reg_rulesTrack US federal rules and proposed rules from the Federal Register by term/agency/significance, with deadline flags (comments_close_soon, effective_soon) and day counts. $0.005/call. Not legal advice.
| Name | Required | Description | Default |
|---|---|---|---|
| term | No | ||
| limit | No | ||
| since | No | ||
| types | No | rule,proposed_rule | |
| until | No | ||
| agency | No | ||
| significant_only | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds value by disclosing cost ($0.005/call) and output specifics (deadline flags, day counts), which are not captured in annotations. No contradiction with annotations exists.
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?
Two sentences with zero filler. The main purpose is front-loaded, and the second sentence delivers cost and disclaimer efficiently. Every clause earns its place.
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?
An output schema exists, so return format is typically covered. However, with no parameter descriptions in the schema, the description must clarify all input semantics. It only covers filters like term/agency/significance, missing limit, since/until (e.g., expected date format), and types. An agent might call it incorrectly without further guidance.
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 0%, so the description bears the burden. It explains three primary filters (term, agency, significant_only) but leaves limit, since, until, and types unexplained. While it partially compensates, it does not fully cover the seven parameters, leaving ambiguity on date formats and type enumeration.
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 tracks US federal rules and proposed rules from the Federal Register, with specific filters (term/agency/significance) and output features (deadline flags, day counts). This distinguishes it from sibling tools focused on finance/tech, leaving no ambiguity about its domain.
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 federal regulation tracking through its domain specification, but it does not explicitly state when to use this tool versus alternatives, nor does it mention any exclusions or alternative tool names. The guidance is indirect rather than explicit.
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.
Each tool targets a distinct data domain: billing/product info, federal funding, federal regulations, SEC filings/events, and research topic analytics. The three technology_* tools are related but differentiated by scope (ranked list, snapshot, deep analysis), so an agent should be able to select correctly with descriptions. Minor potential confusion exists between funding_signals and sec_events since both emit 'signals', but they cover clearly different sources.
All names are snake_case and map to clear domains, but the pattern is not uniform: list_products uses verb_noun, while funding_signals, reg_rules, sec_company, and technology_lookup use noun-style names. The sec_* and technology_* prefixes help navigation, but there is no consistent verb convention across the set.
Nine tools is a well-scoped size for a data-as-a-service server. It includes meta tools (credit, product list) and seven data products without obvious redundancy. Each tool appears to earn its place.
As a read-only data API, full CRUD is not expected, and the server covers billing, product selection, federal money, regulations, SEC events/company snapshots, and emerging tech analytics. The main gap is a lack of a general search/discovery tool, but the specialized endpoints cover their stated purposes well.