Click on "Install 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., "@renpho-mcp What was my latest body composition reading?"
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.
A read-only Model Context Protocol server for RENPHO Health smart scale data. Ask your MCP client for your latest reading, body composition history, or weight change over time.
Built in TypeScript with the official MCP SDK. The research report explains the language choice and documents the encrypted API contract.
Requires RENPHO Health, not the legacy RENPHO app. This project uses an unofficial API and is not affiliated with RENPHO. Automated tests use synthetic data; live account compatibility has not yet been verified.
Install Node.js 22 or newer, then run the public npm package:
npx -y @jegormeister/renpho-mcpThe server uses stdio and waits for an MCP client. Supply the environment variables described below through that client.
You can also download renpho-mcp.cjs and THIRD_PARTY_NOTICES.txt from the latest release. The bundle includes its dependencies and runs on macOS, Linux, and Windows with Node.
node /absolute/path/renpho-mcp.cjs --versionAlternatively, install the command globally:
pnpm add --global @jegormeister/renpho-mcp
renpho-mcp --versionPackages are published as @jegormeister/renpho-mcp. Downloadable bundles are hosted on GitHub.
Related MCP server: Renpho MCP Server
Create a STDIO server with command npx and arguments -y @jegormeister/renpho-mcp. Set RENPHO_EMAIL, RENPHO_PASSWORD, and optionally RENPHO_AREA_CODE in the server's environment. Node.js 22 or newer must be available inside the MetaMCP runtime.
For JSON import:
{
"mcpServers": {
"renpho": {
"type": "stdio",
"command": "npx",
"args": ["-y", "@jegormeister/renpho-mcp"],
"env": {
"RENPHO_EMAIL": "your-account-email",
"RENPHO_PASSWORD": "your-account-password",
"RENPHO_AREA_CODE": "US"
}
}
}
}Pin a version, such as @jegormeister/renpho-mcp@0.1.3, when you want upgrades to be explicit.
For clients using mcpServers JSON configuration:
{
"mcpServers": {
"renpho": {
"command": "node",
"args": ["/absolute/path/renpho-mcp.cjs"],
"env": {
"RENPHO_EMAIL": "your-account-email",
"RENPHO_PASSWORD": "your-account-password",
"RENPHO_AREA_CODE": "US"
}
}
}
}Use an absolute path to node too if your desktop client cannot find it. On Windows, escape backslashes in JSON or use forward slashes. Keep the client configuration private.
Environment variable | Meaning |
| Required RENPHO Health account email |
| Required account password |
| Two uppercase letters, defaults to |
The server connects only to https://cloud.renpho.com. Other regional hosts and every possible country code have not been verified. It reads variables from the process environment and does not load credential files.
Tool | Inputs | Result |
| None | Allowlisted account profile, excluding tokens and passwords |
| None | Scale tables and linked profile IDs |
| Optional | Latest owned reading with available body composition fields, or null |
| Optional | Newest-first history, pagination and scan metadata |
| Optional | Weight statistics for a complete scan, or a reason statistics are unavailable |
| None | Login and scale discovery status |
| None | Clear the local session so the next request logs in again |
Example requests:
"Show my latest RENPHO body composition reading."
"List the scale profiles linked to my account."
"Show my weight trend for the past 30 days."
"Get my measurements between August 1 and September 1, 2026."
History accepts ISO 8601 timestamps with a timezone, such as 2026-08-01T00:00:00Z. Both date bounds are inclusive. limit defaults to 100 and is capped at 500; offset defaults to 0. Trend days defaults to 30 and accepts 1–3650.
By default, readings must have a bUserId matching the logged-in account. The server never guesses the first linked person. If readings are missing, call get_scale_users, identify the intended profile, and pass its ID as scaleUserId. That explicitly selects measurements by subUserId. A linked family profile may contain another person's health data.
Measurements retain native RENPHO field names and values. timeStamp is Unix seconds. IDs are strings, including large IDs that JavaScript cannot represent exactly as numbers. Common fields include weight, weightUnit, bmi, bodyfat, water, muscle, bone, bmr, visfat, protein, bodyage, subfat, sinew, and heartRate. Missing metrics are not replaced with zero.
Public reverse-engineering sources map weight-unit codes 1 to kg, 2 to lb, 3 to stone/pounds, and 4 to stone. These mappings have not been confirmed against a live account here. A missing code means unspecified units; the account's display preference is not substituted. Some composition units disagree between existing clients. This server does not convert them or add medical classifications. See the source evidence.
The client scans every linked scale table forward, up to 50 requests of 200 readings per history scan. It does not trust reported counts or assume page ordering. It filters ownership and dates, deduplicates IDs, and sorts before applying the output limit. scanComplete: false means the cap prevented a complete scan; hasMore refers only to already-scanned matching rows. Latest and trend tools return errors on incomplete scans instead of treating partial history as authoritative. Concurrent uploads can still change the upstream data during pagination, so this is not a database snapshot.
