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audit_cell

Get the provenance/audit trail for a specific cell in an AQL query result. Useful for tracing where a value came from — which imports, calculations, or source data contributed to it.

Provide the same AQL query that produced the result, plus the zero-based row and col index of the cell you want to audit.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
colYesZero-based column index of the cell to audit
rowYesZero-based row index of the cell to audit
contextYes
databaseYesDatabase name
rowHeadingsYes
columnHeadingsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.3/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It implies a read-only audit and adds the useful prerequisite of re-supplying the originating query, but it discloses nothing about permissions, output format, cost, or limits for a tool that returns a provenance trail.

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?

Three tight sentences, front-loaded with the purpose and followed by the use case and input note. No filler, though the parameter sentence introduces ambiguity rather than resolving it.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 6-required-parameter tool with no annotations, no output schema, and 50% schema coverage, the description is too thin. It neither accounts for the three unexplained parameters nor describes the shape of the provenance result the agent will receive, and it references an 'AQL query' input that does not correspond to any schema field.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is only 50%, and the description explains only row/col (already documented in the schema) while leaving context, rowHeadings, and columnHeadings completely undocumented in both places. Worse, it instructs the agent to 'provide the same AQL query,' but no query parameter exists in the schema, so the mapping between the described input and the actual required fields is unclear.

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?

States a specific verb and resource ('Get the provenance/audit trail for a specific cell in an AQL query result') and explains the value proposition (tracing which imports, calculations, or source data contributed). It is distinguishable from most siblings, though it never names the closely related query_aql or describe_query tools to sharpen the boundary.

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?

Gives a clear when-to-use condition ('useful for tracing where a value came from') and states the prerequisite that you supply the query that produced the result. There is no when-not guidance or explicit routing to an alternative, so it stops 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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