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chartmogul

ChartMogul MCP Server

Official
by chartmogul

update_customer

Modify customer details in ChartMogul, including company information, contact data, status, location, and custom attributes using structured updates.

Instructions

[ChartMogul API] Update customer attributes. MODIFIABLE: company, lead_created_at, free_trial_started_at, zip, city, state, country, attributes (nested tags/custom), owner, primary_contact, status, website_url. Provide updates in data dict. attributes format: {"tags": ["new", "tags"], "custom": [{"type": "String", "key": "value"}]}. Returns updated customer object with all fields. REQUIRED: uuid (string), data (dict)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
uuidYes
dataYes
Behavior3/5

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

With no annotations provided, the description carries full burden. It discloses the tool modifies data (implied by 'Update'), lists modifiable fields, and specifies the return format. However, it doesn't mention authentication requirements, rate limits, error conditions, or whether changes are reversible/destructive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized with three sentences: purpose, parameter details, and return format. It's front-loaded with the core action. Minor redundancy exists ('Provide updates in data dict' could be tighter).

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 mutation tool with no annotations and no output schema, the description covers purpose, parameters, and return format adequately. However, it lacks behavioral context like error handling, side effects, or prerequisites. Given the complexity, more completeness would be beneficial.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must compensate fully. It provides essential semantic context: 'uuid' identifies the customer, 'data' contains updates in dict format with specific field examples and nested attribute formatting rules. This adds significant value beyond the bare schema.

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 updates customer attributes with a specific list of modifiable fields. It distinguishes from siblings like 'create_customer' (creation vs update) and 'add_customer_tags' (partial vs comprehensive update), though not all sibling differences are explicitly called out.

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 usage when updating customer attributes rather than creating new customers, but doesn't explicitly state when to use this vs alternatives like 'update_contact' or 'add_customer_tags'. No explicit exclusions or prerequisites are provided.

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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