OSHA inspection and violation history for a named employer — total inspections, violations, cumulative penalties, and violation type breakdown. Source: DOL Enforcement Data. Use in vendor diligence, M&A, or ESG risk. Cryptographically attested with a post-quantum signed settlement receipt. Verify at trust.stratalize.com/verify.
| Name | Required | Description | Default |
|---|---|---|---|
| employer_name | Yes |
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 meaningful behavioral context by citing the data source ('DOL Enforcement Data') and the post-quantum signed settlement receipt with a verification URL, which goes beyond a typical read operation.
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 three sentences long, front-loaded with the core purpose, and every sentence adds value: purpose, source/use case, and verification mechanism. No redundant or vague wording.
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?
Despite the lack of an output schema, the description lists the key return components (inspections, violations, penalties, violation type breakdown) and provides context on data source and verification. For a single-parameter read-only tool, this is sufficiently complete.
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%, but the single parameter (employer_name) is directly clarified by the phrase 'for a named employer' in the first sentence. The description provides enough semantic context for the agent to understand what value to supply, even without a schema description.
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 verb ('Get') and resource ('OSHA enforcement data'), and enumerates specific outputs ('total inspections, violations, cumulative penalties, and violation type breakdown'). This distinguishes it from sibling tools like get_workplace_safety_benchmark or get_employer_h1b_wages.
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 explicitly names use cases ('Use in vendor diligence, M&A, or ESG risk'), providing clear context for when to invoke this tool. It does not explicitly mention when not to use it or name alternative tools, but the context is unambiguous.
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 overlap significantly: get_vendor_benchmark and get_vendor_market_rate both return pricing benchmarks with median/low/high; get_industry_spend_benchmark, get_industry_spend_profile, get_category_spend_benchmark, and get_spend_by_company_size all address spend benchmarking; get_saas_market_intelligence, get_category_ai_leaders, get_sector_ai_intelligence, and get_market_intelligence_brief all cover AI citation and market themes. These overlapping purposes make tool selection ambiguous.
All tools follow the 'get_' prefix consistently, creating a predictable pattern. However, the object naming is inconsistent in ordering (e.g., get_category_ai_leaders vs get_top_vendors_by_category) and some use 'synthesis' vs 'signal' vs 'benchmark' without a clear rule. Overall, the pattern is readable and consistent.
With 45 tools, the surface is extremely large. While the server's scope is broad (market intelligence, vendor benchmarks, regulatory data, etc.), this count overwhelms an agent and dilutes focus. Many related tools could be consolidated (e.g., vendor benchmarking into one tool with modes). A typical well-scoped server would be 3-15 tools.
The server covers numerous domains with read-only intelligence, including market trends, vendor pricing, compensation, regulatory, and patent data. However, there are gaps within those domains: no historical trend comparison, no side-by-side vendor comparison across multiple metrics beyond alternatives, and no write or action capabilities. The breadth is impressive, but the depth is uneven.