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List decisions

list_decisions
Read-only

List decision log newest first, covering accepted, rejected, edited, deleted, and restored fixes or clips. Filter by kind or project and paginate to review past choices, including deleted projects.

Instructions

Your decision log: what you accepted, rejected, cut, corrected, edited, deleted or restored, newest first, kept after a project is deleted so Reelsaw learns from it. Returns { summary: { enabled, total, projects, byKind }, items: [{ id, kind, explicit, projectId, clipId, before, after, context, models, source, createdAt }], nextCursor, total }. kinds are fix.* (transcript fixes) and clip.*; clip.replaced (explicit false) is a pick a re-analyze replaced.

Input Schema

NameRequiredDescriptionDefault
kindNoOnly this kind of decision
limitNoPage size (1-200, default 50)
cursorNonextCursor from the previous page
projectIdNoOnly decisions about this project (it may already be deleted)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.28.1

TDQS

A3.9/5.0
Behavior4/5

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

Annotations only declare readOnlyHint=true, while the description adds meaningful behavior: decisions are returned newest first, are retained after project deletion so the system can learn from them, and include summary/item/pagination fields. It also explains the special meaning of clip.replaced and explicit=false. These details go well beyond the annotation, though permission or rate-limit behavior is not discussed.

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

Conciseness4/5

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

The description is front-loaded with the core purpose and then moves efficiently into return shape and kind semantics. It is fairly dense but each part contributes useful information, with only mild run-on density in the first sentence.

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 read-only list tool with no output schema, the description supplies the return structure, ordering, pagination field, retained-after-deletion behavior, and kind semantics. Combined with full schema coverage and readOnlyHint, it gives an agent enough context to call and interpret the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is already 100%, so the baseline is 3, but the description adds useful semantic grouping for kind values as fix.* transcript fixes versus clip.* decisions, and clarifies the meaning of clip.replaced with explicit=false. It also notes that projectId may refer to an already-deleted project, reinforcing the schema's wording.

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?

The description clearly states a specific verb and resource: it lists the user's decision log, including accepted/rejected/cut/corrected/edited/deleted/restored decisions, newest first. It is specific enough to distinguish this from generic listing tools, though it does not explicitly name sibling tools such as export_decisions or forget_decisions.

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?

Usage is implied as viewing the decision history, but the description gives no explicit when-to-use guidance and does not contrast this tool with nearby alternatives like export_decisions, forget_decisions, or set_learning. An agent can infer the purpose, but routing between decision-related siblings is left to the agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.