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mfbaig35r
by mfbaig35r

artifact_build

Build a derived artifact by filtering existing cells, keeping schedules consistent with their sources, and reporting unreviewed rows or standards violations.

Instructions

Build a derived artifact by filtering cells that already exist.

00a: never re-derive a schedule by asking a fresh question, so that the schedule and its sources cannot disagree. Each artifact reports how many of its rows are still unreviewed or carry a standards violation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
artifactYesconsent_schedule | coverage_register | closing_conditions | transaction_payments | chain_gaps | issues_list
min_review_statusNoOnly carry cells at this review status, e.g. 'Verified'.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.1/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It discloses some behavior: it does not re-derive schedules from fresh questions (implying it reuses existing data) and it reports counts of unreviewed rows or violations. However, it lacks details on side effects, reversibility, or the creation/update nature. The provided info is a start but not comprehensive.

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

Conciseness3/5

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

The description is reasonably short but suffers from a formatting anomaly '00a:' that disrupts flow. The sentences are not well connected, and the rule about schedules appears out of place. It is concise but not structurally clean.

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?

Given that there is an output schema (though not shown) and the input schema is fully covered, the description provides the core purpose and some output behavior. However, it lacks context on how to choose artifact types, prerequisites, and typical use cases. It is minimally sufficient but not complete.

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 both parameters are well documented. The description does not add additional semantic meaning beyond the schema. It mentions filtering cells, which aligns with min_review_status, but does not elaborate further. Baseline of 3 is appropriate.

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 the core function: 'Build a derived artifact by filtering cells that already exist.' This specifies the verb and resource. However, it does not explicitly differentiate from sibling tools, though none of the siblings appear to perform the same task. The purpose is clear enough.

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

Usage Guidelines2/5

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

The description provides a somewhat cryptic rule: '00a: never re-derive a schedule by asking a fresh question, so that the schedule and its sources cannot disagree.' This hints at a constraint but does not clearly state when to use this tool versus alternatives. There is no mention of when not to use it or any explicit selection criteria.

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