Rank accounts by multi-dimensional scoring to focus sales efforts on high-priority prospects. Input account data and custom weights to get a prioritized list based on fit, intent, timing, and relationship.
Rank and prioritize accounts using multi-dimensional scoring
| Name | Required | Description | Default |
|---|---|---|---|
| accounts | No | List of accounts to prioritize | |
| prioritization_weights | No | Custom weights (must sum to 100) |
Changes observed during successful MCP inspections.
v1.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full burden of behavioral disclosure, but it only says 'rank and prioritize' with 'multi-dimensional scoring.' It does not explain how weights are applied, whether any data is persisted, what the output format is, or how missing values are handled.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no redundant wording. It earns its place, though it is brief enough that adding a bit more actionable context would not have made it bloated.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given nested input objects, no annotations, and no output schema, the description is incomplete. An agent is not told what the scoring returns, how to interpret the prioritized list, or how to handle unknown fields like an intent_signals value of 0.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema documents all parameters with 100% coverage, including descriptions for account dimensions and the constraint that weights must sum to 100. The description adds little beyond the generic 'multi-dimensional scoring' label, so the baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the operation ('Rank and prioritize accounts') and the target resource ('accounts'), and it hints at the approach ('multi-dimensional scoring'). It is distinguishable at a high level from the sibling ICP-analysis tools, though it does not explicitly contrast with them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided about when to use this tool instead of siblings like icp_scoring_model or icp_gap_analysis. There are no prerequisites, exclusions, or contextual triggers, leaving tool selection entirely to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.