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Update a workout template

update_training_frame
Destructive

Update a library workout template. Read it first with get_training_frame, apply your changes in reasoning, then send the patch — nodes kept from the read keep their "id" (the server preserves everything the web editor set on them; a node without id is created new). If no schema field expresses a detail of the coach's intent, put it in the comment of the relevant node — never approximate it into another field, never drop it. If a request expands to more than 3 write operations, list the computed dates/targets and get explicit confirmation before writing.

Input Schema

NameRequiredDescriptionDefault
idYesPublic id of the event to update.
patchYesRFC 7396 merge-patch of the envelope: omitted fields stay untouched, null clears a field, provided values replace it. "workout", when provided, replaces the whole structured content (full-replace): re-emit the entire array, preserving the "id" of every node you keep; null removes it and zeroes the duration and distance it synced.
revisionNoRevision token from your last get_* read — optional but strongly recommended. On mismatch the server rejects with the fresh state: re-read, re-apply your change, re-emit. It only detects concurrent edits — no version history, no server-side undo: to be able to restore, keep the JSON you read before modifying and resubmit it without node ids.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations flag destructiveHint=true and readOnlyHint=false, and the description correctly reinforces that instead of contradicting it, adding that kept nodes retain their id and that the server preserves web-editor settings on them. The id-omission-equals-new-node rule is real behavioral disclosure the annotations cannot carry. It stops short of stating what happens to nodes dropped from the re-emitted array.

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

Conciseness5/5

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

Four sentences, purpose front-loaded, then workflow, then the id rule, then two guardrails. Every sentence carries either a procedure or a constraint, and nothing repeats the schema verbatim.

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 mutation tool with a very deep nested schema and no output schema, the description covers the parts that matter operationally: read-first, id preservation, the comment escape hatch for intent the schema cannot express, and a confirmation gate for large writes. Required category, revision token and full-replace semantics of `workout` are all carried by the schema.

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 all three parameters and the deep nested node types are already documented in the schema — baseline 3 applies. The description adds one genuinely useful overlay (id preservation vs. new-node creation, which the schema also repeats per node type) but no additional semantics beyond that.

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 — "Update a library workout template" — which separates it from create_training_frame and list_training_frames. It also names get_training_frame as the mandated read step, but never distinguishes itself from apply_training_frame or update_planned_training, so an agent must infer which update path applies.

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?

Gives an explicit read-modify-write sequence: read with get_training_frame, compute changes in reasoning, then send the patch. Also adds a threshold rule (more than 3 write operations requires listing computed dates/targets and getting confirmation). No stated when-not or alternative-tool routing.

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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