BEST FOR QUESTIONS. Ask any question about probabilities or future events. Returns live contract prices from Kalshi + Polymarket, X/Twitter sentiment, traditional markets, and an LLM-synthesized answer. Free-tier and rate-limited; API keys unlock higher limits.
| Name | Required | Description | Default |
|---|---|---|---|
| q | Yes | Natural language query (e.g. "iran oil prices", "fed rate cut 2026", "recession probability") |
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 of behavioral disclosure. It mentions rate limits and the free-tier/API-key distinction, which is useful. However, it does not explicitly say the tool is read-only or describe any side effects, though 'query' implies a read-only operation. The output composition is explained adequately.
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 concise and front-loaded with 'BEST FOR QUESTIONS' and immediately explains the purpose. It packs a lot of information into a few sentences. Minor marketing language like 'BEST FOR' is slightly redundant but not harmful. Overall, it's well-structured and efficient.
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 single-parameter query tool with no output schema, the description covers the key aspects: what it does, the data sources, the synthesized answer, and rate limits. It does not detail the exact return structure, but the high-level description is sufficient for a general-purpose query tool. No missing critical 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 input schema already provides a clear description of the 'q' parameter with examples, and schema coverage is 100%. The tool description adds broader context about the nature of the question (probabilities or future events) but does not significantly enhance parameter semantics beyond the schema. Baseline 3 is appropriate.
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's function: ask any question about probabilities or future events and get synthesized answers from multiple sources. The verb 'ask' and specific resource (probabilities/events) make it clear, but it does not explicitly distinguish itself from sibling tools like get_answer or get_forecast, though the multi-source aggregation is implied.
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 gives clear context for when to use the tool: for any question about probabilities or future events. It does not explicitly mention alternatives or exclusions, but 'BEST FOR QUESTIONS' and the broad scope serve as a strong usage signal. The rate-limit note adds practical context.
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.
