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 | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It clearly indicates this is a read-only 'list' operation and details the return content (all 28 profiles with trait targets, one per bot category). It does not state sorting or exact output format, but for a simple listing tool, the behavior is sufficiently transparent.
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, well-structured sentence that front-loads the key action and scope ('List all 28...'). Every element contributes meaningful information without redundancy.
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 there is no output schema or annotations, the description fully explains what the tool returns and its scope. For a straightforward list endpoint, this is complete and sufficient for an agent to use it correctly.
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 0 parameters, so the empty schema already provides full parameter coverage. The description adds value by explaining what the returned data contains (profiles with trait targets), exceeding the baseline for parameterless tools.
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 states the exact action ('List'), the resource ('all 28 scoreable human personality profiles'), and the scope (one per bot category with trait targets). This clearly distinguishes the tool from siblings like score_text or get_pricing, which serve entirely different purposes.
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 phrase 'scoreable human personality profiles' implies these profiles are used for scoring, so the tool is useful before calling score_text. However, there is no explicit when-to-use or alternative exclusions, leaving the agent to infer the context from the sibling list.
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 target distinct concerns: stats, pricing, profiles, fee previews, feature requests, and scoring. The main ambiguity is between get_pricing and preview_fee, but their descriptions clarify that one is the general pricing manifest and the other is a wallet-specific quote.
The majority of tools follow a readable snake_case verb_noun pattern, such as get_network_stats, list_profiles, preview_fee, request_feature, and score_text. The outlier is storelayer_widget_menu, which is a noun phrase rather than a clear verb action, and the retrieval verbs vary slightly, but the overall pattern is consistent enough.
Seven tools is a reasonable size for a scoring and benchmarking platform and stays well within the ideal range. However, storelayer_widget_menu feels like an unrelated promotional addition that does not earn its place alongside the scoring-focused tools.
The free scoring path, profile listing, pricing, and network stats are present, but the paid validation workflow via POST /api/simulate, progress reports via POST /api/progress, and bundle purchases are only described in get_pricing rather than exposed as MCP tools. This leaves a notable gap in the core paid workflow, though agents could fall back to REST.