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NocturnusAI

NocturnusAI

by Auctalis
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Hybrid

teach

Define logical rules for automated inference. When conditions hold, the conclusion becomes derivable through backward chaining. Supports variables and negation-as-failure.

Instructions

Define a logical rule for automatic reasoning. When body conditions hold, head becomes derivable via backward chaining. Use ?-prefixed variables; supports Negation-as-Failure. Example: 'If ?x is human AND NOT god(?x), THEN ?x is mortal'. Side effects: mutates state (additive) — rules remain active until explicitly removed. Auth: requires X-Tenant-ID header; RULE_WRITE permission when auth is enabled. Rate-limited per principal. Errors: VALIDATION_ERROR on malformed rules.

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": {
      "head": "<head>",
      "body": "<body>"
    },
    "name": "teach"
  }
}

Input Schema

NameRequiredDescriptionDefault
headYesThe conclusion — what becomes true when all body conditions hold
bodyYesConditions that must all hold. Each object has 'predicate', 'args', optional 'negated' (explicit negation) and 'naf' (closed-world negation-as-failure)
scopeNoOptional scope

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.12

TDQS

A4.7/5.0
Behavior5/5

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

Despite no annotations, the description fully discloses side effects (additive mutation, persistence until removed), auth requirements, rate limiting, and error types.

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 concise and informative, though a slightly more structured format could improve readability.

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?

Given the complexity of rule definition, the description covers all essential aspects: purpose, usage, parameters, side effects, auth, errors, and output indication.

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?

With 100% schema coverage, the description adds value by explaining variables, negation-as-failure, and the reasoning semantics beyond the raw schema.

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 specifies 'Define a logical rule for automatic reasoning' and explains backward chaining, clearly distinguishing this tool from siblings like 'tell' or 'bulk_assert'.

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?

Describes when to use (defining rules) with a concrete example, but does not explicitly compare to alternatives or state when not to use.

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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