Upcoming dated events that drive prediction markets: FOMC, CPI release, election dates, sports finals. Returns date, topic, and linked tickers.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Lookahead days (default 30) | |
| category | No | Category filter (econ, election, sports, geo) |
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?
No annotations are provided, so the description carries the full disclosure burden. It accurately describes a read-only operation and lists return fields, which is basic transparency. However, it doesn't disclose potential side effects, rate limits, or nuances like how 'linked tickers' are derived or whether the calendar is manually curated versus automatically generated. The default lookahead is left to the schema, and no behavioral caveats are mentioned.
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 core purpose, and includes examples to clarify scope. Every word earns its place; no wasted or redundant content. This is excellent conciseness.
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 simple tool with two optional parameters and no output schema, the description provides sufficient context: what it returns, what types of events, and category examples. It slightly misses an explicit statement about the default lookahead (though present in the schema) and clear differentiation from similar sibling tools, but these are minor gaps given the tool's simplicity.
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 coverage is 100%: both 'days' and 'category' have descriptions in the schema. The description adds no new semantic detail beyond what the schema already provides. It mentions examples that align with the category filter but doesn't elaborate on value formats or constraints. Baseline 3 is appropriate when the schema carries the parameter load.
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 what the tool does: it returns upcoming dated events that drive prediction markets, with examples (FOMC, CPI, elections, sports). It specifies return fields (date, topic, linked tickers), giving a clear picture of the resource. However, it doesn't differentiate from the sibling tool 'get_schedule', which could serve a similar purpose.
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?
There is no guidance on when to use this tool versus alternatives like get_schedule, get_briefing, or get_milestones. The description implies use for market-moving events but doesn't state exclusions, prerequisites, or preference criteria. This leaves the agent without a clear selection rationale.
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.
