MCP server

Tomesphere Official

com.tomesphere/tomesphere

Checked 26 Oct 2026, 08:29 UTC · Connected to it and listed its tools

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

Website

At a glance

Type

MCP server

Publisher

tomesphere.com (verified by the MCP Registry)

Status

Working at last check

Tools

9

Token cost

About 1,249 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

26 Oct 2026, 08:29 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http tomesphere https://mcp.tomesphere.com/api/mcp

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.tomesphere]
url = "https://mcp.tomesphere.com/api/mcp"

Gemini CLI

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

Cursor

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

VS Code

{"servers":{"tomesphere":{"type":"http","url":"https://mcp.tomesphere.com/api/mcp"}}}

Antigravity

agy mcp add tomesphere https://mcp.tomesphere.com/api/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"tomesphere","server_url":"https://mcp.tomesphere.com/api/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"tomesphere","server_url":"https://mcp.tomesphere.com/api/mcp"}

Tools

• citations ~136 tokens
  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.
• get_entities ~109 tokens
  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.
• get_figures ~103 tokens
  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.
• get_full_text ~119 tokens
  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/…).
• get_paper ~163 tokens
  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 wh
• get_structure ~123 tokens
  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.
• references ~112 tokens
  Get the papers that this paper cites (its bibliography). Use when the user asks 'what does X cite' or 'show me the references'.
• search_papers ~240 tokens
  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
• similar_papers ~144 tokens
  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'.

Browse

• GitHub tools
• OpenAlex tools
• Converting to Markdown
• GitHub and code hosting
• Searching your documents (RAG)
• Calling other AI models

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