ThinAir Data

pii_scan

Read-onlyIdempotent

Sweep string columns across tables for common PII patterns (email, SSN, credit card, phone, JWT, bearer tokens). Heuristic-only — not a compliance guarantee. [BUILD tier]

Input Schema

TableJSON Schema

Name	Required	Description
`connection`	No	Target connection name from this tenant's inventory. Call `list_connections` to see every name + dialect, then match semantically to the user's intent (e.g. 'analytics' → a connection named `-analytics-`; 'prod' → a connection with `prod-` prefix). If the user didn't specify, use the tenant's default (first added). Do not invent names — resolve from `list_connections` output.
`max_tables`	No	Max tables to sample (default 25, cap 50)
`sample_rows`	No	Rows sampled per table (default 20, cap 100; values < 5 are floored to 5)

Output Schema

TableJSON Schema

Name	Required	Description	Default
`data`	No
`meta`	No
`display`	No
`summary`	No
`insights`	No

TDQS

A4/5.0

Behavior4/5

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

Annotations already provide readOnlyHint, openWorldHint, and idempotentHint. The description adds valuable context by explicitly stating 'Heuristic-only — not a compliance guarantee', which qualifies the reliability of results. This goes beyond the annotations and helps set expectations.

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 two concise sentences, front-loaded with the action and purpose. Every clause is informative, including the heuristic caveat. The '[BUILD tier]' tag is minor but not distracting.

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

Completeness4/5

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

With rich annotations, full parameter documentation, and an output schema, the description is sufficient for the agent to understand the tool's purpose and limitations. It could benefit from mentioning sampling behavior, but that is already captured in parameter descriptions (max_tables, sample_rows).

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 all three parameters are well-documented in the schema. The description does not add parameter-specific meaning, but the baseline of 3 is appropriate given the schema handles the heavy lifting.

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 identifies the tool's function with a specific verb ('sweep') and resource ('string columns across tables'), and enumerates common PII patterns (email, SSN, credit card, phone, JWT, bearer tokens). This distinguishes it from sibling tools like analyze_table or data_profile, which are more general.

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?

The description implies the tool is for scanning PII patterns across tables, but does not explicitly state when to use it over alternatives or mention any exclusions. The heuristic-only caveat gives some context, but there is no clear 'use this when...' guidance or reference to sibling tools.

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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TDQS

A3.9/5.0

Disambiguation4/5

Each tool targets a distinct resource/action, though some overlap exists between analyze_table and data_profile (both profile tables) and between pii_scan and configure_allowlist (both deal with PII). Clear descriptions and separation of quick vs. full analysis mitigate confusion.

Naming Consistency3/5

Most tools follow verb_noun (add_connection, describe_schema, generate_migration), but a notable minority use noun phrases (data_profile, pii_scan, query_firewall, saved_queries, quota, impact_analysis). This mixed convention creates inconsistency, though the naming is still readable.

Tool Count3/5

26 tools is slightly over the 16-25 heavy threshold, but each tool addresses a distinct need across connection management, querying, analysis, security, and performance. While the count feels high, the breadth justifies it; however, it's approaching the 'too many' range.

Completeness5/5

The tool set covers the full lifecycle: connections (add/remove/list/test), querying (query_sql, saved_queries, cross_db_query), schema exploration/migration (describe_schema, generate_migration, impact_analysis), data quality/compliance (analyze_table, data_profile, pii_scan), performance (explain_query, optimize_query, show_locks), and monitoring (watch_table, detect_anomalies). Any gaps are minor, such as no update_connection, but that's not a core need.

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