Browse prediction markets across 7 categories with filtering and sorting. 500+ markets from Kalshi, Polymarket, and Metaculus.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max markets (default: 10, max: 50) | |
| source | No | Filter by source | |
| sort_by | No | Sort order | |
| category | No | Filter by category (default: all) | |
| jurisdiction | No | Filter by jurisdiction |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It adds useful context like '500+ markets from Kalshi, Polymarket, and Metaculus' and mentions filtering/sorting capabilities. However, it does not disclose behavior such as pagination, return format, or rate limits, which is a moderate gap for a listing tool.
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-loaded with the primary action, and contains zero fluff. Every clause adds value: the scope (browse prediction markets), features (filtering/sorting), and scale (500+ markets from three sources). It is highly efficient and well-structured.
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?
The tool has 5 optional parameters, no output schema, and no annotations. The description gives a high-level overview but falls short on providing enough context for an agent to anticipate the response shape or how to combine parameters effectively. It is adequate for a simple browse endpoint, but not fully complete.
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 baseline is 3. The description's mention of 'filtering and sorting' is generic and does not add meaning beyond what each parameter's description already provides. It does not explain the semantics of any specific parameter, so it neither improves nor degrades the schema's clarity.
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 'Browse prediction markets' which is a specific verb and resource. It mentions 7 categories, filtering, sorting, and sources. However, it doesn't explicitly distinguish from the sibling 'search_markets' beyond the broader 'browse' concept, so it's clear but not fully differentiated.
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?
No guidance is given about when to use this tool versus alternatives like 'search_markets'. The description only states what it does ('Browse... with filtering and sorting') but provides no exclusions, prerequisites, or context for when it's preferred. This leaves the agent to infer usage.
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 have distinct purposes, but list_markets and search_markets overlap in market discovery. However, list_markets is for browsing with filters while search_markets is for keyword/natural language queries, so the distinction is clear enough.
All tools follow a consistent verb_noun pattern, with most using 'get_' for data retrieval and a couple using 'list_' and 'search_' for market discovery. The naming is uniform and predictable.
With 9 tools, the server is well-scoped for its purpose of providing prediction market data. Each tool serves a distinct function, and the count is within the ideal range.
The tool surface covers core operations: authentication, market listing/search, real-time probability, historical data, sentiment, trending, aggregate stats, and usage monitoring. A minor gap is the lack of a dedicated market detail endpoint, but get_probability already returns metadata.