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customer_success_plan_domain_intelligence

Destructive

Plan customer success domain intelligence by submitting a free-text objective. Optional structured inputs tailor the analysis to your company and tenant scope.

Instructions

Run the customer_success domain agent action plan_domain_intelligence.

Routes through the platform's domain-agent dispatcher under your JWT, tenant, and company scope.

Args: message: Free-text objective for the action. inputs: Optional JSON string of structured inputs for the action.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputsNo{}
messageNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.3/5.0
Behavior2/5

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

While annotations indicate destructiveHint=true and readOnlyHint=false, the description adds no detail about side effects, state changes, or return behavior. It only says 'Run' and 'Routes through dispatcher,' which does not clarify what the action actually does or what the user should expect. This is a minimal addition beyond the annotations and fails to disclose behavioral nuances.

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 exceptionally concise, consisting of two short sentences. It efficiently conveys the essential information without any fluff, making it easy for an agent to parse and act upon.

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 tool with only two parameters and a simple action, the description covers the basics but omits any information about the output or return value (despite the output schema being indicated as present). It also does not explain what 'plan_domain_intelligence' accomplishes or what 'domain-agent dispatcher' implies, leaving some contextual gaps that could affect correct invocation.

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?

The description gives basic semantics for both parameters: 'message' is a free-text objective and 'inputs' is an optional JSON string. This is useful given the schema has no descriptions (0% coverage), but it stops short of explaining what kind of objectives are expected or what structured inputs are appropriate, leaving some ambiguity.

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 clearly states the tool's action ('Run the customer_success domain agent action plan_domain_intelligence'), specifies the resource (the domain agent action), and distinguishes it from siblings by naming the specific domain (customer_success) and the exact action. It also explains the routing mechanism, making the purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives (e.g., the sibling 'brain_plan_domain_intelligence' or other domain-specific tools). It mentions authentication scope but lacks any conditional context or suggested use cases, leaving the agent to infer appropriate invocation.

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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