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Leadgen MCP

by io.github.darksider4all

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

TDQS

A3.9/5.0
Behavior3/5

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

No annotations are provided, so the description carries full responsibility. It discloses the data source (official Ministry of Finance open data, loaded locally by load_financiare.py), that values are RON integers from the latest annual filing(s), and that it's paid-tier. However, it does not mention error handling, pagination, or explicitly confirm read-only behavior (though 'Return' implies it).

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?

The description is well-structured: it starts with the purpose, then gives data source context, tier differentiation, and a clean Args section. It is not overly verbose, but every sentence adds value (source, currency, tier).

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

Completeness2/5

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

There is no output schema, so the description should explain what the tool returns. It only says 'annual financial statements (MFP data)' without specifying fields, structure, or whether it's a single object or list. It mentions RON integers but leaves the response format largely undefined for a paid-tier enrichment tool.

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 description coverage is 0%, so the description must compensate. It does so with an Args section explaining 'cui' as a Romanian tax identification number and providing an example ('2816464'), which fully clarifies the parameter's meaning beyond the bare schema.

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 states the specific action ('Return a Romanian company's annual financial statements (MFP data)') and names the resource (MFP data from the Ministry of Finance). It also clarifies it is the paid-tier financial enrichment, distinctly different from the sibling lookup tools (contacts, business, director, domain).

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 explicitly differentiates the free tier (returns registry identity) from this paid-tier financial enrichment, giving clear context for when to use it. It does not name sibling tools directly, but the financial scope implies this is the go-to tool for financial statements rather than the other lookup categories.

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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TDQS

A4/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: web contact extraction, company registration lookup, director search, domain intelligence, and financial statements. Even the two ONRC-based tools differ in query type (name/CUI vs director name), eliminating ambiguity.

Naming Consistency4/5

Four of five tools follow the consistent 'lookup_' prefix with a clear noun (business, director, domain, financials). The fifth, 'extract_contacts', uses a verb-noun pattern but with a different verb. This is a minor deviation and still readable, but slightly breaks the uniform style.

Tool Count5/5

With exactly 5 tools, the set is well-scoped for a lead-generation server. Each tool covers a distinct aspect of lead research without redundancy, making the count appropriate for the purpose.

Completeness4/5

The toolset covers the core leadgen workflow: find companies, identify directors, extract contacts, check domains, and review financials. Minor gaps exist (e.g., no social profile lookup or email verification), but the surface is sufficient for typical lead generation tasks.

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