get_prediction_marketsLive prediction markets from Polymarket and Kalshi: YES price, volume, close date. Optional topic search (e.g. 'election', 'bitcoin', 'nfl'). Free public data.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | Optional topic to search |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, idempotent, and non-destructive behavior, so the bar is lower. The description adds that the data is free/public (no auth barrier), that it is live, and which fields are returned — useful context beyond the annotations. No contradiction exists.
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 dense sentence front-loads the key outcome (live prediction markets, fields, sources), then tucks optional search and access information into a compact second clause. Every word adds value with no filler.
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 one-optional-param, read-only tool with no output schema, the description covers the source universe, the returned data fields, the optional query semantics, and the free/public access condition. It does not describe the list shape or what happens with no query, but the field listing and 'optional' wording handle most of the needed context.
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 schema already fully documents 'query' as an optional topic string (100% coverage), yielding a baseline of 3. The description adds concrete example topics ('election', 'bitcoin', 'nfl'), which helps an agent form valid queries; it does not clarify behavior when the query is omitted.
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 names a specific resource ('prediction markets'), scopes it to two named sources (Polymarket, Kalshi), and lists the returned data fields (YES price, volume, close date). This clearly separates it from sibling market-data tools like get_fx_rates and get_perp_prices.
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?
Usage is implied: use this when you need live prediction-market quotes from Polymarel/Kalshi. There is no explicit when-to-use, when-not-to-use, or comparison to sibling tools, so the agent must infer routing from the tool name and resource.
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 addresses a distinct data domain: FX rates, perpetual futures, portfolio summary, open positions, prediction markets, and general prices. There is minor potential overlap between get_price and get_perp_prices for crypto assets, but the distinction between spot/current price and perpetual futures is clear enough.
All tool names follow a consistent get_<resource> pattern in snake_case. Some resources are plural and some singular, but that is natural given the noun being fetched, so the naming is predictable and uniform.
Six tools is a well-scoped set for a market data and portfolio monitoring server. Each tool provides a distinct, useful capability without unnecessary bloat or an overly thin surface.
The server exposes only getter operations: no order placement, cancellation, position closing, or historical account activity. Given the broker/paper trading context implied by the server name and portfolio tools, the lack of any trading lifecycle actions is a significant gap.