Search legislative data: bills, nominations, members, CRS reports. Cross-references with prediction markets. Use for political/policy questions.
| Name | Required | Description | Default |
|---|---|---|---|
| q | Yes | Search query (e.g., "save act", "fed chair nomination") | |
| mode | No | raw = skip LLM synthesis, full = include answer | full |
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 behavior disclosure. It discloses a notable behavior: 'Cross-references with prediction markets,' which adds context beyond a simple search. However, it does not mention the LLM synthesis behavior hinted by the 'mode' parameter, nor any other operational details like rate limits or data freshness. This is adequate but not comprehensive.
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 extremely concise: three short sentences that front-load the primary action and follow with usage guidance. Every word earns its place, with no redundant or vague filler. This is an ideal level of conciseness.
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 no output schema, so the description should clarify what the response looks like, but it does not mention return format or that 'full' mode includes a synthesized answer (a behavior partly in the schema). Given the moderate complexity (2 params, no annotations), the description covers the basic use case but leaves gaps around output and operational context. This is a passable but not fully complete description.
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 descriptions for both parameters (q and mode) with 100% coverage, so the baseline is 3. The description adds general context about the search domain but does not elaborate on parameter syntax or specific values beyond what the schema supplies. This fits the baseline exactly.
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 searches legislative data, listing specific content types (bills, nominations, members, CRS reports). It uses a specific verb 'Search' and provides resource scope. However, it does not explicitly differentiate from sibling tools like get_legislation or list_legislation, so it stops short of a 5.
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 gives a clear usage context: 'Use for political/policy questions.' This implies when the tool is appropriate but does not mention alternatives or when not to use it. Sibling tools such as query_econ or get_legislation exist, so explicit exclusions would improve this dimension.
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.
