TheoTown MCP Server

by d-init-d

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Python

Local

theotown_validate_plan

Validates a batch of construction commands by running dry-run bounds checks and estimating costs before execution.

Instructions

Perform dry-run bounds checks and cost estimations for a batch of construction commands.

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": {
      "commands": "<commands>"
    },
    "name": "theotown_validate_plan"
  }
}

Input Schema

Name	Required	Description	Default
`dry_run`	No
`commands`	Yes

Output Schema

Name	Required	Description	Default
`valid`	Yes
`errors`	No
`warnings`	No
`command_count`	No
`affected_tiles`	No
`estimated_cost`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Nov 11, 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, and it discloses meaningful content (bounds validation plus cost estimation). However, it never clarifies the paradox of a 'dry-run' tool exposing a dry_run flag defaulting to true — an agent cannot tell from the text what dry_run=false actually does, whether any state is mutated, or what permissions are needed.

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 front-loaded sentence with no filler; the action and scope appear immediately. It is efficient, though the terseness comes at the cost of the missing guidance noted elsewhere.

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

Completeness2/5

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

An output schema exists, so return values need no explanation, but the free-form commands array has no item schema and the dry_run flag is undocumented, leaving the core input contract undefined. For a validation tool whose accuracy depends on callers passing correct command objects, this is a significant omission.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate and it largely does not. It implies a batched array for 'commands' but never describes the shape of a command object (the schema allows arbitrary additional properties with no item definition), and it does not mention the dry_run parameter at all.

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 set (bounds checks and cost estimation) against a specific resource (a batch of construction commands), and the word 'dry-run' signals it is non-executing. It does not name theotown_execute_plan, so the contrast with the sibling that actually builds is left implicit.

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?

The 'dry-run' framing implies this is the pre-flight step before executing a plan, but the description never says 'call this before theotown_execute_plan' or states any condition for choosing it over direct execution. Usage is inferable rather than stated.

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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