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show_runtime_stats

Calculate total and average episode runtime and release cadence for a Spotify show, using episode data to summarize its length and publishing frequency.

Instructions

[local-compute] Runtime profile for one show: total and average episode runtime plus release cadence (days between episodes), built from a paged /shows/{id}/episodes walk. Quota: 🟡 one paginated walk (typically several API calls).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
marketNoISO 3166-1 alpha-2 market code (e.g. 'US'); omit for 'from_token' behaviour
show_idYesSpotify show ID
max_episodesNoCap on episodes analyzed. Default: SPOTIFY_MCP_FETCH_ALL_CAP
response_formatNo'concise' = human prose, 'detailed' = more fields in prose, 'json' = raw API objectconcise
Behavior4/5

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

With zero annotations the description carries the full burden and uses it well: it discloses local compute ([ocal-compute]), the paged /shows/{id}/episodes walk mechanism, and the quota cost (typically several API calls). It stops short of edge-case behavior such as empty shows or unordered walks, but the mechanism and cost profile are genuinely useful behavioral facts beyond a bare stat-returning statement.

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?

Two sentences with zero filler. The deliverable and scope are front-loaded, and the quota sentence earns its place by giving the agent a cost signal for planning. The compact [ocal-compute] prefix is high-value context in few characters.

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

Completeness3/5

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

With no output schema and no annotations, the description does the heavy lifting reasonably: it specifies the computed fields, the traversal mechanism, and cost. Gaps remain around sibling differentiation (episode_runtime_report could be confused) and edge cases such as shows with no episodes or how the walk orders episodes — relevant at this sibling scale.

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 baseline 3 applies — all four parameters (market, show_id, max_episodes, response_format) are already documented with defaults and enum meanings. The description adds only mild context by tying the paged walk to why max_episodes exists; no parameter-level detail is missing.

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 deliverable — a runtime profile for one show with total/average episode runtime plus release cadence — anchored in a concrete mechanism (paged /shows/{id}/episodes walk). The 'one show' scope and computed-stats nature clearly distinguish it from raw-listing siblings like list_show_episodes and get_show_details.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

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

The 'for one show' scoping and quota note ( one paginated walk) give an agent useful cost and scope signals, but no alternatives are named and there is no when-not-to-use guidance. With near-neighbor siblings like episode_runtime_report, show_episode_timeline, and shows_release_calendar, explicit routing to those would materially improve selection accuracy.

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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