Fitbit Health MCP Server
Provides read-only access to Google Health data, including sleep, steps, heart rate, heart rate variability, and health summaries.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Fitbit Health MCP Servershow my health summary for the last 7 days"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Fitbit Health MCP
A single-user reference implementation that exposes read-only Google Health data to ChatGPT through a remote Model Context Protocol (MCP) server.
The project supports both local stdio MCP and remote Streamable HTTP MCP. The remote path separates ChatGPT authentication (MCP OAuth 2.1 Authorization Code with PKCE) from Google Health authorization (Google Web OAuth with PKCE), and is packaged for deployment on Render.
This project is not a medical device and does not provide diagnosis, treatment, or medication advice.
Architecture
flowchart LR
Owner["Owner browser"] -->|"Google Web OAuth + PKCE"| Google["Google OAuth"]
Google -->|"Google credentials"| Token["Private token.json"]
ChatGPT["ChatGPT Connector"] -->|"MCP OAuth 2.1 + PKCE"| OAuth["MCP authorization server"]
OAuth -->|"MCP access token"| MCP["Remote /mcp endpoint"]
MCP --> Service["HealthMCPService"]
Service -->|"Load and refresh Google credentials"| Token
Service --> API["Google Health API"]
API -->|"Requested health data"| Service
Service -->|"Structured MCP result"| ChatGPTThe two OAuth boundaries are intentionally independent:
MCP OAuth authenticates ChatGPT to the remote
/mcpresource. Access and refresh tokens are opaque, stored as digests in process memory, and scoped tohealth:read.Google OAuth authorizes the server to read the owner's Google Health data. Google credentials are stored in
.private/token.json; they are never issued to ChatGPT.
When a tool is called, the requested health data travels through the deployed MCP server and is returned to the connected ChatGPT conversation. Google OAuth credentials are not returned to ChatGPT.
Related MCP server: Google Health Fitbit MCP
MCP tools
The same six tools are registered for stdio and Streamable HTTP:
get_sleep(days: int = 7)get_steps(days: int = 7)get_heart_rate(days: int = 7)get_resting_heart_rate(days: int = 7)get_hrv(days: int = 7)get_health_summary(days: int = 7)
days accepts only 14, 7, 3, or 1; the default is 7. Tool results use a stable JSON envelope containing requested_days, available_days, data, missing_data, and diagnostics.
Requirements and installation
Python 3.12 or newer
A Google Cloud project with the required read-only Google Health scopes
A Desktop OAuth client for local CLI/stdio authorization, or a Web OAuth client for the remote bootstrap flow
python -m pip install -e ".[test]"OAuth client files, tokens, private data, generated reports, environment files, and logs are excluded by .gitignore. Never commit real credentials or health data.
Local CLI and stdio MCP
Place a Google Desktop OAuth client JSON in the project root using a name matched by client_secret_*.json, then authorize and synchronize:
python -m fitbit_health sync --days 7The local flow opens a temporary localhost callback and stores the resulting Google authorized-user credentials in .private/token.json.
Start the stdio MCP server with either command:
fitbit-health-mcp
python -m fitbit_health.mcp_serverGeneric Codex configuration:
[mcp_servers.fitbit_health]
command = "python"
args = ["-m", "fitbit_health.mcp_server"]
cwd = "/path/to/fitbit-health-mcp"Remote MCP and ChatGPT Connector
The remote entry point is:
python -m fitbit_health.http_mcp_serverIt exposes:
/mcp— authenticated Streamable HTTP MCP/.well-known/oauth-protected-resource— protected-resource metadata/.well-known/oauth-authorization-server— authorization-server metadata/oauth/authorizeand/oauth/token— MCP OAuth authorization code, PKCE, and refresh flow/auth/googleand/oauth2/callback— owner-only Google Web OAuth bootstrap
To connect from ChatGPT, deploy the server over HTTPS, configure the fixed public MCP client ID and ChatGPT redirect URI, then add the deployment's /mcp URL as a custom connector. ChatGPT discovers the OAuth metadata and six tools from that endpoint.
The current implementation uses an owner password at /oauth/authorize. Use a unique random value and do not reuse the Google bootstrap password.
Render deployment
render.yaml defines a single free-plan Python Web Service that installs the package and runs the remote MCP entry point. Render terminates TLS; the app binds to 0.0.0.0:$PORT and keeps the MCP resource at /mcp.
