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aaronarnold2

concept2-mcp-server

by aaronarnold2

Bulk Create Concept2 Workout Results

concept2_create_results_bulk

Log multiple Concept2 workout results to the Logbook in one request. Specify type, date, distance, time, and weight class for each result.

Instructions

Log multiple workout results to the Concept2 Logbook in a single request.

Each result in the array must include the same required fields as concept2_create_result: type, date, distance, time, weight_class.

Args:

  • user_id: User ID or "me" (default: "me")

  • results: Array of workout objects, each with required fields:

    • type: 'rower', 'skierg', or 'bikeerg'

    • date: YYYY-MM-DD

    • distance: meters (number)

    • time: tenths of a second (integer)

    • weight_class: 'H' or 'L' Optional per result: timezone, workout_type, stroke_rate, heart_rate, calories_total, drag_factor, comments, privacy.

  • response_format: 'markdown' or 'json'

Returns the created results with their assigned IDs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultsYesArray of workout results to create (max 100)
user_idNoUser ID or "me" for the authenticated userme
response_formatNoOutput format: 'markdown' for human-readable or 'json' for machine-readablemarkdown
Behavior3/5

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

Annotations already mark this as a non-read-only, non-idempotent operation; the description adds that it returns created results with IDs and supports markdown/json output. It does not discuss batch failure handling or rate limits, so it remains adequate but not deeply transparent.

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 front-loaded with a one-sentence summary and uses a clean Args bullet structure. It repeats some schema details but remains readable and not bloated.

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?

For a bulk create tool, it covers purpose, per-item fields, optional fields, and return behavior; no output schema exists, so the return description helps. It omits batch limits/error semantics, but schema's maxItems and annotations cover some constraints.

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

Parameters4/5

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

The schema already describes all parameters (100% coverage), but the description adds crucial units ('tenths of a second', 'meters') and clarifies the default for user_id and response_format. This goes beyond field types/enums in the schema.

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 opens with a clear, specific verb ('Log') and resource ('multiple workout results to the Concept2 Logbook'), and the title/name emphasize bulk creation. This unambiguously distinguishes it from the sibling concept2_create_result.

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?

It explicitly frames the tool as a bulk operation ('in a single request') and references concept2_create_result for required fields, implying the single-result alternative. However, it does not explicitly state 'use for multiple, use concept2_create_result for one' or mention exclusions.

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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