MCP server

Can Immune

io.github.sanjaysgk/can-immune

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

Query CAN-IMMUNE: cancer neoantigen mutations, peptides, cell lines, MHC-I binding. Read-only.

Website Source

At a glance

Type

MCP server

Publisher

Not verified

Status

Working at last check

Tools

12

Token cost

About 2,542 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 can-immune https://canelib.erc.monash.edu/mcp

Claude Desktop

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

Codex (config.toml)

[mcp_servers.can-immune]
url = "https://canelib.erc.monash.edu/mcp"

Gemini CLI

{"mcpServers":{"can-immune":{"httpUrl":"https://canelib.erc.monash.edu/mcp"}}}

Cursor

{"mcpServers":{"can-immune":{"url":"https://canelib.erc.monash.edu/mcp"}}}

VS Code

{"servers":{"can-immune":{"type":"http","url":"https://canelib.erc.monash.edu/mcp"}}}

Antigravity

agy mcp add can-immune https://canelib.erc.monash.edu/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":"can-immune","server_url":"https://canelib.erc.monash.edu/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"can-immune","server_url":"https://canelib.erc.monash.edu/mcp"}

Tools

• database_overview ~151 tokens
  Get the headline size of the CAN-IMMUNE database in one call. Returns total counts of mutations, genes, and unique mutant peptides, plus how many cell lines, tissues, and cancer types are covered, and the data sources (COSMIC, DepMap/CCLE, PubMed). Use this first to size the resource or
• fetch ~180 tokens
  Fetch the full record for an id returned by `search` (ChatGPT connector interface). Takes an id like 'gene:TP53' or 'cell_line:MDA-MB-231' and returns {id, title, text, url, metadata} where `text` is the full JSON profile (same data as get_gene / get_cell_line). Always call `search` fir
• get_cell_line ~222 tokens
  Get the full profile for one cancer cell line by exact name. Returns tissue of origin, cancer type, total mutations, unique mutant peptides and genes, data sources, Cellosaurus and DepMap model IDs, gender, category / cell type, and the top ~15 most-mutated genes in that line, plus a li
• get_gene ~229 tokens
  Get the full profile for one gene by exact symbol. Returns total mutations and how they split between cell lines vs tissues, unique mutant peptides, sample and transcript counts, UniProt accession / name / reviewed status, the top ~15 recurrent mutations in that gene, and a link to the
• get_gene_mutations ~260 tokens
  List individual mutations in a gene (a bounded sample of rows). Each row gives the CAN-IMMUNE mutation ID (CANLIB...), the amino-acid change, the CDS change, mutation type (missense / complex substitution), data source, the sample it came from, its primary site, and the mutant peptide.
• get_mhc_binding ~259 tokens
  Look up a CACHED MHC-I binding prediction for a peptide + HLA allele. Returns the NetMHCpan result (best binding core, %Rank_EL, binder level SB/WB/NB, and neoepitope %Rank_Neo when computed) for predictions already stored in the cache. This tool does NOT run new predictions - it only r
• list_cancer_types ~156 tokens
  List cancer types (histology) ranked by mutation burden. Returns each cancer type / histology with its total mutations, unique samples, and unique genes, most-mutated first. Use to see which cancer types dominate the database or to pick one for further drill-down on the website.
• list_tissues ~154 tokens
  List primary tissues ranked by mutation burden. Returns each tissue (COSMIC primary-tissue samples only, kept separate from cell lines) with its total mutations, unique samples, and unique genes, most-mutated first. Use for tissue-level questions; for cell lines use `search_cell_lines`.
• search ~210 tokens
  Search CAN-IMMUNE for genes and cell lines (ChatGPT connector interface). Returns {results: [{id, title, url}]} combining matching genes and cell lines, each with a stable `id` (like 'gene:TP53' or 'cell_line:MDA-MB-231'). Pass that `id` to `fetch` to retrieve the full record. This is t
• search_cell_lines ~260 tokens
  Find cancer cell lines by name, ranked by mutation burden. Returns each matching cell line with its tissue of origin, cancer type, total mutations, unique mutant peptides, data sources (COSMIC / DepMap-CCLE / PubMed), and Cellosaurus ID. Covers only cell-line models (kept separate from
• search_genes ~248 tokens
  Find genes by symbol or full name, ranked by how mutated they are. Matches the gene symbol OR the full gene name (partial, case-insensitive) and returns each hit with its total mutation count, unique mutant-peptide count, sample count, and UniProt accession. Use it to locate a gene befo
• top_genes_in_cell_line ~213 tokens
  Rank the most-mutated genes within one specific cell line. Answers "what is the top mutated gene in <cell line>" / "which genes are most altered in <cell line>". Returns genes with their mutation counts for that line, highest first, from precomputed per-cell-line stats (fast). For the w

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