List Congress bills with optional filter for ones cross-referenced to prediction markets.
| Name | Required | Description | Default |
|---|---|---|---|
| q | No | Keyword | |
| limit | No | Max rows | |
| congress | No | Congress number (e.g. 119) | |
| hasMarket | No | Only bills with a linked market |
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 burden. It mentions the filter for prediction markets but does not disclose pagination, default limits, return format, or whether results are sorted or include full bill text. This is a gap for a list tool, as users would not know what to expect in the response.
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 a single, concise sentence that front-loads the primary action (List Congress bills) and the optional filter. Every word earns its place, with no irrelevant details or repetition.
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 list tool with 4 optional parameters and no output schema, the description provides the essential purpose. However, without annotations or any mention of return value, pagination, or default behavior, it is not fully complete. The absence of output schema means the description should hint at what is returned, which it does not.
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% with each parameter (q, limit, congress, hasMarket) having a clear description. The description adds context by linking 'hasMarket' to 'prediction markets', which slightly clarifies the tool's purpose but does not significantly enhance the parameter semantics beyond what the schema provides.
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 lists Congress bills, with an optional filter for those cross-referenced to prediction markets. This specific verb+resource combination distinguishes it from sibling tools like get_legislation (likely for a single bill) and list_congress_members (lists members, not bills).
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 usage when you need a list of Congress bills, and the optional filter suggests when to use the hasMarket parameter. However, it does not explicitly mention alternatives or when not to use this tool, leaving room for ambiguity with other list tools.
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.
