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robyroro

romania-mcp

by robyroro
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Company financials (bilanț)

company_financials
Read-onlyIdempotent

Fetch Romanian ANAF financial statements by CUI to verify a company is real and assess revenue, profit, employees, debts, equity and cash year by year.

Instructions

Financial statements a Romanian company filed with ANAF, year by year: revenue (cifra de afaceri), total income and expenses, net profit or loss (net_result), average employees, debts, equity, cash, receivables, inventory and fixed assets. Amounts are in RON. Years with nothing filed are marked filed=false.

Use it to see if a company is real and how it's doing: a shop with zero revenue and no employees, or with negative equity, is worth a closer look. Banks and insurers file a different layout, their lines come through under 'other'. Data starts around 2020 for some companies. Takes about a second per year. Cached for 24h.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cuiYesCompany fiscal code (CUI / CIF), with or without the RO prefix, e.g. "14399840" or "RO14399840".
yearsNoHow many years to fetch, counting back from last year. Statements for a year are filed by mid next year, so the most recent one can still be missing.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.6/5.0
Behavior5/5

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

Beyond the annotations (readOnly, idempotent, openWorld, non-destructive), it discloses currency (RON), the filed=false convention, that banks/insurers use a different layout surfaced under 'other', the ~2020 data horizon, ~1s per year latency, and 24h caching. That is unusually complete behavioral context.

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

Conciseness4/5

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

Front-loaded with the resource and contents, then caveats and performance notes in short sentences. It is slightly long, but each sentence (currency, field list, layout exception, latency, caching) carries distinct information.

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?

With no output schema, the description compensates by enumerating returned fields, the currency, and the filed=false marker, and it flags layout exceptions. An agent has everything needed to call and interpret the result.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3, but the description adds meaning the schema lacks: the data horizon around 2020 and the caveat that the most recent year may be missing. The 'years' semantics are largely already in the schema, so it lands above baseline without being fully additive.

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?

It states a specific resource (Romanian company financial statements filed with ANAF) and enumerates the actual line items returned (revenue, net result, employees, debts, equity, etc.). This is clearly distinguishable from company_lookup, court_cases, exchange_rate and shop_check.

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?

It gives explicit usage context ('use it to see if a company is real and how it's doing') plus interpretation heuristics (zero revenue, no employees, negative equity). It doesn't name sibling alternatives for the basic-identity case, so it stops short of a 5.

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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