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mutation_log_export

Export filtered mutation history as CSV or markdown reports for readable audit trails, using local files with zero API calls.

Instructions

Render the JSONL mutation history as a CSV or markdown report, date/uri-filtered — an audit trail you can actually read. Local file only, zero API calls.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toNoEnd date (inclusive), YYYY-MM-DD
fromNoStart date (inclusive), YYYY-MM-DD
formatNomarkdown
max_resultsNoMax items to return (default: SPOTIFY_MCP_MAX_ITEMS env or 50)
response_formatNo'concise' = human prose, 'detailed' = more fields in prose, 'json' = raw API objectconcise

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.26.1

TDQS

A3.9/5.0
Behavior4/5

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

Annotations are absent, so the description carries the full burden, and it delivers key behavioral facts: it is non-destructive ('Render'), reads a local JSONL file only, and makes 'zero API calls' — implying no auth, no rate limits, and no network failure modes. It does not explain how it relates to the undo/backup mutation system, but the core safety and execution profile is disclosed.

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 front-loaded sentences totaling roughly 29 words. The first defines what and how, the second defines scope and side-effect profile; no filler and no repetition of schema content.

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?

For a simple read/report tool with no annotations and no output schema, the description covers source, formats, filters, and local-only behavior. It falls short on the unbacked 'uri-filtered' claim and on how the report output maps to the response_format parameter, leaving some agent-facing ambiguity.

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 80% with solid descriptions for from/to, format, max_results, and response_format, so the schema already carries the parameter meaning. The description's 'date/uri-filtered' confirms the date range but claims a uri filter with no corresponding parameter in the schema, and it doesn't clarify how the format (csv/markdown) and response_format (concise/detailed/json) enums interact.

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 and resource — 'Render the JSONL mutation history' — plus output formats (CSV or markdown) and filters (date/uri). 'Local file only, zero API calls' distinguishes it from the many Spotify-API-backed export/search siblings, and 'audit trail' positions it beside undo/verify tools rather than competing with them.

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 description implies when this applies ('Local file only, zero API calls' → offline audit of the mutation log), but names no alternatives or exclusions. With a large sibling set including history_search, export_listening_history, undo_mutation, and receipt_lookup, explicit routing ('use X when you want Y') would meaningfully help an agent choose correctly.

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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