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Glama

Diavgis — Greek Public Procurement

by gr.diavgis

Rank Buyers or Suppliers

rank_entities
Read-onlyIdempotent

Rank top Greek public-procurement entities. role=buyer (default) or supplier; by=spend (default — COMMITTED procurement value in EUR, counted ONCE per procurement: deduplicated across sources and lifecycle stages, tenders excluded), single_bidder (count of ≤1-bid awards/contracts) or direct_award (count of απευθείας ανάθεση). Answers 'top 10 suppliers by public revenue', 'which buyers award most without competition'. Returns afm, name, metric, total. Precomputed and refreshed every 6 hours, so it is instant and always the top 15.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
byNodefault spend
roleNodefault buyer
limitNomax 15, default 15 — the published ranking holds the top 15

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
byYes
roleYes
resultsYes

TDQS

A4.8/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the readOnly/idempotent annotations, the description discloses important data behavior: spend is COMMITTED value in EUR, counted once per procurement, deduplicated across sources and lifecycle stages, and tenders are excluded. It also states that results are precomputed and refreshed every 6 hours, making the tool instant and bounded to the top 15.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense and front-loaded, starting with the core purpose and immediately giving defaults and metric semantics. Every clause adds necessary information; there is no filler or repetition of annotations.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the output schema exists and annotations carry the read-only/idempotent profile, the description fully covers what an agent needs: the exact question types, the metric definitions, the defaults, the limit constraint, and the freshness/performance behavior. No meaningful selection or invocation details are missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is already 100%, but the description adds significant meaning: it explains what each 'by' value means, clarifies defaults for role and by, and ties limit to the published top-15 dataset. It also specifies the output fields (afm, name, metric, total), surpassing what the schema alone conveys.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific action ('Rank') and resource ('top Greek public-procurement entities'), then defines the ranking dimensions (role, metric) with concrete defaults and example queries. It clearly separates this tool from spending/lookup tools by focusing on precomputed top-N entity rankings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit usage context with natural-language question examples such as 'top 10 suppliers by public revenue' and 'which buyers award most without competition'. It does not name alternative sibling tools or exclusion cases, but the intended condition — ranking aggregated entity metrics — is clear enough for an agent to select it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Diavgis — Greek Public Procurement toolsInspect
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TDQS

A4.4/5.0
Disambiguation4/5

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.

Naming Consistency4/5

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.

Tool Count5/5

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.

Completeness5/5

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.

Resources