list_billsThe curated landmark-bill index — the named, name-searchable AI laws (valid slugs for get_bill). Read from the primary-sourced landmark registry. Free at every tier. Data by AI Law Tracker (CC BY 4.0). Informational only — not legal advice.
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds context about the data source ('primary-sourced landmark registry'), licensing ('CC BY 4.0'), and disclaimers ('not legal advice'), which enhance transparency without contradicting annotations.
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 very concise, using two sentences (effectively one with a dash) to convey purpose, data source, licensing, and disclaimers. It front-loads the key action and is free of unnecessary words.
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 tool with no parameters and no output schema, the description provides sufficient context: it lists landmark bills, explains their use for get_bill, specifies the data source, licensing, and legal disclaimer. Combined with thorough annotations, it fully equips an AI agent to select and invoke this tool correctly.
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 tool has zero parameters, and baseline is 4. The description adds meaning beyond the empty schema by explaining the output contains 'named, name-searchable AI laws' with 'valid slugs for get_bill,' giving the agent context on what the result will contain.
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 curated landmark AI bills, specifying they are 'the named, name-searchable AI laws (valid slugs for get_bill).' This distinguishes it from sibling tools like list_ai_laws, which likely lists all AI laws, by emphasizing the 'landmark' subset and direct usefulness for get_bill.
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 for retrieving valid slugs for get_bill but does not explicitly compare to alternatives like list_ai_laws or state when not to use this tool. It mentions the data is 'free at every tier' and 'informational only,' but lacks explicit guidance on choosing between sibling 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.
Multiple tools are nearly interchangeable: assess_ai_compliance and generate_compliance_report both take a business profile and return risk score plus obligations, and get_sector_detail overlaps with get_ai_obligations/get_ai_penalties. Additionally, list_recent_changes and list_law_feed both describe dataset changes, while list_jurisdictions overlaps with list_countries and list_us_states. An agent cannot reliably pick the right tool without reading fine print.
The set mostly follows a consistent snake_case verb_noun pattern (get_ai_law, list_sectors, search_court_opinions). Minor deviations exist: assess_ai_compliance and generate_compliance_report use different verb styles for near-identical actions, and list_ai_law_news is inconsistent with list_ai_laws. Overall the pattern is still predictable and readable.
With 28 tools, the server exceeds the comfortable range and includes several redundant or overlapping endpoints that could be consolidated. The broad domain justifies a larger surface, but duplicates like assess_ai_compliance/generate_compliance_report and the three jurisdiction listers make the count feel inflated rather than well-scoped.
The tool surface covers the core read-only AI-law workflow well: search and retrieve laws, obligations, penalties, deadlines, sectors, jurisdictions, bills, court opinions, news, and change history. Minor gaps remain, such as no way to fetch a single obligation/penalty record by ID and no separate news-detail endpoint, but these are workable and not dead ends.