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dkp5897

SQL Server MCP Server

by dkp5897

sql_compare_counts

Compare row counts between parent and child tables joined by a foreign key to detect missing or extra records and identify data gaps.

Instructions

Compare row counts between a parent and child table (joined by FK) to detect missing or extra records. Useful for finding data gaps.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
childTableYesChild table (e.g. 'Orders')
joinColumnYesThe FK column name that links them (e.g. 'CustomerId')
parentTableYesParent/reference table (e.g. 'Customers')
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It states it's a read-only comparison of row counts, which implies no data mutation. However, it doesn't disclose return format, whether it handles NULLs in the join, performance considerations on large tables, or what 'missing/extra' means exactly (left vs right outer join semantics). For a tool of this complexity, the disclosure is adequate but not rich.

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?

Two sentences, zero waste. Front-loads the action ('Compare row counts between parent and child table'), then adds the purpose ('detect missing or extra records') and use case ('finding data gaps'). Every word earns its place.

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

Completeness3/5

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

For a 3-parameter tool with no output schema and no annotations, the description is reasonably complete for the selection decision: the agent knows what it does and roughly when to use it. However, given the complexity of count comparison (join semantics, NULL handling, directionality of parent/child), additional behavioral detail would improve completeness for invocation confidence.

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 all 3 parameters are documented with examples ('Customers', 'Orders', 'CustomerId'). The description adds context that these are parent/child/FK relationships, complementing the schema. However, the description doesn't add semantic detail beyond the schema's straightforward parameter docs; baseline 3 is appropriate given full coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Compare row counts') and resource ('parent and child table joined by FK'), and its purpose (detect missing or extra records / find data gaps). It distinguishes itself reasonably from siblings like sql_check_foreign_keys (which checks FK integrity, not counts) and sql_find_data. However, it doesn't explicitly name alternatives, so it stops short of a 5.

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 when to use it ('finding data gaps') but doesn't explicitly state when not to use it or name specific alternative tools. For a diagnostics-focused tool among many sql siblings, telling the agent how this differs from sql_check_foreign_keys, sql_count_and_sample, or sql_diagnose_issue would strengthen guidance.

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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