Aihw MCP vs Genomics MCP
A side-by-side look at the Aihw MCP and Genomics MCP MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose Aihw MCP for biology & bioinformatics work as a local process; choose Genomics MCP for biology & bioinformatics as a local process. You can also run both side by side in one mcpServers block.
Which MCP server should you choose?
Choose Aihw MCP when
- You need dedicated capabilities in the Biology & Bioinformatics domain.
- You prefer a local stdio subprocess transport.
- You want tools like
search_datasets, describe_dataset, get_data.
Choose Genomics MCP when
- You need dedicated capabilities in the Biology & Bioinformatics domain.
- You prefer a local stdio subprocess transport.
- You want tools like
list_sources, search_datasets, describe_dataset.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Biology & Bioinformatics | Biology & Bioinformatics |
| Quality signal | 55/100 · Good | 59/100 · Good |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Local subprocess (stdio) |
| Auth | Not declared | Not declared |
| Pricing | Not declared | Not declared |
| Required env vars | None listed | None listed |
| Verified owner | Community listing | Community listing |
| GitHub stars | 0 | 3 |
| Directory activity | 1 views 0 installs 0 upvotes | 1 views 0 installs 0 upvotes |
| Tools | 6 | 23 |
| Full listing | View Aihw MCP | View Genomics MCP |
Tools & capabilities
Aihw MCP tools 6
search_datasetsFuzzy-search the curated AIHW dataset catalog. All datasets ship hand-curated in v0.1: long-term mortality (GRIM), regional mortality (MORT), cancer incidence and mortality, health expenditure, youth justice detention, and the public hospitals register. Examples: # Find a dataset that gives deaths by cause results = await search_datasets("mortality cause of death") # → [{id: 'GRIM_DEATHS', name: 'GRIM — long-term mortality', ...}] # Discover what's available on cancer results = await search_datasets("cancer") Returns: List of DatasetSummary (id, name, description, update_frequency, is_curated), ranked by relevance.describe_datasetDescribe a dataset's filterable dimensions, returnable measures, units, and source. Use this before calling get_data on a new dataset — it tells you the valid filter keys ('sex', 'year', 'state'), the valid filter values ('Females', 'Males', 'Persons'), the measure aliases ('deaths', 'crude_rate_per_100000'), and the canonical source URL. Returns: DatasetDetail with id, name, description, period_coverage, list of dimensions, list of measures (each with key, source_column, unit, description), and source_url + download_url.get_dataQuery a curated AIHW dataset and return observations. Examples: # Deaths from diabetes, all years and sexes resp = await get_data( "GRIM_DEATHS", filters={"cause_of_death": "Diabetes"}, measures="deaths", ) # Breast cancer incidence in females over time resp = await get_data( "CANCER_INCIDENCE_MORTALITY", filters={"cancer_type": "Breast cancer", "sex": "Female", "type": "Incidence"}, ) # Public hospitals in NSW with peer group "Principal referral" resp = await get_data( "PUBLIC_HOSPITALS", filters={"state": "NSW", "peer_group_name": "Principal referral"}, ) Returns: DataResponse with records (or csv), unit, period bounds, row_count, source URL, and CC-BY attribution.latestReturn the most recent observation(s) per measure for a dataset. For transposed time-series tables this trims to the most-recent period. For wide datasets with a real period, "latest" means the most recent period(s) — keeping ALL entities/dimensions at that period, not an arbitrary N rows. For wide single-year tables with no period variation (most AIHW datasets) it returns the same shape as get_data, capped at `limit` rows so a large register (e.g. PUBLIC_HOSPITALS) doesn't blow the response budget. Truncated responses set DataResponse.truncated_at to the original row count. When the curated YAML declares a `headline_slice`, those filter values are applied automatically so the no-filter call returns ONE canonical headline row (e.g. HEALTH_EXPENDITURE → national total, all areas, all sources). User-supplied filters override the headline_slice per-key — `latest("HEALTH_EXPENDITURE", filters={"state": "NSW"})` keeps the area/source defaults and returns the NSW latest-year total. Examples: # Latest