ThinAir Data

saved_queries

Destructive

Manage your personal library of reusable SELECT queries. action=save stores a query by name; action=run executes a saved query; action=list returns all your saved queries; action=delete removes one. [BUILD tier]

Input Schema

TableJSON Schema

Name	Required	Description
`id`	No	Query ID (alternative to name for run/delete)
`sql`	No	SQL to store (required for action=save)
`tag`	No	Filter by tag (action=list)
`name`	No	Query name (save/run/delete)
`tags`	No	Tags (action=save)
`action`	Yes	save \| run \| list \| delete
`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.
`description`	No	Freeform description (action=save)

Output Schema

TableJSON Schema

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

TDQS

A4/5.0

Behavior3/5

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

Annotations already declare destructiveHint=true and readOnlyHint=false, and the description confirms the delete action. However, the description adds little beyond the annotations—it does not mention whether run returns data, whether delete is irreversible, or any other side effects. No contradiction exists.

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 sentences, front-loaded with the purpose and followed by a compact enumeration of actions. It wastes no words, and the [BUILD tier] tag is minor but not confusing. Excellent conciseness.

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?

The tool has 8 parameters and an output schema, but the schema's parameter descriptions are highly detailed (especially the connection guidance). The description adequately covers the four actions and the overall purpose. It does not discuss edge cases like overwriting existing names or missing IDs, but the overall package is complete for a CRUD-style tool.

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?

Schema coverage is 100%, so the baseline is 3. The description adds value by explaining the semantics of the action parameter (e.g., 'action=save stores a query by name; action=run executes a saved query'), which goes beyond the simple enum values in the schema. Other parameters are already well documented.

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 opens with a specific verb+resource ('Manage your personal library of reusable SELECT queries') and then enumerates the four distinct actions (save, run, list, delete). This clearly distinguishes it from sibling tools like query_sql (ad-hoc execution) and suggest_queries (suggestions).

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 usage by listing the actions and the concept of a personal library, but it does not explicitly state when to use this tool versus alternatives (e.g., 'for ad-hoc SQL, use query_sql'). No exclusions or alternative tool references are provided.

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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