Search the ANOTS directory for businesses whose AI agent can answer a question or need. Returns matching businesses with their slug, name, what they do, and agent-card URL. Use this first to discover which agents to ask.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max businesses to return (default 5, max 10) | |
| query | Yes | What you are looking for (e.g. "honest product reviews", "coffee subscription that ships internationally") |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must convey behavioral traits. It correctly indicates the tool performs a search and returns a list, but does not disclose potential side effects, authentication needs, or rate limits. It implies read-only behavior but doesn't guarantee safety.
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 two sentences long, wastes no words, and front-loads the core purpose. It is concise and scannable.
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 output schema, the description adequately lists return fields. Params are fully covered. It lacks error handling or pagination details, but for a simple search tool this is sufficient.
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?
Schema coverage is 100%, so baseline is 3. The description does not add significant extra meaning beyond the schema's parameter descriptions. It repeats the query purpose but does not elaborate on formatting or edge cases.
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 the tool searches a directory for businesses whose AI agents can answer questions, and specifies return fields (slug, name, what they do, agent-card URL). This effectively differentiates from sibling tools like 'ask_business' which would query an individual agent.
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 explicitly advises to use this tool first to discover agents before asking them, providing clear usage context. However, it doesn't explicitly state when not to use it or contrast with alternatives like 'find_and_ask'.
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.
Each tool has a clearly distinct purpose: search_businesses discovers agents, ask_business queries a specific one by slug, and find_and_ask combines both in one step. No overlap or ambiguity.
All tools follow a verb_noun pattern with snake_case: search_businesses, ask_business, find_and_ask. The naming is consistent and predictable.
Three tools is minimal but well-scoped for a directory server focused on agent discovery and interaction. It covers the essential workflow without unnecessary extras.
The tool set covers the core user-facing functionality: search, ask specific, and combined ask. It lacks listing all agents or administrative tools, but for a client-facing directory it is complete enough.
