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Stratalize Intelligence

by com.stratalize

get_salary_benchmark

Read-only

Use when setting compensation ranges, evaluating a job offer, or preparing a comp committee presentation for any role. Returns p25, p50, p75 wage estimates with state and industry adjustments across 50+ role families. Example: Software engineer in Illinois — p25 $98K, median $127K, p75 $158K — organizations benchmarking above p75 retain 34% fewer departures in competitive talent markets. Source: BLS Occupational Employment Statistics, latest release.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stateNoTwo-letter US state code
industryNoe.g. saas, healthcare, legal, financial_services, manufacturing, retail
job_titleYese.g. Software Engineer, CFO, Account Executive, Data Scientist, HR Manager

TDQS

A4.1/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds meaningful behavior: it returns percentile-based estimates, supports state/industry adjustments, gives an illustrative example, and cites the BLS source. This goes beyond 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with usage context and remains focused, but the final sentence about retention percentages is tangential to tool invocation and the source attribution could be shortened. Still, every sentence earns some place and there is no fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only benchmark tool with no output schema, the description covers purpose, output percentiles, an example, adjustment dimensions, and the data source. It does not specify the exact response structure, but the described outputs are sufficient for an agent to understand how to use and interpret the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% and each parameter already has clear descriptions/examples (e.g., state as 'Two-letter US state code', industry with enumerated examples, job_title with examples). The description reinforces that state and industry are adjustment dimensions but adds no new syntactic or formatting guidance beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with 'Use when...' and clearly states the tool returns p25, p50, p75 wage estimates with state and industry adjustments, making its purpose specific and distinct from siblings like get_company_salary_disclosure. The verb 'Returns' plus the resource (salary benchmark) anchors it well.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly lists three use cases: setting compensation ranges, evaluating a job offer, and preparing a comp committee presentation. It does not name alternatives or when-not-to-use, but the context is clear and actionable.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

B3.4/5.0
Disambiguation2/5

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.

Naming Consistency4/5

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.

Tool Count2/5

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.

Completeness3/5

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.

Resources