tomesphere

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

Sollte ich dies verwenden

Qualität und Sicherheit

A
Qualität der Beschreibung
94%
Vollständigkeit des Schemas
100%
Qualität der Benennung
93%
Risiko der Vergiftung
100%
Übereinstimmung der Berechtigungen
100%
Einhaltung des Protokolls
100%

Befunde (1)

  • LOWTool 'get_structure' description lacks action verbin get_structure

Basierend auf einer automatisierten Analyse der Tool-Definitionen und der Einhaltung des Protokolls.

Kontextkosten

~1,354Tokens (Tool-Definitionen)
~598 BTypische Antwortgröße
Mittlere Auswirkung auf die Aufmerksamkeit (1.06% von 128k Kontext)

Dies ist die ungefähre Anzahl der Tokens, die jedes Mal verbraucht werden, wenn die Tools des Servers in den Kontext eines Modells geladen werden. Höhere Werte verringern die Aufmerksamkeit, die für andere Aufgaben verfügbar ist.

Installieren

Installation mit einem Klick

Fügen Sie dies Ihrer Datei `claude_desktop_config.json` hinzu:

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

Remote-Endpunkte

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

Was es kann

Tool-Inventar

Tools (9)

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🟢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'.

Eingabe-Schema

{
  "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.

Eingabe-Schema

{
  "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'.

Eingabe-Schema

{
  "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.

Eingabe-Schema

{
  "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'.

Eingabe-Schema

{
  "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/…).

Eingabe-Schema

{
  "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.

Eingabe-Schema

{
  "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.

Eingabe-Schema

{
  "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.

Eingabe-Schema

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

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