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generate_migration

Read-onlyIdempotent

Generate dialect-correct ALTER TABLE migration SQL + rollback from a plain-English intent. Output uses the connection's exact dialect (ALTER TABLE for all three, plus pg-specific USING casts / mssql-specific sp_rename / mysql-specific MODIFY COLUMN). Never executes. Check response dialect field before manually editing — don't hand-translate across dialects. [BUILD tier]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
intentYesPlain English: 'add soft delete to drivers', 'add index on trips.driver_id'
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

A4.4/5.0
Behavior5/5

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

Beyond the readOnlyHint/idempotentHint annotations, the description explicitly states 'Never executes' and details dialect-specific behaviors (pg `USING`, mssql `sp_rename`, mysql `MODIFY COLUMN`). It also advises checking the response `dialect` field before manual edits, adding valuable post-invocation guidance that annotations do not convey.

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?

The description is dense but every clause contributes: purpose, dialect coverage, safety note, and a concrete warning against hand-translation. The enumeration of dialect specifics is slightly long but useful, making it efficient for a tool with this complexity.

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?

Combined with a rich input schema, output schema, and safety annotations, the description covers the core purpose, dialect behavior, non-execution safety, and post-invocation guidance. There are no obvious gaps that would prevent an agent from using the tool correctly.

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%: both `intent` and `connection` have descriptive text, with connection even explaining how to resolve names via `list_connections`. The tool description adds high-level dialect context but does not add parameter-specific meaning beyond the schema, so baseline 3 is appropriate.

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 'Generate dialect-correct ALTER TABLE migration SQL + rollback from a plain-English intent', which clearly identifies the verb (generate), resource (migration SQL + rollback), and input (plain-English intent). No sibling tool focuses on generating migrations, so it is well-distinguished.

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 practical context: it never executes, so an agent knows to treat it as a code-generation step. It warns about dialect mismatches and checking the `dialect` field, which guides safe usage. While it does not name explicit alternatives, the sibling list contains no other migration-generation tool, so this is adequate.

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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