 정의와 프로토콜 준수에 대한 자동 분석을 기반으로 합니다.

컨텍스트 비용

~2,427토큰 (도구 정의)
~1.3 KB일반적인 응답 크기
중간 정도의 주의 영향 (128k 컨텍스트의 1.90%)

이는 서버의 도구가 모델의 컨텍스트에 로드될 때마다 소비되는 대략적인 토큰 수입니다. 수치가 높을수록 다른 작업에 사용할 수 있는 주의가 줄어듭니다.

설치

원클릭 설치

`claude_desktop_config.json` 파일에 다음을 추가하세요:

{
  "mcpServers": {
    "cure-cancer-with-ai": {
      "url": "https://www.curecancerwithai.com/api/mcp"
    }
  }
}

원격 엔드포인트

https://www.curecancerwithai.com/api/mcpstreamable-http

할 수 있는 일

도구 목록

도구 (15)

🟢 읽기 전용🟡 쓰기🔴 삭제⚪ 알 수 없음
🟢search_oncology(q, types, cancerType, limit)

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.

입력 스키마

{
  "type": "object",
  "properties": {
    "q": {
      "type": "string",
      "description": "The keyword to search for, e.g. \"osimertinib\"."
    },
    "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."
    },
    "limit": {
      "type": "integer",
      "minimum": 1,
      "maximum": 100,
      "description": "Max results per dataset (1–100, default 5)."
    }
  },
  "required": [
    "q"
  ],
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}
🟢list_research(cancerType, treatmentType, search, from, to, ...)

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.

입력 스키마

{
  "type": "object",
  "properties": {
    "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."
    },
    "search": {
      "type": "string",
      "description": "Keyword search across title and abstract."
    },
    "from": {
      "type": "string",
      "description": "ISO date lower bound (e.g. 2024-01-01)."
    },
    "to": {
      "type": "string",
      "description": "ISO date upper bound (e.g. 2024-12-31)."
    },
    "limit": {
      "type": "integer",
      "minimum": 1,
      "maximum": 100,
      "description": "Results per page (1–100, default 20)."
    },
    "offset": {
      "type": "integer",
      "minimum": 0,
      "description": "Number of results to skip (default 0)."
    }
  },
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}
🟢get_research_paper(idOrPubmedId)

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

입력 스키마

{
  "type": "object",
  "properties": {
    "idOrPubmedId": {
      "type": "string",
      "description": "Internal id or PubMed id, e.g. \"38123456\"."
    }
  },
  "required": [
    "idOrPubmedId"
  ],
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}
🟢list_news(cancerType, search, from, to, limit, ...)

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

입력 스키마

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

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

입력 스키마

{
  "type": "object",
  "properties": {
    "category": {
      "type": "string",
      "description": "Filter by primary category."
    },
    "cancerType": {
      "type": "string",
      "description": "Filter by cancer-type tag."
    },
    "search": {
      "type": "string",
      "description": "Keyword search across title, excerpt, and content."
    },
    "limit": {
      "type": "integer",
      "minimum": 1,
      "maximum": 100,
      "description": "Results per page (1–100, default 20)."
    },
    "offset": {
      "type": "integer",
      "minimum": 0,
      "description": "Number of results to skip (default 0)."
    }
  },
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}
🟡get_blog_post(slug)

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

입력 스키마

{
  "type": "object",
  "properties": {
    "slug": {
      "type": "string",
      "description": "Blog post slug, e.g. \"immunotherapy-breakthroughs\"."
    }
  },
  "required": [
    "slug"
  ],
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}
🟢list_fda_approvals(cancerType, search, from, to, limit, ...)

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

입력 스키마

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

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

입력 스키마

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

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

입력 스키마

{
  "type": "object",
  "properties": {
    "nctId": {
      "type": "string",
      "description": "NCT id or internal id, e.g. \"NCT01234567\"."
    }
  },
  "required": [
    "nctId"
  ],
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}
⚪predict_ppi(protein_a, protein_b)

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.

입력 스키마

{
  "type": "object",
  "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."
    }
  },
  "required": [
    "protein_a",
    "protein_b"
  ],
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}
⚪predict_dti(target_seq, drug_seq, norm_y_mean, norm_y_std)

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.

입력 스키마

{
  "type": "object",
  "properties": {
    "target_seq": {
      "type": "string",
      "description": "Target protein amino-acid sequence (single-letter codes)."
    },
    "drug_seq": {
      "type": "string",
      "description": "Drug structure in SMILES notation."
    },
    "norm_y_mean": {
      "type": "number",
      "description": "Optional normalization mean override."
    },
    "norm_y_std": {
      "type": "number",
      "description": "Optional normalization standard-deviation override."
    }
  },
  "required": [
    "target_seq",
    "drug_seq"
  ],
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}
⚪predict_clintox(smiles)

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.

입력 스키마

{
  "type": "object",
  "properties": {
    "smiles": {
      "type": "string",
      "description": "Compound structure in SMILES notation."
    }
  },
  "required": [
    "smiles"
  ],
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}
🟢search_compounds(examples, preferences, limit, include_characteristics)

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.

입력 스키마

{
  "type": "object",
  "properties": {
    "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",
      "additionalProperties": {
        "type": "number",
        "minimum": -4,
        "maximum": 4
      },
      "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."
    },
    "limit": {
      "type": "integer",
      "minimum": 1,
      "maximum": 100,
      "description": "Max results (1–100, default 20)."
    },
    "include_characteristics": {
      "type": "boolean",
      "description": "Include each result’s characteristic scores in the response."
    }
  },
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}
🟢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.

입력 스키마

{
  "type": "object",
  "properties": {},
  "$schema": "http://json-schema.org/draft-07/schema#"
}
🟢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.

입력 스키마

{
  "type": "object",
  "properties": {},
  "$schema": "http://json-schema.org/draft-07/schema#"
}

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