gap_analysis
Identify gaps in the Register — missing fields, incomplete entries, missing exit plans, LEI coverage issues.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Identify gaps in the Register — missing fields, incomplete entries, missing exit plans, LEI coverage issues.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full responsibility for behavioral disclosure. It fails to mention whether the operation is read-only, what output it produces, or if any side effects occur. The agent cannot infer safety or return expectations from this text.
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 single, concise sentence that front-loads the purpose and lists specific gap categories. Every word earns its place with no redundancy or filler.
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?
Although the tool has no parameters and a simple action, the description omits any indication of what the result looks like (e.g., a list, report, or summary). Since there is no output schema, the agent lacks full context on the expected return value, but the purpose is sufficiently clear for a basic call.
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, so no parameter documentation is needed. The schema is vacuous, and the description need not explain any inputs. Baseline 4 applies per rubric for 0-parameter tools.
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 tool's function with a specific verb 'Identify' and resource 'Register', and enumerates distinct gap types (missing fields, incomplete entries, missing exit plans, LEI coverage issues). This distinguishes it from sibling tools like register_stats or get_provider that serve different purposes.
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 implies use when gap analysis is needed but provides no explicit guidance on when not to use it or mentions alternatives. While siblings exist (concentration_risk, validate_roi), no exclusions are noted. This is minimal but not misleading.
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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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.