tomesphere

Search 8.5M scientific papers with LLM TLDRs, citations, linked entities, figures, and full text.

我该使用它吗

质量与安全性

A
描述质量
94%
模式完整度
100%
命名质量
93%
投毒风险
100%
权限匹配度
100%
协议合规性
100%

发现(1)

  • LOWTool 'get_structure' description lacks action verb在 get_structure 中

基于对工具定义和协议合规性的自动分析。

上下文开销

~1,354token 数(工具定义)
~598 B典型响应大小
对注意力有中等影响(占 128k 上下文窗口的 1.06%)

这是每次将服务器的工具加载到模型上下文窗口时所消耗的大致 token 数。数值越高,可用于其他任务的注意力就越少。

安装

一键安装

将以下内容添加到你的 `claude_desktop_config.json` 文件中:

{
  "mcpServers": {
    "tomesphere": {
      "url": "https://mcp.tomesphere.com/api/mcp"
    }
  }
}

远程端点

https://mcp.tomesphere.com/api/mcpstreamable-http

它能做什么

工具清单

工具(9)

🟢 只读🟡 写入🔴 删除⚪ 未知
🟢search_papers(query, k, year_min, year_max)

Search 8.5 million academic papers (arXiv + biomedical: PMC / bioRxiv / medRxiv, all disciplines) by topic, keyword, author, or linked entity (gene / protein / disease). Each hit returns id, title, TLDR, type, and links. Use to find papers about a topic, e.g. 'transformer efficiency' or 'CRISPR base editing'.

输入模式

{
  "type": "object",
  "properties": {
    "query": {
      "type": "string",
      "description": "Natural-language search query. E.g. 'transformer attention efficiency', 'graph neural networks for molecular property prediction'."
    },
    "k": {
      "type": "integer",
      "description": "Number of results (default 10, max 25).",
      "default": 10
    },
    "year_min": {
      "type": "integer",
      "description": "Earliest publication year, e.g. 2024."
    },
    "year_max": {
      "type": "integer",
      "description": "Latest publication year."
    }
  },
  "required": [
    "query"
  ]
}
🟢get_paper(id)

Fetch a paper's full metadata: title, authors, year, abstract, TLDR (LLM-generated), key findings, citation count, GitHub repos, HuggingFace models/datasets, videos, peer reviews, and links. Accepts an arXiv ID (e.g. '2401.12345' or '1706.03762v5') or an OpenAlex Work ID (e.g. 'W4390723197'). Use when the user names a specific paper or pastes an arXiv link.

输入模式

{
  "type": "object",
  "properties": {
    "id": {
      "type": "string",
      "description": "arXiv ID like '2401.12345' or OpenAlex Work ID like 'W4390723197'."
    }
  },
  "required": [
    "id"
  ]
}
🟢similar_papers(id, k)

Find papers semantically similar to a given paper using SPECTER2 embeddings (trained on scientific-citation triplets). Returns nearest neighbors with TLDR. Use when the user wants 'papers like X' or 'what's adjacent to this work'.

输入模式

{
  "type": "object",
  "properties": {
    "id": {
      "type": "string",
      "description": "arXiv ID or OpenAlex Work ID."
    },
    "k": {
      "type": "integer",
      "description": "Number of neighbors (default 10, max 25).",
      "default": 10
    }
  },
  "required": [
    "id"
  ]
}
🟢citations(id, k)

Get papers that cite the given paper (who refers to this work). Use when the user asks 'who cites X', 'what's the impact', or wants follow-up work. Note: 2024+ citation coverage is sparse; indexing in progress.

输入模式

{
  "type": "object",
  "properties": {
    "id": {
      "type": "string",
      "description": "arXiv ID or OpenAlex Work ID."
    },
    "k": {
      "type": "integer",
      "description": "Max citing papers (default 25, max 100).",
      "default": 25
    }
  },
  "required": [
    "id"
  ]
}
🟢references(id, k)

Get the papers that this paper cites (its bibliography). Use when the user asks 'what does X cite' or 'show me the references'.

输入模式

{
  "type": "object",
  "properties": {
    "id": {
      "type": "string",
      "description": "arXiv ID or OpenAlex Work ID."
    },
    "k": {
      "type": "integer",
      "description": "Max references (default 25, max 100).",
      "default": 25
    }
  },
  "required": [
    "id"
  ]
}
🟢get_full_text(id)

Fetch a paper's full body text as Markdown (methods, results, protocols, inline tables) — use for deep questions the abstract can't answer. Accepts an arXiv ID (2401.12345), a PMC ID (PMC5339222), or a bioRxiv/medRxiv DOI (10.1101/…).

输入模式

{
  "type": "object",
  "properties": {
    "id": {
      "type": "string",
      "description": "arXiv ID, PMC ID, or 10.1101/… DOI."
    }
  },
  "required": [
    "id"
  ]
}
🟢get_figures(id)

Get a paper's real figure images — URLs, labels, and captions. Most biomedical papers have figures; arXiv papers often don't. Accepts an arXiv ID, PMC ID, or bioRxiv/medRxiv DOI.

输入模式

{
  "type": "object",
  "properties": {
    "id": {
      "type": "string",
      "description": "arXiv ID, PMC ID, or 10.1101/… DOI."
    }
  },
  "required": [
    "id"
  ]
}
🟢get_entities(id)

Get the biomedical entities linked to a paper: genes, proteins, chemicals, diseases, species, mutations, cell lines, and clinical-trial (NCT) IDs. Accepts an arXiv ID, PMC ID, or bioRxiv/medRxiv DOI.

输入模式

{
  "type": "object",
  "properties": {
    "id": {
      "type": "string",
      "description": "arXiv ID, PMC ID, or 10.1101/… DOI."
    }
  },
  "required": [
    "id"
  ]
}
🟢get_structure(name)

Resolve a gene/protein name (e.g. 'TP53', 'CD44') or UniProt accession to its 3D structure — returns the UniProt accession + AlphaFold model URL (and PDB when available). Great for a gene named in a paper's entities.

输入模式

{
  "type": "object",
  "properties": {
    "name": {
      "type": "string",
      "description": "Gene/protein symbol (TP53, CD44) or UniProt accession (P04637)."
    }
  },
  "required": [
    "name"
  ]
}

社区

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最近观测

已验证未记录版本9 个工具
已验证未记录版本9 个工具