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 burden. It discloses the operation ('List'), the exact count (28), and the return content (trait targets per category). It does not explicitly state read-only, but 'List' implies no side effects.
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?
One sentence, ~25 words, starts with verb and resource, includes count and categories, no fluff.
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?
For a zero-parameter listing tool with no output schema, the description fully specifies the scope (all profiles), count, organization (by category), and output (trait targets). No ambiguity remains.
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, so schema coverage is trivially 100%. The baseline of 4 applies; the description adds no parameter semantics because there are no parameters to describe.
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 uses the verb 'List' and specifies the exact resource: all 28 scoreable human personality profiles, organized by bot category, with trait targets. This clearly distinguishes it from sibling tools which handle network stats, pricing, and scoring.
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 usage for retrieving the complete set of personality profiles, but it does not explicitly state when to use this over alternatives or provide exclusions. Sibling tools are unrelated, so usage is obvious but not articulated.
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 clearly distinct resources: scoring, profiles, pricing, network stats, fee quotes, and feature requests. The only mild ambiguity is between get_pricing and preview_fee, since both deal with fees, but their descriptions clarify one is a general manifest and the other is a wallet-specific quote.
The set mostly follows a snake_case verb_noun convention: get_network_stats, list_profiles, preview_fee, request_feature, score_text. storelayer_widget_menu is the clear outlier, using a noun-heavy compound with no verb and a different naming pattern.
Seven tools is a reasonable size for a platform-facing MCP server and avoids bloat. However, storelayer_widget_menu is an unrelated promotional detour that makes the set feel slightly less well-scoped to its core EQ-scoring purpose.
Core discovery and free scoring are covered well: stats, pricing, profiles, fee previews, feature requests, and score_text. However, paid benchmark submission, progress/trajectory retrieval, and bundle purchasing are only described as external REST endpoints rather than actual tools, leaving notable gaps in the full validation loop.