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Stratalize Intelligence

get_vendor_risk_signal

Read-only

Use when screening a vendor for financial instability or procurement risk before a long-term contract commitment. Returns a risk score from 0 to 1 with risk indicators and negative mention evidence. Example: Vendor X scores 0.72 risk — indicators: customer churn citations, pricing disputes, product roadmap uncertainty — HIGH risk classification, recommend short-term contract only. Source: Stratalize citation risk composite.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
vendor_nameYes

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already indicate read-only, but the description adds substantial context: return format (score 0-1), risk indicators, negative mention evidence, classification, and recommendation. The example illustrates how to interpret the output, which goes well beyond the annotation's safety profile.

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 moderately detailed but every sentence adds value: use case, output, example, and source. It is front-loaded with the trigger scenario. It could be trimmed slightly, but overall it is well-structured and not wasteful.

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

Completeness5/5

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

For a simple one-parameter, read-only tool with no output schema, the description covers purpose, output format, use case, and interpretation. It is complete enough for an agent to select and invoke the tool correctly without further guidance.

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

Parameters4/5

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

The schema has a single vendor_name parameter with no description, so the description must compensate. It indirectly defines the parameter through the use case and example ('Vendor X'), making it clear that the value is the vendor's name. However, it lacks explicit detail on format or matching rules, which would elevate it further.

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 clearly states the tool's function: screening a vendor for financial instability or procurement risk. It specifies the output (risk score 0-1, indicators, evidence) and provides a concrete example, making it distinct from sibling tools like get_vendor_benchmark.

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 says 'Use when screening a vendor for financial instability or procurement risk before a long-term contract commitment,' which gives a clear trigger context. It does not name alternatives, but the use case is specific enough to guide selection.

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.

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