top_marketsMost-active open Manifold markets right now — a live attention/demand signal for what the prediction-market crowd is trading. Optionally narrow to a topic. Returns compact market records sorted by popularity. Keyless.
| Name | Required | Description | Default |
|---|---|---|---|
| term | No | Optional topic to narrow to, e.g. "politics", "AI". | |
| limit | No | Max markets to return (default 15, max 25). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds that it returns 'compact market records sorted by popularity' and is 'Keyless', providing behavioral context beyond the 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 two sentences, front-loads the core purpose, and contains no unnecessary words. Every sentence adds value.
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 two optional parameters, no required params, and no output schema, the description sufficiently explains what the tool does and returns. It lacks detail on the record format but 'compact market records' is adequate for a simple list.
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 description coverage is 100%, so the schema already documents both parameters. The description adds value by saying 'Optionally narrow to a topic' and 'Keyless', which are not in the schema comments.
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 it returns 'most-active open Manifold markets right now' as a 'live attention/demand signal', with optional topic narrowing. It distinguishes itself from siblings like search_markets by focusing on popularity rather than search.
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 indicates use for current popular markets and mentions optional topic and keyless access, but does not explicitly state when not to use it or compare to alternatives. The context from sibling names helps, but the description itself could be more explicit.
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.
Tools have distinct purposes but some overlap exists, e.g., multiple ask_pipeworx variants and deep_research could confuse an agent. Prediction market tools are differentiated but not immediately obvious.
Names are consistently in snake_case but mix verb and noun orders (e.g., 'ai_visibility_check' vs 'ask_pipeworx'). No strict verb_noun pattern throughout.
34 tools is on the high side but still reasonable given the broad domain coverage. Some tools could be consolidated without loss.
Covers data querying, company research, prediction markets, subscriptions, and memory. Minor gaps like no direct web search but ask_pipeworx substitutes.