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reconcile_migration

Compare source records to ServiceNow target records to verify migration integrity using count checks, correlation matching, and field-level diffs with transform-aware verdicts.

Instructions

Deep comparison between source records and ServiceNow target records to verify the migration was successful. Goes far beyond fill-rate checks:

  1. COUNT CHECK — source count vs SN count (detects missing or duplicate records)

  2. RECORD MATCHING — correlates each source record to its SN counterpart using a correlation field (e.g. u_jira_key, u_sf_id)

  3. FIELD-LEVEL DIFF — for every matched pair, compares each mapped field value Expected vs Actual, with transform-awareness (so a Jira "In Progress" → SN state "2" is not flagged as a mismatch)

  4. VERDICT — PASS / PARTIAL / FAIL with specific reasons

Returns: verdict, per-record diffs, per-field accuracy %, missing/extra records. Use after a test migration or a full migration to confirm data integrity.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
source_recordsYesArray of source records (Jira issues, SF records, etc.) to compare against SN
sn_tableYesTarget ServiceNow table (e.g. incident, u_my_custom_table)
field_mappingsYes{ sourceField: snField } — the same mapping used during migration
correlation_fieldYesField in the SN table that stores the source record ID (e.g. u_jira_key, u_sf_id, correlation_display)
source_id_fieldYesField in source records that holds the unique ID (e.g. "key" for Jira, "Id" for Salesforce)
transform_rulesNoTransform mappings to apply when comparing — prevents false mismatches for status/priority fields
date_fieldsNoSN field names that are dates — compared as YYYY-MM-DD only, ignoring time
ignored_fieldsNoSN field names to skip in value comparison (e.g. sys_created_on, sys_updated_by)
limitNoMax source records to compare (default 200)
full_scanNoAlso check for records in SN that are not in the source sample (detects extras/duplicates)
Behavior5/5

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

Since no annotations are provided, the description fully carries the burden of behavioral disclosure. It details the algorithm (count check, record matching, field-level diff with transform awareness), explains how transform rules prevent false mismatches, and describes return values (verdict, per-record diffs, accuracy %). This is comprehensive.

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 well-structured with a clear purpose statement, numbered steps, and bullet points. Every sentence adds value. No unnecessary text. Front-loaded with the core purpose.

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 tool's complexity (10 parameters, 5 required, no output schema), the description covers all essential aspects: what it does, return values, algorithm steps, and parameter roles. Missing details like error handling are acceptable for a tool of this nature.

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

Parameters4/5

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

Schema description coverage is 100%, so baseline is 3. The description adds value by explaining how parameters like transform_rules and date_fields are used in the algorithm (e.g., transform-awareness prevents false mismatches). This goes beyond the schema descriptions, which are already clear.

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?

Description clearly states the tool performs a deep comparison between source and target records to verify migration success, listing four specific steps (count check, record matching, field-level diff, verdict). It distinguishes itself from simpler fill-rate checks, making its purpose highly specific.

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 explicitly says 'Use after a test migration or a full migration to confirm data integrity.' It provides clear context for when to use the tool but does not explicitly mention when not to use it or list alternative tools. However, the sibling tool set includes verify_migration_counts, which implies a simpler alternative.

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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