Regulatory watch (bundle) — One call: the most recent US Federal Register documents (rules, proposed rules, notices) matching a topic — with agency, type, date, and abstract. Optional agency filter. For compliance monitoring. JSON. Price: $0.03 USDC (Base, via x402).
| Name | Required | Description | Default |
|---|---|---|---|
| topic | Yes | search topic, e.g. 'stablecoin' or 'PFAS' | |
| agency | No | optional agency slug, e.g. securities-and-exchange-commission |
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?
Annotations already signal read-only and non-destructive behavior. The description adds useful behavioral context: it returns the 'most recent' matching documents, includes agency/type/date/abstract, returns JSON, and has a specific price. This supplements the annotations without contradicting them.
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 compact and front-loaded, immediately stating the tool's value ('One call') and core output. Every sentence adds useful information including source, fields, optional filter, use case, format, and price, with no wasted 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?
Given no output schema, the description adequately covers what the tool returns and its purpose. It lacks detail on pagination, result limits, or the exact time window for 'most recent,' but these are minor gaps for a simple 2-parameter read-only tool.
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%, so the schema already documents both 'topic' and 'agency' with examples. The description only restates the optional agency filter and topic matching; it does not add meaningful parameter semantics beyond the schema.
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 identifies a specific resource (recent US Federal Register documents), a specific scope (matching a topic), and the returned fields (agency, type, date, abstract). This clearly distinguishes it from sibling tools focused on SEC filings, FDA labels, or sanctions screening.
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 states a clear use case: 'For compliance monitoring.' It also indicates the data source and that an optional agency filter is available, giving an agent enough context to select this tool. It does not explicitly name alternatives or exclusions, but the intended context is clear.
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 are clearly separated by prefix and data source, but several bundled products overlap heavily: vehicle_deal_check vs vehicle_report, realestate_property_report vs realestate_site_risk, finance_company_360 vs finance_health_scan, and domain_due_diligence vs email_domain_check/business_vet. An agent would frequently struggle to pick the correct premium bundle.
Tool names overwhelmingly follow a consistent snake_case category-prefix pattern like weather_, crypto_, vehicle_, finance_, and geo_. Minor deviations such as bare names (domain, ip) and noun-verb forms (dns_lookup, url_check) are easy to learn and don't create real confusion.
50 tools is far beyond the typical well-scoped 3–15 range and will require heavy filtering to navigate. The broad multi-domain data marketplace partially justifies the size, but it would be more coherent split into per-domain servers or consolidated further.
For a read-only data/diligence marketplace, the surface is quite comprehensive: weather, vehicle, crypto, SEC/finance, domain/email, sanctions, and geo workflows all have core operations plus fused verdict bundles. Minor gaps exist—such as a simple crypto price lookup or vehicle market value—but agents can usually work around them.