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Salesbot - B2B outreach LinkedIn MCP Server with CRM and email

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enrich_crm_company_ares

Idempotent

Fill a CRM company with official data from the Czech business registry ARES: IČO, legal form, main activity and all registered activities (CZ-NACE), employee size category, registered address and founding date. Pass ico when known; otherwise the company name is searched and, when several registry entries match, candidates are returned — call again with the right ico. With include_people the current officers from the public business registry are stored too — only name, role, body and source (never date of birth or home address); the user is responsible for any further use of these personal data. Only for Czech companies; limited to 60 lookups per account per day. Registered activities are broad (firms list many); judge what a company really does from its website, not from ARES.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
icoNo8-digit Czech company ID (IČO). Optional.
crm_company_idYes
include_peopleNoAlso store current officers from the public business registry (statutory body, supervisory board, procurators): name, role and body only. Default false.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / include_people
      Added value: +{
+  "description": "Also store current officers from the public business registry (statutory body, supervisory board, procurators): name, role and body only. Default false.",
+  "type": "boolean"
+}
  2. Added

TDQS

A4.8/5.0
Behavior5/5

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

Annotations establish the safety profile (readOnlyHint=false, idempotentHint=true, destructiveHint=false, openWorldHint=true), and the description layers on non-structured behavior: a concrete rate limit, the CRM write side effect, that officers are stored as name/role/body/source only (never DOB or home address), and an explicit data-protection responsibility note. This is meaningful context well beyond the annotations.

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 purpose followed by usage, privacy, and caveats in a tight sequence; every sentence carries distinct information (rate limit, ambiguity handling, data minimization, interpretation advice). It is dense and long-ish for a single paragraph but not padded.

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 still tells the agent what is written, how ambiguity resolves, what personal data is persisted, and the operational limits. For a 3-parameter enrichment tool this is sufficient to call it correctly without further inference.

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 67%, and the description compensates by explaining the fallback semantics of ico (name search + candidate resolution) and the privacy-scoped behavior of include_people, adding meaning beyond the schema's field descriptions. crm_company_id remains undocumented in both places, but its role is inferable from the tool name.

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 verb and resource (fill/enrich a CRM company) and names the exact data source (Czech ARES registry), enumerating the fields written (IČO, legal form, CZ-NACE activities, employee size, address, founding date). An agent can distinguish this from siblings like update_crm_company, enrich_contacts, or get_crm_company without opening a schema.

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

Usage Guidelines5/5

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

Explicitly states when to pass ico and what happens when it is absent (name search, candidate list returned, call again with the right ico), when to use include_people, and hard exclusions: only Czech companies, 60 lookups/account/day. It even advises on interpretation (ARES activities are broad — judge from the website), covering the when, the when-not, and the follow-up action.

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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