entity_counterpartiesTop counterparties of an entity (ΑΦΜ): for a buyer, the suppliers it pays most; for a supplier, its top public buyers — with act counts and EUR totals. The 'who does business with whom' due-diligence view.
| Name | Required | Description | Default |
|---|---|---|---|
| afm | Yes | 9-digit ΑΦΜ | |
| limit | No | max 30, default 10 |
| Name | Required | Description | Default |
|---|---|---|---|
| afm | Yes | ||
| counterparties | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds meaningful context beyond this by explaining the buyer/supplier distinction, the 'top' selection criteria, and the inclusion of act counts and EUR totals, which are not covered by the annotations.
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 no filler. The first sentence front-loads the core purpose, and the second adds a clarifying explanatory phrase. Every part contributes to understanding.
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 the simple tool, strong annotations, and existence of an output schema, the description is complete. It covers the essential logic (directional counterparties), the output attributes (counts and totals), and the use case, leaving no significant gaps.
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?
Schema coverage is 100% for both parameters (afm and limit), with descriptions already provided. The description adds no additional parameter-level semantics beyond what the schema contains, so the baseline score of 3 applies.
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 that the tool retrieves top counterparties of an entity, with explicit directionality (buyer vs supplier) and output contents (act counts, EUR totals). This distinguishes it from sibling tools like get_entity or rank_entities by focusing specifically on business relationships.
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 a due-diligence use case ('who does business with whom') but does not explicitly state when to use this tool versus alternatives, nor does it mention exclusions. Usage guidance is mostly implied rather than explicitly articulated.
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 distinct concern: entity resolution, entity profiles, counterparties, rankings, spend aggregates, search, act detail, lifecycle chains, expiring contracts, and quota. Minor overlap exists between get_act and get_chain since get_act can include lifecycle chain data, but the descriptions make the intended primary use clear.
All names use snake_case and most follow a verb_noun pattern like find_entity, get_entity, search_tenders, and rank_entities. A few tools such as entity_counterparties and expiring_contracts are noun-phrase names, and spend_by_cpv uses a different structure, so the pattern is mostly consistent but not uniform.
Ten tools is well-scoped for a Greek public procurement data API: the count is large enough to cover discovery, search, entity analytics, spend analytics, lifecycle detail, and quota management without feeling bloated. Each tool appears to earn its place with no obvious redundant duplicates.
The tool surface covers the full read-only procurement workflow: resolve names to tax IDs, profile entities, find counterparties, rank entities, search tenders with filters, inspect individual acts, follow lifecycle chains, monitor expiring contracts, and aggregate spend by CPV. There are no major missing operations for the stated purpose.