constraints-registry-mcp

get_constraints

Retrieve engineering constraints for a given scope, including providers, resource types, environments, and repos. Returns available constraints with bundle ID, or empty list on error to allow proceeding.

Instructions

Return engineering constraints relevant to a scope. Inputs: scope (providers, resource_types, environments, repos, relationship), optional version (bundle id; defaults to latest). resource_types use Terraform resource identifiers, e.g. 'aws_s3_bucket' (NOT 's3_bucket'); repos are tags like 'tag:data-plane'. If unsure of valid values, call describe_scope first, or simply omit a dimension (omitted dimensions are 'don't care' and broaden the match rather than excluding). Output: {available, bundle_id, constraints[]}. Fails open: on any error returns available=false with an empty constraints list so you can proceed.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_constraints"
  }
}

Input Schema

Name	Required	Description	Default
`scope`	No
`version`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.0.0Jun 20, 2026

TDQS

A4.9/5.0

Behavior5/5

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

No annotations provided, so description carries full burden. It explains input specifics (Terraform identifiers, repos as tags), optional version with default, output format, and error behavior (fails open).

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?

Single paragraph with good front-loading, but could be slightly more structured (e.g., bullet points for input fields). Still concise and informative.

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?

Despite no output schema, description states output format. Covers scope usage, defaults, error behavior, making it complete for agent decision-making.

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

Parameters5/5

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

Schema coverage is 0%, but description fully explains both parameters: scope (with details on its dimensions) and version (with default). Provides examples and rules for scope values.

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 clearly states the tool returns engineering constraints relevant to a scope, and distinguishes itself from siblings like describe_scope and validate by explaining its specific purpose.

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

Usage Guidelines5/5

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

Explicit guidance on when to call describe_scope first if unsure of valid values, and that omitting dimensions broadens match. Also explains error handling (fails open) so agent knows how to proceed.

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

Deploy Server