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Stratalize Intelligence

by com.stratalize

get_vendor_market_rate

Read-only

Use when a CFO or procurement team needs to know if they are overpaying for any software vendor. Returns monthly_median, monthly_low, monthly_high, annual_median, pricing_model, source, and data_as_of from healthcare vendor benchmark lookups with Stratalize composite medians as fallback when no vendor-specific row exists. Example: Salesforce CRM median ~$8,400/mo — organizations above the monthly_high range are overpaying by a recoverable margin. Source: healthcare_vendor_benchmarks with Stratalize composite medians.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
industryNoOptional industry filter
vendor_nameYesVendor name to look up
company_sizeNoOptional company size segment

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral context: it reveals a fallback mechanism (Stratalize composite medians) and the data source, plus an example interpretation. No contradictions with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

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

The description is concise, front-loaded with the use case, and packs essential information (returned fields, fallback, source, example) into two sentences with no wasted words.

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?

There is no output schema, so the description lists the returned fields and provides an example, making the return format clear. It also covers fallback behavior and data source. It could mention potential limitations or edge cases, but for a lookup tool with this complexity, it is sufficiently complete.

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%, so the schema already documents all three parameters. The description adds no additional parameter-level meaning beyond the schema, which yields the baseline score of 3.

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

Purpose4/5

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

The description clearly states the tool's purpose: to help CFOs/procurement teams determine if they are overpaying for software vendors, and it lists the returned fields. However, it does not explicitly differentiate itself from sibling tools like get_vendor_benchmark or get_software_pricing_intelligence, so it lacks full sibling differentiation.

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?

The description explicitly states when to use the tool ('Use when a CFO or procurement team needs to know if they are overpaying for any software vendor'). It provides clear context but does not mention when not to use it or name alternative tools.

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