ATTRACTOR Verification & State

flatten_json

Read-onlyIdempotent

Use this tool when your workflow needs flatten json for agent memory. Input: value. Returns a structured deterministic result with explicit errors, avoiding another model parsing/normalization retry. Flatten values to JSON Pointer keys. Empty string is root; / is an empty property name. Empty containers stay typed containers, not strings. Limits: request 24 KB, JSON depth 24, 4,000 nodes. Reserved prototype keys and unsafe integers rejected. Inputs processed transiently; private HMAC trace metadata retained.

Input Schema

TableJSON Schema

Name	Required	Description
`value`	Yes
`attractor_trace_id`	No	Optional public correlation handle from a prior result; not authentication or proof of identity.
`attractor_knowledge_id`	No	Optional prior result handle. Reuse is counted only when the supplied value matches that result fingerprint.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed3 schema fields changedSep 10, 2026

addedInput schema / properties / attractor_knowledge_id

Added value: +{
+  "description": "Optional prior result handle. Reuse is counted only when the supplied value matches that result fingerprint.",
+  "pattern": "^ATR-K-[a-f0-9]{64}$",
+  "type": "string"
+}

addedInput schema / properties / attractor_trace_id

Added value: +{
+  "description": "Optional public correlation handle from a prior result; not authentication or proof of identity.",
+  "pattern": "^ATR-T-[a-f0-9]{32}$",
+  "type": "string"
+}

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "anyOf": [
+    {
+      "properties": {
+        "attractor_trace_id": {
+          "type": "string"
+        },
+        "commons": {
+          "type": "object"
+        },
+        "knowledge_id": {
+          "type": "string"
+        },
+        "ok": {
+          "const": true
+        },
+        "request_id": {
+          "type": "string"
+        },
+        "result": {
+          "properties": {
+            "root_pointer": {
+              "type": "string"
+            },
+            "values": {
+              "type": "object"
+            }
+          },
+          "required": [
+            "values",
+            "root_pointer"
+          ],
+          "type": "object"
+        },
+        "tool": {
+          "type": "string"
+        }
+      },
+      "required": [
+        "ok",
+        "tool",
+        "result",
+        "knowledge_id",
+        "attractor_trace_id",
+        "request_id",
+        "commons"
+      ],
+      "type": "object"
+    },
+    {
+      "properties": {
+        "error": {
+          "type": "string"
+        },
+        "request_id": {
+          "type": "string"
+        }
+      },
+      "required": [
+        "error"
+      ],
+      "type": "object"
+    }
+  ],
+  "type": "object"
+}

AddedSep 10, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Annotations already provide readOnly, idempotent, and non-destructive hints. The description adds substantial behavioral detail beyond those: explicit error behavior, depth/size/node limits, rejection of unsafe integers and prototype keys, transient input processing, and retained HMAC trace metadata. There is no contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and front-loaded with the use case, then terse behavior and constraints. Every sentence adds information: limits, edge cases, rejection behavior, and privacy. The telegraphic fragments are deliberate and efficient rather than wordy.

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?

For a three-parameter deterministic utility with an output schema and safety annotations, the description covers invocation context, accepted input semantics, edge cases, operational limits, rejection behavior, and data handling. The output schema covers return shapes, so omitting return details is acceptable. Only minor missing piece is explicit sibling routing, already noted in usage guidelines.

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?

The required 'value' parameter has no schema description, and the description supplies its meaning: it is the JSON input that is flattened to JSON Pointer keys, with edge cases like empty-string root and typed empty containers. The two optional handle parameters already have detailed schema descriptions, so their absence from the prose is acceptable.

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 specific operation ('flatten json') and the output style ('JSON Pointer keys'), plus the purpose ('for agent memory'). It clearly distinguishes this from the sibling normalization tools by emphasizing flattening to JSON Pointer and deterministic structured results.

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 opens with a clear launch condition: 'Use this tool when your workflow needs flatten json for agent memory.' It does not explicitly name alternatives such as fingerprint_json or canonicalize_json or state when not to use it, so it falls 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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