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Dutify

hub_reprocess_recording

Destructive

Re-run the FULL pipeline on a recording: transcription + summary + key points + action items. Slower and more expensive than hub_regenerate_summary. Use when a fresh transcription would meaningfully improve the result (different language detection, speaker diarization fix, etc.). Returns the updated recording DTO. Requires recordings:write scope.

Input Schema

NameRequiredDescriptionDefault
accountNoMulti-account auth only: name of the account from X-Dutify-Accounts to use for this call. Ignored in single-account mode (X-API-Key header). When multiple accounts are configured and this is omitted, the call fails with ACCOUNT_REQUIRED listing the available names. list_accounts never requires a selection.
workspaceNoWorkspace identifier or exact name for this call. Overrides the configured workspace. For personal keys, discover candidates with list_workspaces and select explicitly. Does not change other calls or expand a workspace key's access.
regenerateNoWhether to regenerate downstream artifacts (key points, action items) after the new transcription. Default true on the backend.
recordingIdYesRecording ID.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Adds valuable context beyond the annotations: cost/latency tradeoff, the recordings:write scope requirement, and that it returns an updated recording DTO. Annotations already declare destructiveHint=true and idempotentHint=false, but the description does not explicitly warn that the prior transcription and derived artifacts are replaced — a meaningful gap for a destructive re-run.

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 tight sentences, front-loading what the tool does and immediately routing to the alternative. Every sentence carries distinct information (scope, cost, selection criteria, return/scope requirements) with no filler.

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?

No output schema exists, but the description states it returns the updated recording DTO plus the required scope. For a 4-parameter mutation with full schema coverage and rich annotations, nothing an agent needs in order to invoke 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 `account`, `workspace`, `regenerate`, and `recordingId` are already fully documented, which sets the baseline at 3. The description implies the pipeline stages affected but adds no syntax or format detail beyond 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?

States a specific verb (re-run) and the exact resource scope (FULL pipeline: transcription + summary + key points + action items) on a recording. It explicitly contrasts with the sibling `hub_regenerate_summary`, so an agent can distinguish 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 Guidelines5/5

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

Names the cheaper alternative (`hub_regenerate_summary`) and gives the precise condition for choosing this tool instead: when a fresh transcription would meaningfully improve results, with concrete triggers (language detection, diarization fixes). This is explicit when/when-not 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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