ATTRACTOR Verification & State

fingerprint_json

Read-onlyIdempotent

Use this tool when your workflow needs stable json fingerprint. Input: value. Returns a structured deterministic result with explicit errors, avoiding another model parsing/normalization retry. Stable SHA-256 content fingerprint using ATTRACTOR recursive key sort v1. This public fingerprint is not a signature or proof of provenance. 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": {
+            "algorithm": {
+              "type": "string"
+            },
+            "fingerprint": {
+              "type": "string"
+            }
+          },
+          "required": [
+            "fingerprint",
+            "algorithm"
+          ],
+          "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

A4.4/5.0

Behavior5/5

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

Annotations already mark this as read-only and idempotent, and the description adds substantial behavior: stable SHA-256 algorithm, ATTRACTOR key sort v1, explicit error returns, size/depth/node limits, rejection of reserved prototype keys and unsafe integers, transient input processing, and HMAC trace retention. This goes well beyond the structured 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 dense but every sentence contributes: use case, input, output behavior, algorithmic stability, non-signature caveat, hard limits, safety rejections, and privacy characteristics. It is front-loaded with the intended use and stays structured throughout.

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 tool's complexity and the presence of an output schema, the description is complete enough for an agent to call it correctly. It covers what the tool does, why to use it, constraints, error behavior, security bounds, and persistence semantics without over-explaining the return shape.

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 schema describes the two optional handle parameters but leaves the required 'value' parameter undocumented. The description compensates by explaining what 'value' is used for and by adding constraints like request size, JSON depth, node count, and unsafe integer rejection. It does not fully specify value's expected shape, but the output schema helps close that gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool's purpose: producing a stable JSON fingerprint via SHA-256 with deterministic key sorting. It specifies the resource and operation precisely, but it does not explicitly name sibling tools such as canonicalize_json or diff_json for differentiation.

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?

It opens with a direct 'Use this tool when...' statement, establishing the intended workflow. It also gives an exclusion by stating the fingerprint is not a signature or proof of provenance, though it does not mention alternative tools or when to use them instead.

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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