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hindocharaj1997

Data Recon MCP Server

run_row_count_check

Compare row counts between source and target tables to identify major discrepancies quickly. Supports optional date range partitioning for incremental comparison.

Instructions

✅ FAST FIRST CHECK - Compare row counts. Run this FIRST - it's fast and catches major issues. PREREQUISITE: Call validate_table_exists for both tables first. For large tables, use partition_config to compare date ranges.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sourceYes
targetYes
partition_configNoOptional: {column, start_value, end_value} for incremental comparison
Behavior3/5

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

No annotations are present, so the description carries the full burden. It notes the tool is fast and catches major issues, implying a read-only check, but does not explicitly state behavioral traits like nondestructiveness or rate limits. More context about what happens during execution would improve transparency.

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?

Three sentences, each adding value. Front-loaded with key purpose and urgency. No redundancy or wasted words. Efficient and clear.

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?

The description covers prerequisites and a common use case (partitioned large tables) but lacks details about return values, error scenarios, or behavior when source/target tables don't exist (assuming validate_table_exists handles that). Given no output schema, more context on results would enhance completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is low (33%), with only partition_config having a description. The description adds no semantic meaning for required source and target parameters beyond the schema. For partition_config, it adds 'for incremental comparison', which helps, but overall insufficient to compensate for the coverage gap.

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 tool compares row counts and is a fast first check. It implies a preliminary role but does not explicitly differentiate from siblings like 'run_aggregate_check' or 'run_schema_check', missing explicit sibling differentiation.

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 provides explicit prerequisites ('validate_table_exists'), recommends using it first, and gives guidance for large tables with partition_config. However, it does not mention when to avoid using this tool or provide direct alternatives.

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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