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Glama

Leadgen ONRC Romania

by com.adrianhomelab
OverviewAdmin

lookup_financials

Return a Romanian company's annual financial statements (MFP data).

Data source: official Ministry of Finance 'situatii financiare' open data
(data.gov.ro), loaded locally by load_financiare.py. Values are RON
integers from the company's latest annual filing(s). Free tier returns
registry identity; this is the paid-tier financial enrichment.

Args:
    cui: Romanian tax identification number (CUI), e.g. "2816464".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cuiYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Discloses data source, value format (RON integers), recency (latest annual filing), and paid/free behavior. Does not describe error handling for invalid CUI or missing filings, but the current description provides meaningful behavioral expectations.

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 concise yet informative, including data source, value type, tier behavior, and parameter explanation without unnecessary detail. Each sentence adds relevant context.

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

Completeness4/5

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

Explains the core return content and value format, which is important because there is no output schema. It does not list specific financial fields or error cases, but the description gives enough context for typical usage.

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?

The single parameter cui is fully explained in the Args section as a Romanian tax identification number, with a concrete example. This covers the parameter despite the schema itself having only a title.

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?

States a specific action ('Return') and resource ('Romanian company's annual financial statements'), with a clear distinction from free registry identity. The sibling lookup_financial_ratios is not confused because this is explicitly about full financial statements from MFP data.

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?

Mentions when the paid tier is needed and that free tier only returns registry identity. It does not explicitly compare against sibling tools like lookup_financial_ratios, but the paid-tier note gives practical usage context.

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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