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Leadgen MCP

by io.github.darksider4all

lookup_business

Search the Romanian business registry (official ONRC data) for companies.

Data source: official ONRC open-data snapshot (data.gov.ro), loaded locally.
Name queries use a ranked FTS5 index and are diacritic-insensitive
('paval' matches 'PAVĂL'); digit queries match the CUI exactly, falling
back to a CUI prefix match when the exact code is unknown.

Args:
    query: Company name or CUI (tax ID) to search for.
    max_results: Maximum number of companies to return (1-100).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
max_resultsNo

TDQS

A4.5/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full transparency burden. It discloses meaningful behavioral details: the data source is a local snapshot, name queries use an FTS5 index and are diacritic-insensitive, and digit queries match CUI exactly with prefix fallback. These details go beyond basic purpose and help the agent anticipate behavior, though it omits return format and safety statements.

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 and well-structured: a one-line summary, then data source and query behavior, then parameter definitions. Every sentence adds value without redundancy.

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?

For a two-parameter lookup tool with no output schema, the description covers the essential context: purpose, data source, matching behavior, and parameter meaning. It is slightly incomplete by not describing the response shape or edge-case behavior, but given the tool's simplicity and the presence of detailed query semantics, it is largely complete.

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 input schema has no property descriptions (0% coverage), but the description's Args section fully compensates by explaining query as either a company name or CUI and max_results with a valid range (1-100). This adds critical semantics beyond the raw schema types.

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 clearly states the tool's function with a specific verb ('Search') and resource ('Romanian business registry ... for companies'), distinguishing it from sibling tools like lookup_director and lookup_domain by its focus on companies. It also names the official data source, reinforcing its scope.

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 establishes a clear context for when to use the tool (company lookups in the Romanian registry) and explains query types, but it does not explicitly mention alternatives or exclusion conditions relative to sibling tools. This is clear context without explicit guidance, so 4.

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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