ThinAir Data

explain_query

Read-onlyIdempotent

Analyze a SQL query's execution plan and return plain-English performance recommendations. Runs EXPLAIN ANALYZE (Postgres) or EXPLAIN FORMAT=JSON (MySQL). [BUILD tier]

Input Schema

TableJSON Schema

Name	Required	Description	Default
`sql`	Yes	The SELECT query to analyze
`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.

Output Schema

TableJSON Schema

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

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and openWorldHint=true, covering the safety profile. The description adds specific behavioral detail by revealing that it runs EXPLAIN ANALYZE or EXPLAIN FORMAT=JSON depending on dialect, which explains how the analysis is performed without contradicting annotations.

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 with no wasted words, leading with the primary purpose and then adding implementation details. It is tightly structured and easy to parse.

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 the simple two-parameter tool, an output schema, and annotations covering safety, the description provides sufficient operational context including the dialect-specific EXPLAIN behavior. The [BUILD tier] marker adds minor context but doesn't hinder completeness.

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?

Input schema documents both parameters with full descriptions, including detailed guidance for resolving the connection parameter from list_connections output. The tool description itself does not elaborate on parameters, but schema coverage is 100%, so the baseline 3 applies.

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: analyzing SQL execution plans and producing plain-English performance recommendations. The verb 'analyze' and resource 'SQL query's execution plan' distinguish it from sibling tools like optimize_query (which likely modifies queries) and query_sql (which executes queries).

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 implies usage for query performance diagnosis and mentions dialect-specific behavior (EXPLAIN ANALYZE for Postgres, EXPLAIN FORMAT=JSON for MySQL), but does not explicitly state when to choose this over alternatives like optimize_query. It provides clear context but no exclusions or alternative references.

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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