year of GRIM data for All causes combined resp = await latest("GRIM_DEATHS", filters={"cause_of_death": "All causes combined"}) # All ~700 public hospitals (default cap of 50 would otherwise apply) resp = await latest("PUBLIC_HOSPITALS", limit=1000)top_nReturn the N rows with the largest (or smallest) value of a measure. This is the most common agent workflow: "show me the top 10 X by Y". Without this tool, an agent would call get_data, receive the full table, and then sort/slice locally — wasting tokens and turns. top_n does the rank server-side and returns only the requested rows. Examples: # Top 10 causes of death in 2023 (Persons) top_n("GRIM_DEATHS", "deaths", n=10, filters={"sex": "Persons", "year": "2023"}) # 20 SA3 regions with the highest age-standardised mortality top_n("MORT_GEOGRAPHY", "age_standardised_rate_per_100000", filters={"category": "Statistical Area Level 3 (SA3)", "sex": "Persons", "year": "2023"}, n=20) # 5 lowest-funded health expenditure areas in NSW top_n("HEALTH_EXPENDITURE", "real_expenditure_millions", filters={"state": "NSW", "financial_year": "2022-23"}, n=5, direction="bottom") Returns: DataResponse with at most `n` records, sorted by `measure` value in the requested direction. Other fields (period, unit, attribution) match a regular get_data call.list_curatedList every curated dataset ID in this version of aihw-mcp. These are the datasets where get_data accepts plain-English filter keys and returns aliased, well-typed measure columns. Each ID is documented via describe_dataset. Returns: Sorted list of dataset IDs.
Genomics MCP tools 23
list_sourcesList data and reference sources, their state and supported operations.search_datasetsSearch one archive/catalog for studies or datasets by text query. Available for: ega, ena, encode, geo, ncbi_datasets.describe_datasetDescribe a study/dataset by native accession, with linked entities. Available for: ega, ena, encode, geo, ncbi_datasets.list_filesList files of a study/dataset with format, size, checksums, index and readiness. Available for: ega, ena, encode, geo, local, ncbi_datasets, s3.list_samplesList samples of a study/dataset as supplied by the source. Available for: ega, ena, encode, geo, ncbi_datasets.get_sample_metadataGet one sample's metadata and phenotype values exactly as supplied by the source. Available for: ega, ena, encode, geo, ncbi_datasets.fetch_fileStart a bounded download of a file (and index) to the local work dir. Returns a transfer job; files above the default limit need budget_bytes. Available for: default.get_transfer_statusGet progress/result of a transfer started by fetch_file. Available for: default.cancel_transferCancel a running transfer. Available for: default.get_readsAlignments overlapping an interval from indexed BAM/CRAM. Flag filters follow samtools -f/-F. CRAM needs a matching reference unless self-contained. Available for: bam, cram.get_coverageRead depth over an interval from indexed BAM/CRAM (default excludes UNMAP, SECONDARY, QCFAIL, DUP, like samtools depth). Available for: bam, cram.get_pileupPer-position base counts over an interval from indexed BAM/CRAM. Available for: bam, cram.- +11 more on the listing page
Ready-to-paste client configs
Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.
Aihw MCP
{
"mcpServers": {
"aihw-mcp": {
"command": "uvx",
"args": [
"--upgrade",
"aihw-mcp"
]
}
}
}
Genomics MCP
{
"mcpServers": {
"genomics-mcp": {
"command": "uvx",
"args": [
"rewire-genomics-mcp"
]
}
}
}
Frequently asked questions
Aihw MCP is categorized under Biology & Bioinformatics and uses a local stdio subprocess. Genomics MCP belongs to Biology & Bioinformatics and uses a local stdio subprocess. Pick Aihw MCP for biology & bioinformatics work and Genomics MCP for biology & bioinformatics.
Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client.
Aihw MCP has no declared pricing — check the project's own site. Genomics MCP has no declared pricing — check the project's own site. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.
Yes. MCP clients support multiple servers: add aihw-mcp and genomics-mcp as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Aihw MCP has a quality signal of 55/100 (Good) with 1 views, 0 installs and 0 GitHub stars. Genomics MCP has 59/100 (Good) with 1 views, 0 installs and 3 GitHub stars.