List all 28 scoreable human personality profiles — one per bot category (support, sales, coding, medical, eldercare, voice, and more) — with their trait targets.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations present, the description carries the behavioral burden, and it does well by using the read-only verb 'List' and specifying both the exact item count and the contained data ('trait targets'). It does not disclose output format or authorization needs, but for a simple parameterless read-only list this is a minor gap.
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 filler. It communicates the resource, count, category scope, and return payload compactly, and every clause earns its place.
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?
The tool has no parameters and a fully specified schema, so the main missing context would be output shape. The description broadly conveys that the tool returns profiles with trait targets, which is adequate for selection and invocation, though a precise return format would make it fully complete given there is no output schema.
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 tool has zero parameters and the schema confirms this, so the baseline of 4 applies because there are no parameter semantics for the description to explain. The description adds clarity about what the returned profiles contain, which is the relevant semantic surface for this tool.
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 names a specific verb ('List'), resource ('scoreable human personality profiles'), and exact scope ('all 28'), which distinguishes it from the sibling tools that deal with stats, pricing, fees, features, text scoring, and store layers. The one-sentence definition leaves no ambiguity about what this tool returns.
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?
The description implies the tool is for retrieving personality profile data, and the examples of bot categories give useful context about when it would be relevant. However, it does not explicitly state when to choose this tool over alternatives or mention any exclusion conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools are clearly distinct: stats, pricing, profiles, fee previews, feature requests, scoring, and Storelayer actions each have separate purposes. The two Storelayer tools could be confused since both reference the same 57-widget catalog, but one is explicitly read-only recommendation and the other is activation/install. Pricing-related tools are also separated between general manifest and wallet-specific fee preview.
The majority of tools follow a verb_noun snake_case pattern: get_network_stats, get_pricing, list_profiles, preview_fee, request_feature, score_text. The two storelayer_* tools break the pattern by leading with a domain prefix and one uses a noun-noun form rather than verb_noun. Overall it is readable but not fully consistent.
Eight tools is a reasonable count for a server that combines scoring, pricing, profile lookup, and Storelayer integration. No tool feels redundant or unnecessary. The breadth of domains is wide, but each tool contributes to a distinct function.
The tool surface covers the main informational and free actions: stats, pricing, profile listing, fee preview, scoring, and feature requests. However, paid validation, progress reports, bundle purchases, and Storelayer verification/removal are only described as external endpoints or multi-step flows rather than exposed as MCP tools. These gaps are workable but may require agents to leave the MCP server for key monetized capabilities.