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by ottonic

Recall Decisions

recall_decisions
Read-onlyIdempotent

Retrieve decisions saved for an artifact, newest first, marking each stale when made against a different revision than the one supplied. Filter by subject, status, or entity to recover prior conclusions.

Instructions

Retrieve decisions saved for an artifact, newest first, each marked stale when it was made against a different revision than the one supplied. Pass the current ArtifactRef from Geometry inspect. Filter by subject text, status, or an entity that appears in the basis. An entity filter matches only decisions made against the supplied revision, because a face ID is scoped to the revision that produced it: a decision about face:3 in an older revision is not a decision about face:3 now. Use this when a revision reappears, to recover what was already concluded instead of reaching it again.

Input Schema

NameRequiredDescriptionDefault
limitNoMax decisions. Default 100.
offsetNoSkip N decisions for pagination.
statusNoOnly decisions in this state.
subjectNoOnly decisions whose subject contains this text, case-insensitive.
artifactYes
entity_idNoOnly decisions whose basis names this face, within the supplied revision. A face ID from another revision denotes different geometry, so it is not matched.

Output Schema

NameRequiredDescriptionDefault
decisionsYes
current_revisionNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior5/5

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

Annotations already cover read-only/idempotent/non-destructive, and the description goes further with behavior they cannot express: results come back newest-first, records are marked stale when made against a different revision, and an entity filter silently narrows to the supplied revision because face IDs are revision-scoped. That last point is a non-obvious filtering behavior an agent would otherwise get wrong.

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?

Ordering and staleness semantics are front-loaded, and the usage sentence is placed last as a decision cue. The revision-scoping sentence is dense and could be tightened, but every sentence carries information not available in the schema.

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?

An output schema exists, so return shape needn't be described, and pagination is handled in the schema. The description covers ordering, staleness, and filter semantics, which is what an agent needs; only edge behavior on empty results is unaddressed, a minor gap for a 6-param read tool.

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 83% so the baseline is 3, but the description adds genuine meaning: the `artifact` argument must be the current ArtifactRef from inspect, and the entity/revision interaction is explained with a worked rationale ("a decision about face:3 in an older revision is not a decision about face:3 now"). It also summarizes the subject/status/entity filter axes, going beyond the per-parameter schema text.

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?

Opens with a specific verb+resource and adds real scope: decisions for an artifact, newest first, each flagged stale relative to the supplied revision. It stops short of differentiating itself from the sibling `recall` tool, so an agent must infer which of the two to call.

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?

"Use this when a revision reappears, to recover what was already concluded instead of reaching it again" gives a concrete triggering condition, and "Pass the current ArtifactRef from Geometry inspect" states a prerequisite. It never names `recall` as the alternative or gives an explicit when-not, so routing between the two recall tools remains inferred.

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