mcp-google-health

reconcile_data_points

Read-only

Merges health data points from multiple sources into one de-duplicated stream and lists them with standard filters, so you can reconcile sleep records across Google Health sources.

Instructions

List data points merged across sources into one de-duplicated stream, as the Google Health app shows them. Takes the same filters as list_data_points. Use this for sleep across sources.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "dataType": "<dataType>"
    },
    "name": "reconcile_data_points"
  }
}

Input Schema

Name	Required	Description
`filter`	No	AIP-160 filter on the data type's time field.
`dataType`	Yes	Data type in kebab case, such as steps, sleep, or heart-rate. See list_data_types.
`pageSize`	No
`pageToken`	No	nextPageToken from the previous response.
`apiVersion`	No	API version. Defaults to v4, or v4beta for beta-only data types.
`dataSourceFamily`	No	Data sources: all-sources (default), google-wearables (Fitbit and Pixel devices only), google-sources (Google devices, manual logs, Health Connect), or self-sources (data this client wrote).

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Nov 10, 2026

TDQS

A3.6/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint and openWorldHint, so the safety profile is covered; the description adds genuinely non-obvious behavior by disclosing de-duplication, cross-source merging, and that the output mirrors what the Google Health app displays. It does not disclose merge precedence when sources conflict, which matters for interpreting results.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three short sentences, front-loaded with the distinctive merge/de-duplicate semantics before the routing hint. Little waste, though the trailing 'Use this for sleep across sources' reads as an afterthought rather than a structured usage rule.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only list tool with 83% schema coverage and no output schema, the description covers purpose, relationship to the sibling, and return shape at a high level. It leaves open how conflicts between sources are resolved and how the de-duplicated stream is ordered or paginated relative to the raw stream.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 83%, so per-parameter documentation is largely handled structurally and a 3 is the baseline. The description adds only a pointer to list_data_points' filter set and says nothing about how dataSourceFamily interacts with the merge behavior, which is the one parameter where extra meaning would be most valuable.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource plus an unusual scope qualifier: data points 'merged across sources into one de-duplicated stream.' That clearly separates it from list_data_points, which serves the raw per-source view, so an agent can pick between them without opening either schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives one concrete trigger ('Use this for sleep across sources') and signals its relationship to list_data_points via 'takes the same filters,' which implies reuse of that tool's parameter knowledge. It never states when NOT to use it (e.g., single-source reads) and the single stated use case is narrower than the tool's actual generality.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.