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generate_seed_data

Read-onlyIdempotent

Generate realistic, schema-aware INSERT statements for development and testing. Respects types, constraints, and FK relationships. Never executes. [BUILD tier]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tableYesTable to generate seed data for
formatNoOutput format (default sql)
row_countNoNumber of rows (default 100, max 1000)
connectionNoTarget 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
NameRequiredDescriptionDefault
dataNo
metaNo
displayNo
summaryNo
insightsNo

TDQS

A4.2/5.0
Behavior4/5

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

Beyond the annotations (readOnlyHint, openWorldHint, idempotentHint), the description adds that the tool 'Never executes', which clarifies it only generates statements without running them. It also mentions it 'Respects types, constraints, and FK relationships', adding behavioral detail not implied by annotations. Significant added context.

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 very concise, using only two sentences plus a tier tag, with no wasted words. It front-loads the core purpose ('Generate realistic, schema-aware INSERT statements'), then adds key behavioral constraints. Every clause adds value.

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?

Given that an output schema exists and annotations provide safety hints, the description is complete enough. It explains the tool's role in development/testing, its non-executing nature, and schema awareness. It doesn't need to explain return values because the output schema exists. Minor gaps exist around parameter selection, but these are covered by the detailed schema descriptions.

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%, and every parameter has a detailed description, including a comprehensive explanation for the 'connection' parameter. The tool description adds no parameter-specific information, so it doesn't exceed the baseline of 3. It fully relies on the schema, which is sufficient.

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 the tool generates INSERT statements, with a specific verb ('Generate') and resource ('schema-aware INSERT statements'). It distinguishes itself from sibling query tools by adding 'Never executes' and by focusing on development/testing. This is a specific and unambiguous purpose.

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 gives clear context for when to use the tool ('for development and testing') and emphasizes safety ('Never executes'). However, it does not explicitly name alternative tools or state when not to use it. The context is clear but lacks explicit exclusions.

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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