Create these Render Secret Files with the exact filenames shown:
Secret file | Purpose |
| Google Web OAuth client configuration |
| Optional seed for the runtime Google token |
Configure the following environment variables. Values shown in render.yaml are deployment defaults; every password, client identifier, redirect URI, and secret must be set for the actual deployment.
Variable | Purpose |
| Public HTTPS origin of the authorization server |
| Exact public |
| Pre-registered ChatGPT public client ID |
| Exact ChatGPT connector callback URI |
| Owner login for MCP authorization |
| Owner login for |
| Random signing key for the Google OAuth state session |
| Exact deployment |
| Google Web OAuth client Secret File path |
| Writable runtime Google token path |
| Read-only Google token seed path |
| Required legacy compatibility token in the current release |
After deployment, visit /auth/google over HTTPS and complete the owner-protected Google authorization flow. The callback writes the authorized-user credentials to the runtime token path.
Token lifecycle
Credential | Purpose | Current storage |
Google access/refresh token | Server access to Google Health | Writable |
Google token seed | Restore the runtime token when it is absent | Render Secret File |
MCP access/refresh token | ChatGPT access to | Digests in process memory |
Legacy static bearer | Backward-compatible direct | Render environment secret |
Important operational behavior:
Render's free service does not provide a persistent disk. A restart, cold start, or redeploy may discard the writable Google token and restore the older Secret File seed.
If Google issues a new refresh token, update the seed through Render's secret management. Otherwise a later rebuild may restore an obsolete token.
MCP access and refresh tokens are in memory. A process restart invalidates them and ChatGPT may need to authorize or reconnect the connector.
A Google OAuth project in Testing status may issue refresh tokens with a limited lifetime. Reauthorize through
/auth/googlewhen Google authorization is no longer available.
Security model
Google scopes are read-only.
MCP authorization codes are single-use, short-lived, and stored only as hashes.
MCP access and refresh tokens are opaque; refresh tokens rotate and token material is stored only as digests.
MCP access tokens are bound to the configured
/mcpresource andhealth:readscope.Google Web OAuth uses state validation, PKCE, a signed
Secure/HttpOnlysession cookie, and an owner-protected bootstrap route.Authentication failures are handled before health tools load Google credentials.
Tests use synthetic data and do not contain real health records.
Legacy bearer compatibility
The current remote startup still requires MCP_BEARER_TOKEN and accepts it alongside MCP OAuth access tokens. This is a long-lived, high-privilege compatibility path. For a production hardening release, add an explicit setting such as ENABLE_LEGACY_BEARER=false by default and enable the legacy verifier only when that flag is intentionally set. This release-preparation change does not alter or remove the legacy code path.
Known OAuth compliance gap
The authorization request validates the MCP resource, but the current token endpoint does not yet require and validate RFC 8707 resource on authorization-code and refresh-token requests. The project therefore does not claim full MCP 2025-11-25 authorization compliance. Address this in a separately scoped security change before treating the service as production hardened.
Known limitations
Single user and single tenant; this is a reference implementation, not a SaaS platform.
Read-only health access; no write operations are provided.
Only
14,7,3, and1day request windows are supported.MCP OAuth tokens are not durable across process restarts.
Render Free runtime files are ephemeral.
Google OAuth Testing policy may require periodic reauthorization.
Windows does not receive the same token-file permission hardening as POSIX systems.
Health data returned by a tool is sent through Render to the connected ChatGPT conversation.
No medical diagnosis or clinical reliability is claimed.
Tests
python -m pytest -q
python -m compileall -q src testsLicense
Released under the MIT License.
