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by ai.emberverse
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get_principle

Get a principle's definition and every piece in the corpus that teaches it. This is the most powerful cross-domain query in the graph — a principle like 'local_rule_global_pattern' or 'path_dependence' or 'map_territory_gap_private_access' instantly surfaces every domain (biology, computation, physics, language, mind) that expresses the same underlying structure. Use list_principles first to find the right principle ID, then get_principle to see the full landscape.

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": "get_principle"
  }
}

Input Schema

NameRequiredDescriptionDefault
idYesPrinciple ID (snake_case)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

No annotations, so the description carries the load. It discloses that this is a cross-domain aggregation query returning definitions plus every corpus piece teaching the principle — that is real behavioral context about return scope. It does not mention cost, rate limits, or whether the response is paginated, which keeps it short of a 5.

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?

Three sentences, front-loaded with the core action, then the cross-domain value proposition, then the list_principles sequencing. The middle sentence is somewhat promotional but still informative; no wasted boilerplate.

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

Completeness4/5

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

For a single-parameter read tool with no output schema, the description covers the essential: what it returns, why it is useful, and how to get a valid ID. Missing only minor details like response size or pagination behavior.

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% and there is only one parameter, so baseline is high. The description adds value by specifying how to obtain the ID (via list_principles) and reinforcing that it is a principle identifier, not a free-text query.

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?

States a specific verb+resource (get a principle's definition plus every teaching piece) and explicitly frames the tool's scope as a cross-domain query, distinguishing it from list_principles which only surfaces IDs. The examples ('local_rule_global_pattern') make the resource concrete.

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?

Gives explicit sequencing: 'Use list_principles first to find the right principle ID, then get_principle.' This names the alternative and the exact condition for choosing each, which is exactly what usage guidance should do.

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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