Aihw MCP vs Google Health Fitbit MCP
A side-by-side look at the Aihw MCP and Google Health Fitbit 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 Google Health Fitbit 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 Google Health Fitbit MCP when
- You need dedicated capabilities in the Biology & Bioinformatics domain.
- You prefer a local stdio subprocess transport.
- You want tools like
google_health_connection_status, google_health_quickstart, google_health_data_inventory.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Biology & Bioinformatics | Biology & Bioinformatics |
| Quality signal | 55/100 · Good | 57/100 · 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 | 19 |
| Directory activity | 1 views 0 installs 0 upvotes | 2 views 0 installs 0 upvotes |
| Tools | 6 | 18 |
| Full listing | View Aihw MCP | View Google Health Fitbit 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.
Google Health Fitbit MCP tools 18
google_health_connection_statusLocal config, token, scope and MCP-client readiness — no Google callgoogle_health_quickstartPersonalized 3-step setup walkthrough that adapts to your current stategoogle_health_data_inventorySupported domains, scopes, data-type naming and recommended agent flowgoogle_health_list_data_typesThe 39 kebab-case data-type slugs with units, scopes and supported verbsgoogle_health_demoRealistic synthetic payloads so agents see the contract before real callsgoogle_health_get_identity/v4/users/me/identity` — Google Health + legacy Fitbit identity mappinggoogle_health_get_profile/v4/users/me/profilegoogle_health_get_settings/v4/users/me/settings` — units, timezonegoogle_health_list_paired_devices/v4/users/me/pairedDevices` — your Fitbit trackers and Pixel Watches, and which device produces which datagoogle_health_get_irn_profile/v4/users/me/irnProfile` — irregular-rhythm (AFib) notification engagement statusgoogle_health_list_data_points/v4/.../dataPoints` — intraday detail for any data typegoogle_health_get_data_point/v4/.../dataPoints/{id}` — one specific sleep session, exercise or measurement- +6 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"
]
}
}
}
Google Health Fitbit MCP
{
"mcpServers": {
"google-health-fitbit-mcp": {
"command": "npx",
"args": [
"-y",
"google-health-fitbit-mcp"
]
}
}
}
Frequently asked questions
Aihw MCP is categorized under Biology & Bioinformatics and uses a local stdio subprocess. Google Health Fitbit MCP belongs to Biology & Bioinformatics and uses a local stdio subprocess. Pick Aihw MCP for biology & bioinformatics work and Google Health Fitbit 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. Google Health Fitbit 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 google-health-fitbit-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. Google Health Fitbit MCP has 57/100 (Good) with 2 views, 0 installs and 19 GitHub stars.