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Libsyn: Update episode

libsyn_update_episode

Libsyn: Change the title, show notes or details of a draft or published episode; the RSS feed updates. Changes the fields you pass and leaves the rest. On a published episode the RSS feed updates, so podcast apps show the change. Read the current text with get_episode first if you add to it. 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
fieldsYesAny of title, description, season_number, episode_number, episode_type, rating, author, tags.
episode_idYesEpisode ID from list_episodes.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior5/5

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

Annotations already declare mutation/open-world/non-destructive, and the description goes well beyond them: partial update semantics ('Changes the fields you pass and leaves the rest'), RSS feed propagation to podcast apps, a read-before-write recommendation, per-call confirmation defaults with an opt-out, and the required API key scope. This is rich, load-bearing 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.

Conciseness3/5

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

Front-loaded and easy to scan, but wasteful: the RSS-update consequence is stated twice ('the RSS feed updates' in sentence one and again in sentence three). The confirmation warning is also restated, so a few sentences do not fully earn their place.

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 mutation tool with no output schema, the description covers the important gaps: partial-update behavior, external side effects, auth requirements, and human-confirmation expectations. It is nearly complete, with only the single-vs-bulk scope relative to bulk_edit_episodes left implicit.

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 coverage is 100% (both episode_id and fields are documented, including the full field list), so the schema does the heavy lifting. The description's examples (title, show notes) map to the fields parameter but add no syntax or format detail beyond the schema, making the baseline 3 appropriate.

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?

Names a specific verb (change) and resource (title, show notes, details of a draft or published episode), which is more precise than the title alone. It does not explicitly distinguish itself from the obvious sibling libsyn_bulk_edit_episodes (which handles multiple episodes), so it falls short of the 'distinguishes from siblings' bar for a 5.

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 clear conditional guidance: 'Read the current text with get_episode first if you add to it,' plus confirmation behavior and the requirement to confirm with the user before each call. It stops short of an explicit single-vs-multi-episode routing rule (bulk_edit_episodes) that a 5 would require.

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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