Weight trends omit missing, zero, and negative weights and refuse to mix different unit codes. Trends also require a known scalar unit code: kg, lb, or stone. Missing codes, unknown codes, and mixed stone/pounds return an unavailable summary. Values remain in their native units. No measurement cache is used, so each read checks the cloud again. Login sessions stay in memory for at most 45 minutes; HTTP 401 triggers one reauthentication attempt.
Run health_check to distinguish connection failures from an empty measurement history.
For a rejected login, check account credentials, RENPHO Health app access, and the area code. Single sign-on without an account password may require setting a password in RENPHO first.
If discovery succeeds but no readings match, select a known linked profile explicitly. Sync the mobile app if Wi-Fi readings have not yet been assigned or uploaded.
refresh_session refreshes authentication only. It does not force a scale upload or bind unassigned readings.
Unexpected encrypted data or response formats may indicate an upstream API change. Error messages exclude raw server responses.
The process exposes stdio only. There is no listening HTTP server in production. Credentials go to RENPHO over HTTPS. Returned health data goes to your MCP client and may be processed by its model provider. The server does not write credentials or measurements to disk.
git clone https://github.com/jegork/renpho-mcp.git
cd renpho-mcp
pnpm install --frozen-lockfile
pnpm checkpnpm check runs strict type checking, encrypted HTTP integration tests against a local synthetic server, MCP tool tests, the bundle build, and a stdio smoke test. No live RENPHO credentials are needed. CI runs Node 22 and 24 on Linux and Node 24 on macOS and Windows.
The API client lives in src/renpho.ts, encryption and lossless JSON parsing in src/protocol.ts, and MCP tool registration in src/server.ts. This keeps upstream API changes separate from MCP wiring.
Update package.json's version and docs/release-notes.md, run pnpm install --lockfile-only and pnpm check, then commit and push. Create and push a matching tag:
git tag v0.1.4
git push origin v0.1.4The release workflow checks all CI platforms, verifies that the tag matches the package version, builds and smoke-tests the bundle, publishes to npm with provenance, and publishes a GitHub release. Assets include the bundle, package archive, third-party notices, documentation, and SHA-256 checksums.
npm publishing uses GitHub Actions OIDC. The package's trusted publisher must allow direct publishing from repository jegork/renpho-mcp, workflow release.yml, with no environment restriction. The workflow has id-token: write; no npm token or RENPHO credentials are stored in GitHub. Connecting a GitHub profile to an npm account is separate from granting this package-specific permission. See npm's trusted publishing documentation.
MIT. API behavior was studied in StartupBros-com/renpho-mcp-server, with its MIT attribution retained in NOTICE. Additional protocol evidence is linked in docs/research.md. Bundled dependency licenses are included in each release.
Read the logged-in RENPHO Health account profile. Credentials and tokens are excluded.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark readOnlyHint, idempotentHint, and destructiveHint=false. The description adds useful context beyond annotations by stating credentials and tokens are excluded from the profile, which informs agents about data scope. No contradiction.
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?
Two short sentences, front-loaded with the action and resource. The exclusion note earns its place by adding data-scope clarity without fluff.
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 zero-parameter, read-only tool with annotations covering safety and idempotence, the description covers what it returns (profile) and what it excludes (credentials/tokens). No output schema exists, but the tool's simplicity means the agent can call it safely and inspect the result. Complete enough.
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?
Tool has zero parameters and schema description coverage is 100%. Description adds no parameter details because none are needed; baseline for zero parameters is 4.
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?
Description states specific verb 'Read' and resource 'logged-in RENPHO Health account profile', and clarifies that credentials/tokens are excluded. This clearly distinguishes it from siblings like get_scale_users (different resource) and refresh_session (session/token operation).
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?
Provides clear context for when to use: retrieving the current logged-in account profile. It does not explicitly name alternatives or state when not to use it, but the exclusion of credentials/tokens signals it is not for auth-sensitive data. Clear context without explicit exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Read the latest owned measurement and all available body composition fields. Returns null when none match. Refuses to claim latest if the scan limit is reached.
| Name | Required | Description | Default |
|---|---|---|---|
| scaleUserId | No | Optional linked scale profile ID from get_scale_users. Omit to match the logged-in account by bUserId. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark this as read-only, idempotent, open-world, and non-destructive. The description adds useful non-obvious behaviors: it returns null when no measurement matches and refuses to claim latest if a scan limit is reached. The scan-limit wording is somewhat vague, but it still discloses an important edge case beyond what annotations convey.
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?
Three short sentences with no filler. The core purpose is front-loaded, and each subsequent sentence adds distinct behavioral information: null return and scan-limit refusal. Every sentence earns its place.
