list_authorsReturn all authors available in the corpus — each entry includes author id (used as input to other tools), display name, birth/death dates, and public-domain status.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| authors | Yes | Array of available authors with ids, names, dates, and public-domain status |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly and idempotent hints, so the description's addition of output structure and the note about author ID being used elsewhere adds useful context beyond annotations.
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 sentence that efficiently conveys purpose and output details. No wasted words; structure is front-loaded and clear.
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 no parameters and an existing output schema, the description adequately covers the tool's behavior. It mentions all key output fields and their utility, making it complete for agent understanding.
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?
With zero parameters and 100% schema coverage, the baseline is 4. The description adds no parameter details (none needed) but provides value by explaining the output fields.
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 states 'Return all authors available in the corpus' with specific verb and resource, and it details the included fields. It distinguishes the tool from siblings by being the only author-listing operation.
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?
While the description does not explicitly compare with alternatives, it clearly implies use when a full list of authors is needed. The lack of parameters and straightforward nature make usage obvious, but it could mention that this is the canonical way to get author IDs.
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.
The tool set includes multiple similar tools (e.g., three ask_pipeworx variants, several Polymarket tools) that could cause agent confusion. While each has a distinct purpose, the boundaries are subtle and descriptions lean heavily on jargon, making misselection likely.
All tool names use snake_case consistently. Most follow a noun_verb or verb_noun pattern, but some (e.g., ai_visibility_check, bet_research) start with a subject rather than an action, breaking a strict verb-first convention.
With 40 tools, the server feels overloaded. The name 'Quotes' suggests a narrow focus, yet the tool set spans fact-checking, company research, prediction markets, and more. Many tools are highly specialized or meta-tools, inflating the count without clear necessity.
The server covers a wide range of use cases, from quotes and literature to financial data and prediction markets. However, there are noticeable gaps in core areas (e.g., basic CRUD for quotes beyond search and random), and the sheer breadth creates dead ends for deep workflows.