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bosch-flow-mcp

by partymola
OverviewSchemaSourceRelated ServersScoreDiscussionsAdmin
Python
Remote

bosch_battery_trends

Analyze Bosch eBike battery health over time with per-period averages for charge cycles, energy delivered, and remaining capacity, revealing capacity degradation trends.

Instructions

Analyse battery health trends for your Bosch eBike over time.

Computes per-period averages and deltas for:

  • Charge cycle count (total, rate of accumulation)

  • Average battery level at time of sync

  • Lifetime energy delivered (total kWh ever pushed through the battery)

  • Remaining energy trend (indicates capacity degradation over time)

A declining remaining_energy_wh at a constant charge level indicates the battery capacity is degrading. Compare early and recent snapshots for a long-term health picture.

Args: bike_id: Optional bike UUID. If omitted, includes all bikes. period: Aggregation period. Options: "weekly", "monthly" (default), "quarterly". start_date: Start date (YYYY-MM-DD, YYYY-MM, Nd, or None for all data). end_date: End date. Default: today.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
periodNomonthly
bike_idNo
end_dateNo
start_dateNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / period / enum
      Added value: +[
+  "weekly",
+  "monthly",
+  "quarterly"
+]
  2. Addedv0.2.3
  3. Removedv0.2.2
  4. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

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

Even without annotations, the description discloses meaningful behavior: it computes per-period averages and deltas, and it explains how to interpret a declining remaining_energy_wh as capacity degradation. It does not discuss auth, rate limits, or side effects, but the analytic and presumably read-only nature of the tool is communicated effectively.

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?

The description is well-structured, starting with purpose, then behavioral interpretation, then parameter details. It is longer than the minimum, but every section adds value, especially given the lack of annotations and schema descriptions.

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

Completeness4/5

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

The description covers the tool's purpose, interpretation guidance, and all parameter semantics, and an output schema exists to define the return shape. Minor ambiguities like the meaning of 'Nd' in start_date remain, but an agent has enough context to invoke the tool correctly.

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

Parameters5/5

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

With schema description coverage at 0%, the description fully compensates by explaining every parameter: bike_id's null behavior, period's valid options, start_date's accepted formats, and end_date's default of today. It even notes that None means all data, which is not available from the schema alone.

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

Purpose5/5

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

The description uses a specific verb ('Analyse') and clearly identifies the resource: battery health trends over time, broken out into concrete metrics like charge cycle count and capacity degradation. This also distinguishes it from snapshot tools like bosch_get_capacity or bosch_get_soc, which likely return current values rather than per-period aggregates.

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

Usage Guidelines4/5

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

The description clearly frames when this tool is appropriate: for long-term health analysis and comparing early vs recent snapshots to detect capacity degradation. It does not explicitly name sibling tools to avoid or state when not to use it, but the trend-focused wording provides enough contextual guidance.

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

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