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Everfit: Log body metric

everfit_log_body_metric

Everfit: Add a result for one of a client's tracked metrics. Saves a result for one metric enabled for the client, with a value and the date and time of the entry. The client sees it in their metrics and charts. The metric must already be enabled for the client in Everfit. By default you confirm each call; you can switch that off for this tool. Reaches other people: confirm with the user before each call. Needs the user's Cotlet API key with access to this app.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
valueYesValue in the metric's unit.
metricYesMetric name, e.g. Weight.
client_idYesClient ID from list_clients.
recorded_atNoISO 8601. Default: now.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior5/5

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

Beyond the annotations (readOnly=false, destructive=false, openWorld=true), it discloses that the client sees the value in their metrics and charts, that confirmation is on by default and can be toggled per-tool, that it reaches other people, and that a specific API key scope is required. This is unusually rich behavioral context.

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 core action and constraints are front-loaded and each sentence carries useful information. The trailing confirmation/API-key policy sentences are relevant but slightly boilerplate-heavy, keeping it from a perfect score.

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

Completeness5/5

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

For a 4-param write tool with no output schema, it covers purpose, prerequisite, side effects (client visibility), confirmation policy, and auth scope. Nothing an agent needs to call it correctly is missing.

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 description coverage is 100%, so the schema already documents value, metric, client_id, and recorded_at. The description reinforces that value pairs with the metric's unit and time of entry but adds no new syntax or format detail beyond the schema, so the baseline of 3 applies.

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?

States a specific verb (add/log a result) and resource (one of a client's tracked metrics) with the value and timestamp captured. An agent can distinguish this write operation from the read-oriented sibling everfit_get_body_metrics 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 Guidelines4/5

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

Provides a clear prerequisite (the metric must already be enabled for the client) and confirmation/authorization context. It does not explicitly name the sibling reader or state when not to use this tool, so it falls short of full routing 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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