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niyogi

chatgpt-ads-manager-mcp

by niyogi

update_lead_form

Save a new draft revision for a lead form using the expected revision ID to avoid conflicts and keep fields, description, and privacy policy up to date.

Instructions

Save a new draft revision for a Lead Form. Requires expected_draft_revision_id for conflict detection.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNo
fieldsNo
descriptionNo
lead_form_idYesLead form ID.
ad_account_idNo
privacy_policy_urlNo
expected_draft_revision_idNoCurrent draft revision ID from get_lead_form for optimistic locking.
Behavior3/5

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

With no annotations, the description carries the burden, and it does disclose two meaningful behaviors: it creates a new draft revision and uses expected_draft_revision_id for conflict detection. However, it does not explain what happens on a conflict, whether changes are reversible, or other side effects of saving the revision.

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, no fluff, and the main purpose is front-loaded. The second sentence adds a genuine prerequisite that is not obvious from the schema alone. Every sentence earns its place.

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

Completeness2/5

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

The tool is a non-trivial mutation with seven parameters, no output schema, and no annotations, and the description only covers one parameter and the core action. It lacks context about what the call returns, how conflicts are surfaced, or the workflow relationship with get_lead_form and publish_lead_form. This is not enough for an agent to call it with confidence.

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 description coverage is only 29%, so the description was expected to compensate for the seven parameters. It adds semantics only for expected_draft_revision_id ('conflict detection'), while important parameters like fields, privacy_policy_url, ad_account_id, name, and description receive no explanatory help in the tool description.

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 identifies the action ('Save a new draft revision') and the resource ('Lead Form'), which is distinct from related siblings like create_lead_form, publish_lead_form, or archive_lead_form. However, it does not explicitly name or contrast alternative siblings, so it stops short of full differentiation.

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 requirement for expected_draft_revision_id gives some context for using the tool correctly, but the description does not state when to prefer this over create_lead_form or get_lead_form, nor any exclusions. Usage is implied from 'draft revision' but not made explicit.

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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