AlpineLead OI

DACH Company Research Agent

run_dach_company_research_agent

First ready-made paid Nexus agent. Discovers and researches DACH companies,
returns FIT/NEED/NOW-qualified findings, and never sends outbound messages.
Use start once, then status/result with the same job_id.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "run_dach_company_research_agent"
  }
}

Input Schema

Name	Required	Description	Default
`goal`	No	Research goal. Required only when action=start.
`action`	No	start, status or result	start
`job_id`	No	smj_... job ID returned by start.
`region`	No	Optional region filter.
`country`	No	DACH, Österreich, Deutschland or Schweiz.	DACH
`district`	No	Optional district filter.
`industry`	No	Optional industry filter.
`language`	No		de
`max_budget`	No	Max credits reserved. Actual work only is billed.
`company_count`	No
`minimum_score`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

AddedSep 4, 2026

TDQS

A4.1/5.0

Behavior4/5

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

Adds useful behavioral context beyond the annotations: the agent is paid, returns qualified findings, and never sends outbound messages. These are meaningful traits the annotations do not convey, especially the safety-related 'never sends outbound messages'.

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 compact at two sentences and the operational guidance is immediately actionable. The opening 'First ready-made paid Nexus agent' is slightly promotional, but it still communicates the paid nature efficiently.

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 paid asynchronous tool with 11 parameters, the description covers the essential workflow and a key boundary. With an output schema present and detailed parameter descriptions elsewhere, the remaining gaps around budget and filtering are acceptable.

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

Parameters3/5

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

With 73% schema coverage, the schema already documents most parameters. The description clarifies the start/status/result lifecycle, but this mostly restates what action and job_id descriptions already say. It does not add meaningful detail for goal, filters, budget, or scoring parameters.

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 ('Discovers and researches DACH companies'), a defined output ('FIT/NEED/NOW-qualified findings'), and a key boundary ('never sends outbound messages'). This clearly separates it from discovery-only or outreach-oriented sibling tools like discover_leads or run_sales_mission.

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?

Gives explicit lifecycle guidance: 'Use start once, then status/result with the same job_id.' This is clear operational context, though it does not name alternatives or say when not to use this tool versus similar research tools.

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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