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Resolve Image Assumptions

resolve_assumptions

Use this when you need to confirm or override the open facts in an assumption ledger from trace_from_image (missing scale, inferred/assumed values) before committing geometry built from a reference photo. Reads the persisted <model>.ledger.json at ledgerPath, applies each resolution — { id, confirm: true } to accept a fact as-is, or { id, value } to override it — rewrites the ledger file, and returns the updated ledger plus paramOverrides (factId -> value) to feed straight into set_param. Pair with the kernelcad-from-reference skill.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ledgerPathYesPath to the `<model>.ledger.json` file persisted alongside the traced source.
resolutionsYesOne resolution per ledger fact id to act on.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okYes
ledgerNoThe ledger after applying resolutions (present on success).
diagnosticsYes
paramOverridesYesfactId -> value for every resolved fact with a value; feed into set_param.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.5/5.0
Behavior5/5

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

The description discloses the key side effect: it reads the ledger file, rewrites it, and returns the updated ledger plus paramOverrides. This goes beyond the annotations, which only indicate readOnlyHint=false, and gives an agent a clear model of what changes.

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?

The description is front-loaded with the trigger condition and packs read/apply/rewrite/return behavior into compact, purposeful sentences. Every clause earns its place, including the JSON shapes and the skill pairing.

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?

Given two parameters, an output schema, and annotations already marking the tool as non-read-only, the description covers the source ledger, resolution payload shapes, the file-rewriting side effect, the return value, and the integration with set_param. Nothing essential is missing for correct invocation.

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?

The schema already provides 100% coverage for both parameters, including the confirm/value semantics. The description adds workflow context and output routing, but it does not add substantial parameter-level meaning beyond what the schema already states. Baseline 3 is appropriate.

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?

The description states a precise use case: confirm or override open facts in a trace_from_image ledger before committing geometry. It names the concrete operations (read, apply resolutions, rewrite, return paramOverrides) and references the source sibling trace_from_image and downstream set_param.

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?

The description explicitly opens with 'Use this when...' and specifies the workflow context: 'before committing geometry built from a reference photo.' It does not name a comparable alternative tool or give an explicit when-not-to-use condition, but the workflow positioning is clear.

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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