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sonisoft-cnanda

now-sdk-ext-mcp

Query Update Records

query_update_records

Find records by encoded query and update them with specified fields. Use dry-run to preview affected count before confirming changes.

Instructions

Find records matching an encoded query and update them all with the specified data. Supports a dry-run mode: set confirm=false (the default) to see how many records would be affected WITHOUT making changes, then set confirm=true to execute.

IMPORTANT: When confirm=true, this modifies records on the ServiceNow instance. Always run a dry-run first to verify the match count before committing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
instanceNoThe ServiceNow instance auth alias (e.g., "myinstance", "prod"). If not provided, falls back to the SN_AUTH_ALIAS environment variable.
tableYesThe ServiceNow table to update records on (e.g., "incident", "sys_user").
queryYesAn encoded query string to find records to update. Examples: "active=true^priority=5", "state=1^assignment_group=NULL".
dataYesField values to set on all matching records (e.g., {"priority": "3", "state": "2"}).
confirmNoWhen false (default), performs a dry-run that returns the match count without making changes. Set to true to actually execute the updates.
limitNoMaximum number of records to update. If omitted, updates all matches.
Behavior4/5

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

No annotations are present, so the description itself must disclose behavioral traits. It clearly states that when confirm=true, the tool modifies records, and advises running a dry-run first. This covers the key safety aspect, though details like error handling or rate limits are omitted.

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 two concise paragraphs: first explains the function and dry-run mode, second emphasizes the safety warning. Every sentence adds value without redundancy.

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 no output schema, the description hints at the match count from dry-run but doesn't specify exact output format. It covers the primary concerns for a mutating tool, though details on limit and error behavior could be added.

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 coverage is 100%, so the schema already fully describes all parameters. The description adds value by explaining the confirm parameter in context of dry-run, but overall adds little beyond what the schema provides.

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 explicitly states the tool finds records matching an encoded query and updates them, and highlights the dry-run mode. This clearly distinguishes it from sibling tools like query_table (read-only) and query_delete_records (deletion).

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?

Provides clear guidance on using dry-run before commit, emphasizing safety. However, it does not explicitly compare this tool to alternatives like batch_update_records or update_record, which could help an agent choose optimally.

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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