Ayebot buyer index

Buyer appetite for a sector across Southeast Asia

buyer_appetite_summary

Read-onlyIdempotent

Summarise acquirer appetite for one sector across Singapore, Malaysia, Indonesia, Thailand, Vietnam and the Philippines: per-market counts (ranked), the strategic / private equity / family office mix, and how many name the market directly versus cover it through a regional or global mandate. Use it for "where in Southeast Asia is buyer appetite strongest for my education business?". Aggregate counts only, never names. Links to the sector page on ayebot.com.

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": {
      "sector": "<sector>"
    },
    "name": "buyer_appetite_summary"
  }
}

Input Schema

Name	Required	Description	Default
`sector`	Yes	Sector key or name, e.g. "education", "beauty", "industrial engineering".

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 9, 2026

TDQS

A3.8/5.0

Behavior3/5

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

Annotations already cover the safety profile (readOnlyHint, idempotentHint, destructiveHint=false, openWorldHint=false), so the bar is lowered. The description adds one genuinely useful behavioral constraint, 'Aggregate counts only, never names', which tells the agent what it will and will not receive; beyond that it discloses nothing about auth, rate limits, or data freshness.

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 output inventory is front-loaded in the first sentence and the usage question follows, so an agent gets the payload description immediately. The sentence is dense with comma-separated output types, but no sentence is wasted.

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 carries the return-value burden and does so fully: ranked per-market counts, ownership mix, and direct-vs-mandate coverage. Combined with the aggregate-only restriction, an agent knows both the shape and the limits of the response for this simple one-parameter tool.

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?

Schema coverage is 100% with a single, well-described 'sector' parameter, so the schema already carries the parameter semantics. The description adds no syntax, format, or validity guidance beyond what the schema's example values provide, making the baseline 3 correct.

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 names a specific verb ('Summarise') and resource ('acquirer appetite for one sector') and enumerates the exact outputs (per-market counts ranked, strategic/PE/family office mix, direct vs regional mandate). It never names a sibling such as count_buyers, so an agent must infer the boundary from the outputs alone rather than being routed explicitly.

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?

It supplies a concrete usage scenario ('where in Southeast Asia is buyer appetite strongest for my education business?'), which clearly indicates the context in which this is the right tool. It stops short of stating when NOT to use it or naming count_buyers as the alternative for a single-market count.

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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