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 tool with one optional parameter, strong safety annotations, and no output schema, the description covers the main behavior, the null case, and an important limit-related edge case. The only shortfall is that 'Refuses to claim latest if the scan limit is reached' is ambiguous about what the tool actually returns or does in that scenario.
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 coverage is 100%, and the schema already fully documents the single optional parameter, including its source (get_scale_users) and default behavior when omitted. The description adds no additional parameter-level meaning, so the baseline score of 3 applies.
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 action ('Read'), a specific resource ('the latest owned measurement'), and what fields are included ('all available body composition fields'). It clearly distinguishes itself from siblings like get_measurements and get_weight_trend by emphasizing 'latest'.
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 its use case through the word 'latest', but it does not explicitly say when to prefer this over get_measurements or get_weight_trend, nor does it state any exclusions. The parameter cross-reference to get_scale_users provides some context but not tool-selection guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Read body composition history, newest first, with native RENPHO fields and unit codes. Filters ownership before pagination. Check scanComplete before treating results as complete. No health classifications are computed.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| since | No | Inclusive start, ISO 8601 with timezone. | |
| until | No | Inclusive end, ISO 8601 with timezone. | |
| offset | No | ||
| scaleUserId | No | Optional linked scale profile ID from get_scale_users. Omit to match the logged-in account by bUserId. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations, the description discloses meaningful behavior: results are sorted newest-first, ownership filtering occurs before pagination, scanComplete must be checked before treating results as complete, and no health classifications are computed. These are specific, non-obvious traits that materially affect how an agent should interpret the response.
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?
Three tightly written sentences, each carrying distinct information: what the tool reads, ordering/format, and crucial interpretation caveats. Nothing is redundant, and the most important action is front-loaded.
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 no-output-schema read tool, the description covers the key operational facts: ordering, raw field format, ownership filtering, completeness checks, and absence of health classifications. It does not enumerate the exact RENPHO fields returned, but the description provides enough context for an agent to call and interpret the tool correctly without an output schema.
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 already documents since, until, and scaleUserId well. The description adds some relevant context by mentioning ownership filtering and pagination, which maps to scaleUserId and limit/offset. However, it does not clarify limit or offset beyond their names and constraints, and the schema coverage is only 60%, so the description only partially compensates for the undocumented parameters.
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 opens with a specific verb and resource: 'Read body composition history' and immediately clarifies the sort order ('newest first'). It also distinguishes the raw output ('native RENPHO fields and unit codes') from derived interpretations, making the tool's purpose unmistakable against siblings like get_latest_measurement and get_weight_trend.
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 gives clear context for when this tool applies: retrieving historical body composition data in reverse chronological order. It does not explicitly name alternatives or exclusion conditions, but the 'history' framing plus the caveat about scanComplete gives an agent enough context to choose it over get_latest_measurement or get_weight_trend.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List linked scale tables and profile IDs. Use a returned userIds value as scaleUserId when selecting a family profile.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior, so the safety profile is covered. The description adds value by revealing the output semantics: linked scale tables, profile IDs, and the userIds value's role as scaleUserId. It does not contradict annotations, though it omits edge cases like no linked users or authentication requirements.
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 two tight sentences with no filler. The first sentence front-loads the core action and resource, and the second sentence adds a necessary integration hint about the returned userIds value. Every part earns its place.
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?
There is no output schema, so the description carries the burden of explaining return values; it does state the output consists of linked scale tables and profile IDs and explains how to use userIds. For a zero-parameter list tool with strong annotations, this is sufficient, though it could note behavior when no linked tables exist or explicitly reference sibling tools.
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 input schema has zero parameters, so there is no parameter documentation burden on the description. The schema description coverage is trivially 100%, and the 0-parameter baseline of 4 applies.
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 uses the specific verb 'List' and identifies the resource as 'linked scale tables and profile IDs,' clearly stating the tool's function. It inherently distinguishes itself from sibling tools like get_measurements or get_current_user by focusing on scale user/family profile linkage. No ambiguity remains about what this tool returns.
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 clear downstream usage guidance: use the returned userIds value as scaleUserId when selecting a family profile. It does not explicitly compare this tool against alternatives or state when not to use it, but the context of selecting a family profile is sufficient for an agent to select this tool over unrelated siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Summarize positive weight readings over the past N days in native kg, lb, or stone. Rejects incomplete scans and inconsistent, missing, or mixed stone/pounds unit codes. This is arithmetic, not medical advice.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | ||
| scaleUserId | No | Optional linked scale profile ID from get_scale_users. Omit to match the logged-in account by bUserId. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark it read-only and idempotent; the description adds meaningful behavioral detail beyond that: it only considers positive readings, rejects incomplete scans, and rejects inconsistent/missing/mixed unit codes. These details materially affect expectations.
