Conecto

Find buyers for a company

longlists_buyers

Read-onlyIdempotent

Companies that could buy a company: in its industry, or in the value chain you name (suppliers, customers, adjacent industries), at least twice its size, in any country (registers where Conecto reads them, the businesses on the map elsewhere). Give the target as a company id, or as its industries with its headcount or revenue in euros. Each buyer comes back scored out of 100 for industry, size against the target, country and its own acquisitions on record (mergers_search), with its reasons. Costs $0.05 per buyer returned; nothing if none are found.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "target": "<target>"
    },
    "name": "longlists_buyers"
  }
}

Input Schema

Name	Required	Description
`limit`	No
`target`	Yes	The company to sell: its id, or its industries, size and country.
`countries`	No	The countries to search, as ISO codes ("DE", "BR") or names ("Germany", "Brasil"). Any country works: one whose register Conecto reads comes from it, every other from the businesses on the map (Overture Maps, and OpenStreetMap daily in Germany). Left out: the registers Conecto reads.
`value_chain`	No	Industries along the value chain, with their role towards the target.

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 28, 2026

TDQS

A4/5.0

Behavior5/5

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

Beyond the readOnly/openWorld annotations, it discloses the cost model ('$0.05 per buyer returned; nothing if none are found'), the data provenance (registers Conecto reads vs Overture Maps/OpenStreetMap), and the per-buyer scoring dimensions with reasons. This is meaningful behavioral context an agent cannot get from the annotations or schema.

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?

Purpose and criteria are front-loaded in the first sentence, followed by input guidance, output/scoring, and cost. It is dense but nearly every clause carries information; the data-source parenthetical is slightly wordy but still useful.

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?

With no output schema, the description adequately covers the return shape (scored out of 100 on industry, size, country, acquisitions, with reasons) and cost. Minor gaps remain around limit behavior and what happens at low/no results, but an agent has enough to call it correctly.

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 75%, so the baseline is 3, but the description adds real meaning: the target can be a company id OR industries + headcount/revenue, countries can be any jurisdiction with differing data sources, and the implicit 'at least twice its size' threshold is stated in prose rather than encoded in the schema. The limit parameter is not explained, keeping it from a 5.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: 'Companies that could buy a company' with concrete criteria (same industry, named value-chain role, at least twice the size, any country). It distinguishes the tool conceptually from an M&A-history lookup like mergers_acquirers by framing it as prospective buyer discovery, though it never names that sibling directly or contrasts itself with longlists_find.

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

Usage Guidelines3/5

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

It gives clear input recipes for the target ('as a company id, or as its industries with its headcount or revenue in euros') and explains when the value-chain mode applies, which implies usage. However, it never says when to prefer this over mergers_acquirers or longlists_find, and gives no exclusions or prerequisites, so guidance is implied rather than explicit.

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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