Search Kalshi prediction markets by keyword, series, or ticker. Returns live prices and volume — data not available via web search. Free-tier and rate-limited.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | Keyword search (e.g. "oil recession") | |
| market | No | Specific market ticker (e.g. KXWTIMAX-26DEC31-T140) | |
| series | No | Kalshi series ticker (e.g. KXWTIMAX) |
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?
With no annotations provided, the description carries the full burden. It discloses that the tool returns live prices/volume, that the data isn't available via web search, and that it's free-tier and rate-limited. These are important behavioral traits beyond the schema, though it could also have mentioned read-only behavior or edge cases like no results.
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 with no fluff. The first sentence states the core purpose, and the second adds valuable context about data availability and limits. It is front-loaded and every word earns its place.
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 tool with no annotations and no output schema, the description is reasonably complete: it covers search inputs and high-level return content. However, it doesn't mention that at least one of the three parameters should be supplied, nor describe the result structure or possible errors, leaving some gaps for an agent to infer.
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 groups the parameters by 'keyword, series, or ticker', which aligns with query, series, and market, but adds no additional semantic meaning beyond what the schema already provides. It doesn't clarify interactions or requirements between parameters.
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 Kalshi prediction markets by keyword, series, or ticker, which is a specific verb+resource+scope. It doesn't explicitly differentiate from sibling tools like screen_markets or get_markets, but the focus on search by these specific fields and returning live prices/volume makes it distinct enough.
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 provides clear context on when to use this tool: to obtain live prices and volume not available via web search, and it warns about rate limits. It doesn't name alternatives explicitly, but the context is sufficient for an agent to decide when this search tool is appropriate.
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.
Many tools have overlapping purposes, such as multiple market query tools (scan_markets, screen_markets, get_market_detail, get_market_diff, get_market_history, inspect_ticker) and legislative tools (legislation, get_legislation, list_legislation, query_gov). Aliases like get_heartbeat_config/get_heartbeat_status and explore_public/explore_theses add further confusion. An agent would struggle to select the correct tool without deeply reading each description.
Most tools follow a verb_noun pattern (get_, list_, create_, update_), but there are notable deviations: 'legislation' lacks the 'get_' prefix, 'stt' and 'tts' are acronyms, 'monitor_the_situation' is a full phrase, and 'x_account/x_news/x_volume' use a non-standard prefix. The overall style is readable, but the mixed conventions reduce predictability.
108 tools is extreme for any server, even one covering prediction markets, trading, portfolio management, forum, skills, and speech. The massive surface area overwhelms agents and makes the server feel more like a platform than a coherent toolkit. This many tools inevitably leads to redundancy and maintenance burden.
The server covers an impressively broad domain: market data, thesis management, intents, strategies, positions, portfolio, forum, skills, legislative and economic queries, and audio/visual processing. Minor gaps exist (e.g., no delete for skills/theses, no update for some portfolio items) but core workflows are well-supported. Overall lifecycle coverage for most entities is strong.
