Statesman MCP Server

generate_country_embedding

Calculates a 1024-dimensional vector embedding for a specified country using the configured model to enable vector-based comparison and discovery.

Instructions

Calculate 1024-dimensional vector embedding for a country using the configured embedding model.

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": {
      "id": "<id>"
    },
    "name": "generate_country_embedding"
  }
}

Input Schema

Name	Required	Description	Default
`id`	Yes	Country ID
`sync`	No	If true, wait synchronously for embedding generation to finish

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Nov 13, 2026

TDQS

C2.9/5.0

Behavior2/5

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

No annotations are supplied, so the description carries the full behavioral burden, yet it says nothing about whether the embedding is persisted, what happens when sync is false, whether the call is idempotent, or what permissions or model configuration are required. It does disclose the vector size and that a configured model is used, which is genuinely useful context.

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?

A single well-formed sentence with the operation and vector dimensionality front-loaded and no redundant wording. It is efficient, though it leaves no room for the usage or behavioral context an agent would benefit from.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description does not indicate what is returned, particularly in the sync=false case where a job handle might be expected, nor where the embedding is stored. For a simple two-parameter tool with fully described schema fields this is adequate but has clear gaps around results and side effects.

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%, and the schema already documents both id (Country ID) and sync (wait synchronously). The description adds no parameter-level detail beyond this, so the baseline of 3 applies.

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 gives a specific verb and resource ("Calculate ... embedding for a country") and states the output dimensionality, so the operation is immediately identifiable. It does not explicitly distinguish itself from the obvious sibling generate_city_embedding, which keeps it short of a 5.

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

Usage Guidelines2/5

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

There is no explicit guidance on when to use this tool versus generate_city_embedding or generate_deep_synthesis, nor any stated prerequisites such as the country needing to exist. Usage is only weakly implied by the tool name and the word "country".

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server