archlang-mcp

Validate (parse + resolve + lint)

validate

Read-onlyIdempotent

Check a plan in one pass without returning a drawing (parse, resolve and lint): the gate a plan passes before it ships. strict:true makes advisory warnings fail too. Optional graph checks the plan's interior-door adjacency against an intended room graph ({ room: [neighbours] }); a mismatch fails. Optional intent (a brief's expectations, shaped as the archlang://intent-schema resource describes) is checked against the plan: a missed gating assertion (room count, existence, area, total area, windows) fails, while adjacency and reachability are advisory and only score. A malformed intent returns { ok:false, intentErrors } as data, never an exception. Returns { ok, diagnostics, graph?, intent? }.

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": {
      "source": "<source>"
    },
    "name": "validate"
  }
}

Input Schema

Name	Required	Description
`graph`	No	Intended interior-door adjacency: { room: [neighbour rooms] }.
`intent`	No	A brief's intent JSON (shape: the archlang://intent-schema resource); gating assertions fail.
`source`	Yes	ArchLang source.
`strict`	No	Advisory warnings fail too.

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 11, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Beyond the read-only/idempotent annotations, the description discloses the consequences that matter: a graph mismatch fails, gating intent assertions fail while adjacency and reachability are only advisory, malformed intent returns `{ ok:false, intentErrors }` as data rather than throwing, and it never returns a drawing. This is materially more than the annotations provide.

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 a single dense paragraph that front-loads the core operation and then layers the optional behaviors. Every clause carries information, though the nested parentheticals and the long intent sentence make it slightly heavier than necessary.

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?

There is no output schema, but the description supplies the return shape (`{ ok, diagnostics, graph?, intent? }`) and explains the special data-return path for malformed intent. Combined with the behavioral and parameter detail, an agent has everything needed to call and interpret this tool.

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 100%, so the baseline is 3, but the description goes further by explaining what each optional parameter means for outcomes: strict raises warnings to failures, graph is checked against an intended room adjacency map, and intent is governed by a referenced intent-schema resource with gating versus advisory categories. That adds decision-relevant meaning beyond the schema field descriptions.

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?

The description states a precise verb (check/validate), the resource (a plan), and the composite operation (parse, resolve and lint) in one pass, plus the distinguishing trait that it returns no drawing. That is enough to separate it from sibling tools such as compile, lint, or score.

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 gives clear context for the tool's role ('the gate a plan passes before it ships') and spells out the conditions for the optional modes: strict makes warnings fail, graph compares against intended adjacency, and intent checks a brief's expectations. It stops short of explicitly naming alternatives like lint or score, so no exclusions are stated.

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

All archlang-mcp tools Inspect

Try in Browser

Claude Code, inside the Godot 4 editorFlockbay puts an AI agent in the real Godot 4 editor: it writes GDScript, runs your game and fixes what breaks. Free, or bring your own Claude Code or Codex.flockbay.comAd

Glama MCP Gateway

Add one secure layer between your agents and this server.