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schema_confirm_mapping

Prevent incorrect data writes by previewing a table's schema mapping with sample resolved values, then confirm to activate write tools.

Instructions

Confirm a table's schema mapping, unlocking write tools for it (knowledge_ingest, kb_journal, kb_promote today). overrides lets you correct individual canonical-field -> real-column assignments before confirming, e.g. {"source": "origin_ref"} or {"tags": null} to explicitly mark a field unmapped. Gated separately from knowledge_write — confirming a mapping is a more consequential act than a single write.

preview=True: dry-run — return the proposed mapping AND a rendered sample row (what each canonical field actually resolves to) WITHOUT confirming or writing. Review the sample first: a column that name-matches but holds the wrong data — e.g. a content column that is really a provenance blob, with the real text in title/summary — reveals itself in the sample where a name match cannot. preview=False (default): confirm, and include the sample in the response so the confirmation is never blind.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tableYes
app_idYes
previewNo
overridesNo
Behavior5/5

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

With no annotations provided, the description carries full burden. It details the side effect of unlocking write tools, explains preview vs. confirm behavior, and warns about name-matching pitfalls with a concrete example. It also notes the confirmation is 'more consequential' and includes a sample in the response to avoid blind confirmation.

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 moderately long but well-structured, with the core purpose in the first sentence and detailed preview guidance following. The example about a mismatched `content` column is illustrative but adds length; still, every sentence serves a purpose.

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?

The tool has no output schema, but the description describes the response for both preview and confirm modes: 'return the proposed mapping AND a rendered sample row' and 'include the sample in the response'. It also explains the unlocking of write tools and the gating, making the tool's place in the workflow clear.

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?

The input schema has no descriptions (0% coverage). The description compensates by explaining `overrides` with concrete examples ({'source': 'origin_ref'} and {'tags': null} for unmapping), and by detailing `preview=True` vs `preview=False` semantics. It does not explicitly describe `app_id` and `table`, but their names are self-explanatory and the description references the table being confirmed.

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 uses a specific verb ('Confirm') with a clear resource ('a table's schema mapping') and explains the consequence (unlocking write tools). It distinguishes itself from sibling write tools like knowledge_ingest by framing this as a prerequisite/confirmation step.

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 advises using preview=True as a dry-run and says 'Review the sample first'. It also mentions the tool is 'Gated separately from knowledge_write', implying a distinction from direct write operations. However, it doesn't enumerate explicit alternatives or exclusions, so it falls short of a 5.

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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