diligence-kernel

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run_table

Fill review table cells from row documents in dependency order, resuming safely after failures and validating each entry against standards to record violations.

Instructions

Fill a review table's cells, in dependency order, from the documents in each row.

The run is durable: every cell commits as it is filled, so calling again after a failure resumes rather than restarting. Already-filled cells are skipped unless refill is set, and a locked or human-corrected cell is never overwritten.

Each cell is checked against the 00a standards before it persists: the fallback vocabulary, Classify options, ISO dates, no markdown, no arithmetic, and for Verbatim columns that the quoted text actually appears in the row's documents. Violations are recorded on the cell and returned as findings.

This spends money. A 27-column table over 40 rows is roughly 1,080 model calls.

Input Schema

TableJSON Schema

Name	Required	Description
`model`	No	Override the model for this run.
`table`	Yes	Table number as the corpus defines it, e.g. '01' or '13'.
`reason`	No	Why this run exists, for the record.
`refill`	No	Re-fill cells that already carry a value.
`columns`	No	Restrict to these column names.
`provider`	No	'openai' or 'anthropic'. Defaults to DILIGENCE_KERNEL_PROVIDER.
`unit_ids`	No	Restrict to these rows.
`concurrency`	No	Model calls in flight at once, within a stage. Default 6.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 10, 2026

TDQS

A4.2/5.0

Behavior5/5

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

With no annotations, the description carries full burden and does well: mentions durability, resume on failure, skip unless refill, respect for locked cells, compliance checks, cost estimate, and recording violations as findings. This is thorough for a complex operation.

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?

Paragraphs with clear topic separation, no fluff, but could be more scannable with bullets. The cost detail is useful but not essential upfront, slightly pushing the length down from perfect.

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 a complex tool with output schema and 8 params, description covers execution semantics, edge cases (refill, locked cells), compliance, and cost impact. Output schema exists so return values need no explanation. Complete for an agent to call effectively.

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 baseline is 3. The description adds context for 'refill' (skip unless set) and notes provider defaults, but doesn't explain syntax for 'table' or 'columns' beyond schema, keeping it at baseline.

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 clearly states it fills review table cells from documents, with a specific verb and resource. It is distinct from sibling tools like run_estimate and table_read by focusing on execution rather than estimation or reading.

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?

Provides implicit usage via mention of dependency order, durability, and flags, but no explicit when-to-use or alternatives. For example, it doesn't compare against run_estimate for cost estimation or table_read for checking values, leaving the agent to infer based on the action.

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