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?
Three short sentences, front-loaded with the core purpose. The rejection behavior and disclaimer each add necessary context 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?
The description is sufficient for a simple read-only summary tool with rich annotations. The main gap is that no output schema exists and the description does not specify the exact shape of the summary, though 'Summarize' and the tool name give reasonable grounding.
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 describes scaleUserId well and the description maps 'N days' to the days parameter. The description also adds unit semantics (native kg/lb/stone) that are not present in the schema. It does not deeply elaborate on days, but the name and bounds make it clear.
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 ('Summarize'), a specific resource ('positive weight readings over the past N days'), and supported units ('kg, lb, or stone'). It is clearly distinct from raw-data siblings like get_measurements and get_latest_measurement.
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 purpose is stated explicitly enough that an agent knows this is for weight summaries/trends rather than raw measurements. It does not name alternative tools, but the 'This is arithmetic, not medical advice' caveat clarifies a non-usage boundary.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Verify RENPHO login and scale discovery without returning measurements.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds behavioral context beyond the readOnly and idempotent annotations by stating what the tool checks (login and scale discovery) and what it does not return (measurements). This helps the agent predict the tool's effect and output scope, though it does not mention whether an error is raised on failed verification.
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 sentence with no filler. It front-loads the action and resource, then adds the key exclusion about measurements. Every word contributes meaning.
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 zero-parameter, non-destructive health-check tool, the description is largely sufficient. It explains the purpose and the absence of measurement returns, but it does not specify what the tool actually returns (e.g., boolean, status message). Given the simplicity of the tool, this is a minor gap.
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 tool has no parameters, and the schema fully documents this by defining an empty properties object. The description reinforces that no input is needed, which satisfies the baseline for zero-parameter tools.
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 uses the specific verb 'Verify' and names the resource: RENPHO login and scale discovery. It explicitly states that measurements are not returned, which distinguishes it clearly from the measurement-focused sibling tools.
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 intended use case is clear: verify connectivity and authentication setup before taking measurements. Although it does not explicitly name alternatives or state when not to use it, the 'without returning measurements' qualifier strongly implies this is a preflight check rather than a data retrieval tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Discard the in-memory login session. The next data request authenticates again. Does not trigger scale uploads or change RENPHO data.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description states 'Discard the in-memory login session,' which is a state-changing action, while annotations declare readOnlyHint=true. This is a direct contradiction. Although the description adds helpful non-effect details, the contradiction undermines trust in behavioral disclosure.
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?
Two sentences with no filler: the first names the action and its immediate consequence, the second clarifies what it does not affect. The most critical information is front-loaded.
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 zero-parameter tool, the description explains purpose, the next-request effect, and non-effects, which is largely complete. However, the annotation contradiction leaves the operation's true semantics ambiguous, so the overall context is not fully reliable.
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 tool has zero parameters and schema description coverage is 100%, so there is no parameter information missing. The description reasonably adds no parameter details because none exist.
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 uses a specific verb ('Discard') and resource ('in-memory login session'), making the action unambiguous. It also differs clearly from the sibling tools, which are all data-retrieval or health-check operations, and adds what the tool does not do.
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 'The next data request authenticates again' gives clear context for when the effect occurs, but it does not explicitly state when to prefer this tool over alternatives or provide exclusions. The sibling list makes the niche apparent, so guidance is adequate but not fully explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.1.3get_current_userget_latest_measurementget_measurementsget_scale_usersget_weight_trendhealth_checkrefresh_sessionTools are mostly distinct: get_measurements vs get_latest_measurement vs get_weight_trend could be confused, but their descriptions clearly differentiate history, latest, and summary. Other tools (user, scale users, session, health) are unambiguous. Minor overlap in measurement-related tools.
Naming is predominantly verb_noun with 'get_' prefix (get_current_user, get_scale_users, get_measurements, etc.), but 'refresh_session' and 'health_check' deviate slightly from the 'get_' pattern while still following verb_noun or noun style. Overall consistent snake_case and readable.
7 tools is well-scoped for a health scale integration, covering authentication, user info, scale discovery, health checks, and measurement retrieval. Each tool serves a clear purpose without redundancy or bloat.
The tool surface covers the core lifecycle for a read-only scale API: session management, user profile, device discovery, measurements history, latest measurement, and trend analysis. No obvious gaps for typical use cases; missing write operations are not expected for this domain.
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If you are the server author, to access and configure the admin panel.
Read wearables and lab health data — sleep, activity, workouts, timeseries, lab tests and orders.
Read-only access to InfluSense influencer discovery, ratings, watchlists, and reports via MCP.
MCP server for Withings health data — sleep, activity, heart, and body metrics.
Read-only access to your PumpX workout log: sessions, lift history, PRs, routines, measurements.
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