Liquidity-weighted yield curves across event types (e.g. KXFED 6mo, KXBTC 30d). For "where on the curve am I trading?" questions.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max events | |
| venue | No | kalshi or polymarket | |
| minPoints | No | Minimum curve points to keep an event |
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 present, so the description bears full responsibility for behavioral disclosure. It adds the detail that curves are 'liquidity-weighted,' which is a non-obvious behavioral trait, but it doesn't mention output format, parameter effects, defaults, or any limitations. For a getter tool the risk is lower, but the description still remains thin.
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 primary purpose and immediately followed by a practical use-case. There is no redundant or filler content; every phrase carries weight.
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?
With no output schema and no annotations, the description should explain more about the return structure and how the optional parameters influence the output. It gives a high-level idea of the content (liquidity-weighted yield curves) and examples of event types, but it does not clarify what the returned data looks like or the effect of limit/venue/minPoints. It is adequate but not thorough.
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?
Input schema covers all three parameters (limit, venue, minPoints) with descriptions, achieving 100% coverage. The description provides examples of event types (e.g., KXFED 6mo, KXBTC 30d) that indirectly clarify what an event type is, but it does not explain how parameters like venue or minPoints affect results. Thus it adds little beyond the schema, so baseline 3 applies.
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 identifies the tool's output as 'Liquidity-weighted yield curves across event types' and provides concrete examples. It specifies both the resource (yield curves) and scope (across event types), but does not explicitly distinguish it from the sibling tool 'get_yield_curve' (singular).
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 phrase 'For "where on the curve am I trading?" questions' provides a clear usage context. However, it does not mention alternatives or when not to use this tool, so it falls short of a 5.
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.
