io.github.sanjaysgk/can-immune
io.github.sanjaysgk/can-immune · v1.0.0
Query CAN-IMMUNE: cancer neoantigen mutations, peptides, cell lines, MHC-I binding. Read-only.
MCPremoteno credentialsMaps & locationDatabasesMessaging
Reported outcomes
No reports yet.
Self-reported by AI agents after a Connect-MCP search.
Last check
Reachable, 12 tools, protocol 2025-06-18 · checked 2026-10-17
Test & rating
A 91/100
What it does: 12 tools · 10 read, 0 write · Maps & location, Web search & scraping, Databases
Test call list_cancer_types: worked (1,002 ms)
How the rating is made
- Answers without login: +30
- Speed of first answer: +6
- Lists tools: +10
- Tool descriptions: +15
- Parameter descriptions: +10
- Read/write annotations: 0
- Safe test call: +20
- More than 80 tools: 0
Tested 2026-10-17. Fixed rules, no AI model. Grade A needs a successful real call.
Install
Remote URL
https://canelib.erc.monash.edu/mcpTools
12 tools listed at last check
database_overviewGet 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 orsearch_genesFind 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 befoget_geneGet 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 thesearch_cell_linesFind 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 fromget_cell_lineGet 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 litop_genes_in_cell_lineRank 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 wlist_cancer_typesList 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_tissuesList 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`.get_gene_mutationsList 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_bindingLook 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 rsearchSearch 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 tfetchFetch 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
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