Return the full list of currently unreliable Czech VAT payers from ADIS. WARNING: response can be 50–100 MB (tens of thousands of entries). Intended for daily mirroring into a local database, not for ad-hoc inspection. For "is this specific company unreliable?" use check_dph_payer instead.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, covering safety and data volatility. The description adds critical behavioral context with the WARNING about 50–100 MB response sizes and large entry counts, which is valuable for an agent deciding whether to invoke it. It could also mention lack of pagination, but without an output schema, the warning suffices for the tool's simple nature.
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 two sentences, with the core action first, followed by a critical warning and usage guidance. Every sentence earns its place: purpose, size warning, intended use, and alternative tool. There is no filler or repetition of structured data.
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?
The tool is a zero-parameter list operation with no output schema. The description covers what it returns (full list), the source (ADIS), the scale (50–100 MB), the recommended usage (daily mirroring), and the alternative for targeted checks. Given the simplicity and existing annotations, this is complete and well-rounded.
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?
There are 0 parameters, and the schema reflects this with an empty properties object. The described behavior (returning the full list) is the only parameter-related context needed. With no params, the baseline is 4, and the description does not need to explain any parameter syntax or semantics.
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 opens with the specific verb 'Return the full list of currently unreliable Czech VAT payers from ADIS', which clearly states the action, resource, and scope. It also differentiates from siblings by naming check_dph_payer for targeted lookups, making the purpose unmistakable.
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?
It explicitly states the intended use: 'Intended for daily mirroring into a local database, not for ad-hoc inspection.' It also provides a direct alternative: 'For "is this specific company unreliable?" use check_dph_payer instead.' This is ideal when-to-use and when-not-to-use 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 serves a clearly distinct purpose: single-subject detailed check, bulk reliability screening without personal data, and full list mirroring for unreliable payers. There is no overlap or ambiguity between the three operations.
All tool names follow a predictable verb_noun pattern in snake_case (check_*, list_*), but the object naming varies slightly: 'bulk_dph_payer' vs 'dph_payer' vs 'unreliable_payers'. This is a minor deviation and does not hinder readability.
With 3 tools, the server is well-scoped for its niche domain. It offers a single-check, a bulk-check, and a list operation, covering all necessary entry points without unnecessary bloat.
The server covers the full lifecycle of working with Czech VAT payer reliability data: individual verification, bulk screening, and full registry access for mirroring. No obvious gaps in functionality are apparent.