Available Tools
6 toolsget_health_summaryB
Get the existing multi-metric health analysis as structured JSON.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| data | Yes | |
| diagnostics | Yes | |
| missing_data | Yes | |
| available_days | Yes | |
| requested_days | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only says 'Get' and 'existing', which suggests a read-only operation but does not disclose whether it reflects real-time data, how recent it is, whether it triggers computation, or any rate limits or authentication needs. The term 'existing' hints that it may return stale data but leaves ambiguity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that focuses on the core action and output. It avoids unnecessary words and is perfectly sized for the tool's simplicity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple and has an output schema, which reduces the need to explain return values. However, the description does not clarify what 'multi-metric health analysis' includes or how it relates to the sibling tools. Given the potential for ambiguity, the description is minimally adequate but lacks enough context to be fully self-contained.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, and the description does not mention the 'days' parameter at all. The parameter has an enum of allowed values (14, 7, 3, 1) and a default of 7, but without any explanation in the description, the user must infer that 'days' controls the time window. This is insufficient given the low schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get') and the resource ('existing multi-metric health analysis'), and specifies the output format ('structured JSON'). This distinguishes it from sibling tools that focus on single metrics (e.g., heart rate, steps), making the purpose immediately clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'existing multi-metric health analysis' implies this tool is for retrieving a precomputed aggregate summary, as opposed to the single-metric siblings. However, there is no explicit when-to-use guidance, no mention of alternatives, and no exclusion criteria, leaving the usage context only vaguely implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_heart_rateA
Get daily average heart rate for the requested number of days.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| data | Yes | |
| diagnostics | Yes | |
| missing_data | Yes | |
| available_days | Yes | |
| requested_days | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It conveys a read operation ('Get') and mentions daily averaging, but does not disclose behavior around missing data, units, or permissions. Basic transparency is present but not deep.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence with no redundancy. It is front-loaded with the core purpose and is easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool with an output schema present, the description adequately covers the basic functionality. It lacks explicit sibling differentiation and detailed parameter behavior, but remains sufficient for straightforward use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description connects the 'days' parameter to 'requested number of days,' which adds context to the schema's bare parameter title. With 0% schema description coverage, this provides minimal meaning, but the enum values and default are left for the agent to infer from the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves daily average heart rate for a specified number of days, using a specific verb and resource. This distinguishes it from sibling tools like get_resting_heart_rate and get_hrv.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for retrieving daily average heart rate data but does not explicitly mention when to prefer this tool over get_resting_heart_rate or get_hrv, nor does it provide exclusions or alternative guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_hrvA
Get daily HRV RMSSD for the requested number of days.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| data | Yes | |
| diagnostics | Yes | |
| missing_data | Yes | |
| available_days | Yes | |
| requested_days | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description implies a non-destructive read operation via 'Get' but does not explicitly state side effects, dependencies, or data source. Since there are no annotations, the description carries full burden, but the simple read nature is sufficiently transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single clear sentence that gets to the point without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (one parameter) and the presence of an output schema, the description provides enough information for an agent to select and invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The only parameter 'days' is meaningfully explained as 'requested number of days' in the description, which is necessary because the schema provides no descriptions. However, allowed values are left to the enum.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Get'), resource ('daily HRV RMSSD'), and scope ('for the requested number of days'), which clearly distinguishes it from sibling tools like get_heart_rate or get_sleep.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like get_resting_heart_rate or get_heart_rate, nor any exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_resting_heart_rateA
Get daily resting heart rate for the requested number of days.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| data | Yes | |
| diagnostics | Yes | |
| missing_data | Yes | |
| available_days | Yes | |
| requested_days | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It does state the tool returns daily resting heart rate, which is a read-only behavior, but it does not describe response details, potential error conditions, or any limitations. For a simple getter this is acceptable, but it adds nothing beyond the tool's core function.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence, front-loaded with the verb and resource, and contains no wasted words. It is appropriately sized for the tool's simplicity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has one optional parameter and an output schema, so the description is nearly complete. However, it does not explain the difference from get_heart_rate, and it leaves ambiguity about the nature of 'daily' data (e.g., array of daily values or an average). For a simple tool, this is adequate but with clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It links 'days' to 'requested number of days', which adds some clarity, but it is vague about whether days means calendar days, business days, or how the range ends. The enum values provide concrete options but the description does not enrich them.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the action ('Get') and the resource ('daily resting heart rate'), and specifies the temporal scope ('for the requested number of days'). This distinguishes it from siblings like get_heart_rate and get_hrv, as it specifically targets resting heart rate over a period.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use when daily resting heart rate data is needed, but it does not explicitly mention when to prefer this over alternatives like get_heart_rate or get_hrv. No exclusions or alternative guidance are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_sleepB
