concentration_risk
Analyze ICT concentration risks across providers, countries, cloud types. DORA Art. 29 requires assessment of ICT concentration risk.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Analyze ICT concentration risks across providers, countries, cloud types. DORA Art. 29 requires assessment of ICT concentration risk.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations provided, so the description carries the full burden for behavioral disclosure. It only says 'Analyze', which implies a read/compute operation, but does not describe return format, side effects, data sources, or any operational details. With such sparse behavioral info, the agent lacks critical context for invoking the tool.
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 a tight two sentences. The first delivers the core purpose, the second adds regulatory relevance without redundancy. No filler or repetition, making it highly efficient.
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 there is no output schema and no annotations, the description must explain what the tool returns or how results are presented. It merely states 'Analyze ICT concentration risks' without describing the output type, format, or any operational constraints. For a no-input tool, this leaves a significant gap in what to expect.
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?
The tool has zero parameters, and the input schema is empty with 100% schema coverage vacuously. Per the rubric, 0 params gives a baseline of 4. The description adds value by indicating the analysis scope (providers, countries, cloud types), which clarifies what the no-parameter tool operates on.
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 a specific verb ('Analyze') and resource ('ICT concentration risks') with defined scope ('across providers, countries, cloud types'). This distinguishes it from siblings like get_provider or list_providers, which are retrieval tools, and even from gap_analysis or validate_roi, which focus on different compliance aspects.
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 provides a clear context for when to use the tool by citing 'DORA Art. 29 requires assessment of ICT concentration risk', implying this tool is for regulatory compliance. However, it does not explicitly state when not to use it or name alternative tools, so it stops short of full usage guidance.
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.
Each tool targets a clear, distinct function: CRUD operations, analytics, validation, and export. There is no meaningful overlap between gap_analysis and validate_roi—one identifies data quality gaps while the other formally checks ITS compliance.
Most tools follow a verb_noun pattern (list_providers, get_provider, export_its, validate_roi), but a few use noun_noun or noun_verb forms (concentration_risk, gap_analysis, ctpp_check, health_check). The naming is readable and consistent in style (snake_case), but not uniformly verb-led.
The 10 tools are well-scoped for a DORA Register of Information domain, covering list/get/register, multiple analysis perspectives, validation, export, and health check. Each tool earns its place without redundancy.
The surface covers the core lifecycle (create, read, update via register_provider, list, export, validate) and adds useful analysis tools. The only notable gap is a delete_provider tool for removing deprecated or erroneous entries, but this is a minor omission.