Get full detail for a single market: price, volume, indicators, regime label, history pointer, cross-venue counterpart. Lower-level than inspect_ticker — raw JSON only.
| Name | Required | Description | Default |
|---|---|---|---|
| depth | No | Orderbook depth levels to include (0 = none) | |
| ticker | Yes | Market ticker |
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, the description carries the burden of behavioral disclosure. It states that the tool returns raw JSON only and is lower-level, which adds context not in the schema. However, it does not disclose potential side effects (though 'get' implies read-only), permissions, error behavior, or output format details beyond the field list, so transparency is only partially covered.
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 only two sentences, front-loaded with the main action, and every word earns its place. It efficiently conveys purpose, scope, and differentiation without unnecessary detail.
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 retrieval tool with 100% schema coverage, the description is mostly complete. It lists the key content fields and offers a sibling comparison, but it omits mention of the 'depth' parameter (covered by schema) and does not describe what the 'history pointer' or 'cross-venue counterpart' actually return. Still, the tool is straightforward enough that the description is adequate.
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 schema already provides 100% coverage for both parameters: 'ticker' is described as 'Market ticker' and 'depth' as 'Orderbook depth levels to include (0 = none)'. The description does not add any additional meaning to these parameters beyond the schema, so it relies on the structured schema as baseline.
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 gets full detail for a single market and enumerates specific content (price, volume, indicators, regime label, history pointer, cross-venue counterpart). It also distinguishes itself from the sibling 'inspect_ticker' by noting it is lower-level, which helps differentiate from related tools.
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 a clear context for when to use this tool: when raw JSON-level detail for a single market is needed, and explicitly contrasts it with the higher-level 'inspect_ticker'. However, it does not mention exclusions or alternative tools like 'get_markets' or 'get_market_history', so it is not fully explicit but still provides useful guidance.
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.