Get normalized daily sleep data for the requested number of days.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| data | Yes | |
| diagnostics | Yes | |
| missing_data | Yes | |
| available_days | Yes | |
| requested_days | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It only states 'Get normalized daily sleep data' without disclosing any behavioral traits such as data source, normalization meaning, access requirements, or limitations. This is insufficient for a tool with zero annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence, front-loaded with the action verb 'Get.' It contains no fluff or redundancy, earning its place efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with one parameter and an output schema, so the description does not need to explain return values. However, it omits context about 'normalized' data and does not clarify temporal boundaries (e.g., whether the requested days include today). This is acceptable for basic invocation but leaves room for ambiguity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has zero description coverage for the 'days' parameter, and the description merely repeats 'for the requested number of days,' which adds nothing beyond the parameter name. It does not explain the meaning of each enum value or any nuances.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Get normalized daily sleep data' with a specific verb and resource. It distinguishes itself from sibling tools (heart rate, HRV, steps, etc.) by focusing on sleep data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention exclusions, prerequisites, or context for selecting this tool over siblings, leaving the agent without explicit usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_stepsA
Get normalized daily step counts for the requested number of days.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| data | Yes | |
| diagnostics | Yes | |
| missing_data | Yes | |
| available_days | Yes | |
| requested_days | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It mentions 'normalized' and 'for the requested number of days,' which provide some behavioral context, but it does not explain what normalization means, how the days are calculated, or any other side effects or constraints. This is minimal and leaves room for ambiguity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with no filler. It front-loads the main function (get normalized daily step counts) and includes the key parameter behavior (number of days) without any unnecessary detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (one parameter, no annotations), the description covers the essential purpose and parameter semantics. An output schema exists, so return values need not be described. However, it could briefly clarify 'normalized' or the exact time window, making it complete but not exhaustive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage for the 'days' parameter, so the description must compensate. It states that step counts are for the requested number of days, which directly explains the semantic of the parameter beyond the enum values. This adds meaning and helps the agent understand what to provide.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool retrieves normalized daily step counts for a specified number of days. The verb 'get' and resource 'daily step counts' are specific, and it distinguishes itself from sibling tools that focus on heart rate, sleep, or health summaries.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when step count data is needed, but it does not explicitly state when to use this tool over alternatives like get_health_summary. There is no guidance on exclusions or comparison with siblings, so the usage context is implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
6 tool updates
v0.1.0- First observed
get_health_summary - First observed
get_heart_rate - First observed
get_hrv - First observed
get_resting_heart_rate - First observed
get_sleep - First observed
get_steps
TDQS
Scored across 6 tools
Most tools are clearly distinct (sleep, steps, HRV, health summary), but get_resting_heart_rate and get_heart_rate could be confused without careful reading. Descriptions clarify the difference well, avoiding major ambiguity.
All tool names follow a consistent `get_` prefix with snake_case metric names (get_sleep, get_steps, get_hrv). The abbreviation in get_hrv is a minor deviation but does not break the overall pattern.
Six tools is a well-scoped set for a health data server, covering core metrics without being overwhelming. Each tool serves a distinct purpose, and the count feels appropriate for the domain.
The server covers common Fitbit metrics (heart rate, HRV, sleep, steps) and includes a summary endpoint. Missing metrics like activity or calories are notable but not essential given the focus on health summary, so minor gaps exist but are workable.
Maintenance
Related MCP Connectors
Read wearables and lab health data — sleep, activity, workouts, timeseries, lab tests and orders.
Collect Apple Health data from your wearables through the Context app and query it via MCP
Multi-tenant hosted MCP server for Oura Ring — 21 read-only tools, OAuth per user.
MCP server for Withings health data — sleep, activity, heart, and body metrics.
Related MCP Servers
- AlicenseBqualityAmaintenanceLocal-first MCP server that connects AI agents to your Fitbit activity, sleep, heart-rate, HRV, SpO2 and weight data.3385 npm4MIT
- AlicenseAqualityBmaintenanceAn MCP server that locally authenticates with Google Health API v4 and provides read-only access to Fitbit, Pixel Watch, and other health data for AI agents.2975 npm17MIT
- AlicenseNot gradedqualityBmaintenanceRead-only MCP access to Fitbit-synced health data through Google Health API v4. Provides tools for metrics, summaries, trends, and data quality without write or arbitrary HTTP operations.MIT
- FlicenseAqualityCmaintenanceProvides read-only access to your Garmin Connect health data, including sleep, HRV, body battery, stress, training readiness, and activities, through an MCP server.14-