NEXUS AGI

Run cognitive cycle

run_cognitive_cycle

Idempotent

Run the NEXUS integration cycle over the user's knowledge base, for example after adding a batch of observations or facts.

Stages, in order:
1. World model: a temporal sweep that finds conflicting current relations
   and opens contradictions for them, followed by knowledge-gap detection
   (counts only). A 'cycle.completed' event with per-stage counts and
   timings is recorded.
A failure in one stage does not stop the others.

Returns:
    Dict with 'world_model' (counts and timings per stage), 'hypothesis'
    (null when no hypothesis was created) and 'errors' (stage-level
    errors).

Input Schema

Name	Required	Description	Default
No arguments

Output Schema

Name	Required	Description	Default
`errors`	No
`concepts`	No
`hypothesis`	No
`world_model`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed3 schema fields changedOct 25, 2026

changedOutput schema / description

Previous value: -"Outcome of a full cognitive cycle (strengthen → hypothesize → discover)."New value: +"Outcome of a full cognitive cycle."

removedOutput schema / properties / strengthen

Removed value: -{
-  "anyOf": [
-    {},
-    {
-      "type": "null"
-    }
-  ],
-  "default": null,
-  "title": "Strengthen"
-}

addedOutput schema / properties / world_model

Added value: +{
+  "anyOf": [
+    {},
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "World Model"
+}

First observedOct 8, 2026

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=false, destructiveHint=false, openWorldHint=false, and idempotentHint=true. The description adds meaningful behavior beyond that: it opens contradictions, records a 'cycle.completed' event, isolates stage failures, and describes return fields including null hypothesis behavior. It does not describe permission requirements or idempotency details, but it is substantially more informative than the annotations alone.

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 front-loads the purpose, then organizes stages and return values in a readable way. It is not padded, but the 'Stages, in order' list only includes stage 1 even though the return mentions a hypothesis stage, which makes the structure feel slightly incomplete.

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

Completeness3/5

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

The tool is parameterless and has an output schema, so return-value details are partly redundant but helpful. However, the description lists only the world-model stage while the return object references a hypothesis stage, leaving part of the cycle unexplained. For a multi-stage integration tool, that is a meaningful completeness gap.

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?

This tool has zero parameters, so there is no parameter semantics to explain. The empty input schema means the baseline score of 4 applies.

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 names a specific verb ('Run') and resource ('NEXUS integration cycle'), then explains the world-model stage and what it produces. An agent can distinguish this from sibling tools like cognitive_query or process_observation because the action is an integration cycle over the knowledge base, not a query or single observation write.

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?

The description gives clear usage context: 'for example after adding a batch of observations or facts.' It does not explicitly name when not to use it or point to a sibling alternative, so it stops short of full routing guidance.

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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