Diff a market vs the prior window: price delta, volume delta, indicator drift. For "what changed in the last 6h?" questions.
| Name | Required | Description | Default |
|---|---|---|---|
| sort | No | Sort field (e.g. priceDelta) | |
| topic | No | Topic keyword if no tickers | |
| window | No | Lookback (default 24h) | |
| tickers | No | Comma-separated tickers |
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 transparency burden. It discloses the core behavior (computing price/volume/indicator deltas vs a prior window) but does not state output format, default window behavior, or explicitly confirm the operation is read-only (though 'get' implies it). This is adequate but leaves room for more detail.
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 short sentences, front-loaded with the action and outcome. Every phrase adds value: the action, the comparison basis, the output dimensions, and a natural-language use case. No filler or redundancy.
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?
The tool has 4 optional parameters and no output schema, so the description needs to stand in for return-value documentation. It does list the output dimensions and a use case, but it does not clarify how to specify the target market (tickers vs topic) or how window/sort affect the results. This is a moderate gap for a tool of this complexity.
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 adds contextual phrasing like 'prior window' and a use case, but it does not explain how tickers, topic, window, and sort interact or which combinations are valid. The schema descriptions themselves are the main source of parameter meaning.
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 uses a specific verb 'Diff' and a specific resource 'a market vs the prior window', and enumerates concrete outputs (price delta, volume delta, indicator drift). This clearly distinguishes it from related tools like get_market_history or get_market_detail, which focus on absolute values rather than deltas.
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 an explicit trigger example: 'For "what changed in the last 6h?" questions.' This gives clear context for when the tool is appropriate. It does not mention alternatives or exclusions, but the use case is strong enough to guide an agent.
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.
