health_check
Server and data status.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Server and data status.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full responsibility for disclosing behavior. It only states 'Server and data status', which vaguely implies a read-only health inspection but does not confirm whether the tool modifies anything, what metrics it reports, or whether it requires special permissions. This is a significant transparency gap.
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 extremely concise, using only seven words with no repetition. However, it is under-specified to the point of being a fragment rather than a clear sentence. It lacks a verb and any context, so while there is no wasted text, the brevity reduces usefulness.
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?
With no annotations, no output schema, and only a three-word description, the tool is insufficiently specified. The agent cannot infer what the health check will return, how to interpret 'server and data status', or whether any side effects occur. A health-check tool typically needs to explain the scope and format of its output, which is absent here.
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 input schema is empty with zero parameters, so per the rubric the baseline is 4. There are no parameter names or descriptions to elaborate on, and the schema already fully covers this (100% coverage).
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 'Server and data status' clearly identifies the resource being examined (server and data) and implies a check/status report. It is distinct from sibling tools, which focus on providers, risk, and other domain-specific operations. However, it lacks an explicit verb like 'retrieves' or 'checks', which prevents a higher score.
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 no guidance on when to use this tool versus alternatives. There is no mention of typical scenarios, prerequisites, or exclusions. Sibling tool names suggest different purposes, but the description does not clarify when health_check is the appropriate choice.
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.