Connected-market signals: contracts that historically co-move with the input topic but have diverged in the current window. Surfaces "this market should have moved but didn't" trades.
| Name | Required | Description | Default |
|---|---|---|---|
| topic | No | Topic keyword (fed, election, ai) | |
| window | No | Lookback (e.g. 24h, 7d) |
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 burden. It explains the conceptual behavior (co-movement and divergence) but does not disclose what the response contains, edge cases (e.g., no signals found), or any limitations. The description implies a read-only analysis tool but lacks explicit behavioral details like return format or pagination.
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 long, front-loaded with the key concept, and contains no fluff. Every word adds value, defining the signal type and the trading idea it surfaces. Highly 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?
Given the complexity of the contagion concept and the lack of an output schema, the description provides a good conceptual overview but does not specify the structure of results (e.g., list of market IDs with scores) or behavior under edge conditions. It is adequate but leaves room for more detail in a tool with no annotations or output schema.
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% for both parameters (topic and window), so the baseline is 3. The description does not add any parameter-level detail beyond what the schema already provides. It does not explain how they interact, but the schema descriptions are already sufficient for basic understanding.
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: it surfaces connected-market signals showing contracts that historically co-move with the input topic but have diverged in the current window. It uses a specific concept ('this market should have moved but didn't' trades) and differentiates from siblings like get_trade_ideas or scan_markets by focusing on divergence from historical co-movement.
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 implies a clear use case: finding divergent markets based on historical co-movement. It does not explicitly name alternatives or state when not to use it, but the 'this market should have moved but didn't' phrasing provides contextual guidance. Lacks explicit exclusions or alternative tool references, but sufficient for a straightforward signal tool.
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.
