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ATTRACTOR Verification & State

by io.github.NovanBaillif
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dedupe_records

Read-onlyIdempotent

Use this tool when your workflow needs deduplicate json records. Input: records, keys. Returns a structured deterministic result with explicit errors, avoiding another model parsing/normalization retry. Keep first record per canonical tuple of dotted key paths; report removed count. All records must contain every key. Null is a value, missing is an error. 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
NameRequiredDescriptionDefault
keysYes
recordsYes
attractor_trace_idNoOptional public correlation handle from a prior result; not authentication or proof of identity.
attractor_knowledge_idNoOptional prior result handle. Reuse is counted only when the supplied value matches that result fingerprint.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • 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": {
+            "count": {
+              "type": "integer"
+            },
+            "kept": {
+              "type": "string"
+            },
+            "records": {
+              "type": "array"
+            },
+            "removed": {
+              "type": "integer"
+            }
+          },
+          "required": [
+            "records",
+            "count",
+            "removed",
+            "kept"
+          ],
+          "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"
+}
  2. Added

TDQS

A4.7/5.0
Behavior5/5

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

The description adds substantial behavioral context beyond the readOnly/idempotent/destructive annotations: deterministic results, explicit errors, null-vs-missing handling, request limits, rejection of reserved prototype keys and unsafe integers, transient input processing, and retained private HMAC trace metadata. This is exactly the kind of behavioral detail an agent needs.

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?

Every sentence in the description earns its place: usage trigger, input names, output characteristics, dedup semantics, key constraints, limits, rejection conditions, and privacy behavior. The description remains compact despite covering many operational details and front-loads the primary purpose before constraints.

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, the description covers all needed invocation aspects: required inputs, dedup behavior, error semantics, limits, rejected values, privacy characteristics, and result shape (structured deterministic result with removed count). The output schema further covers the return structure, so nothing critical is missing.

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 description coverage is 50%, so the description must enrich the undocumented 'records' and 'keys' parameters. It does so by explaining canonical dotted key paths, the rule that all records must contain every key, and that null is a value while missing is an error. The optional attractor parameters are already described in the schema, so the description need not repeat them; a slightly more concrete explanation of dotted key path syntax would push this higher.

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 opens with a specific verb and resource: 'deduplicate json records', and immediately names the required inputs (records, keys). It further specifies the exact deduplication rule ('keep first record per canonical tuple of dotted key paths'), making the tool's function unambiguous and distinct from siblings like canonicalize_json or fingerprint_json.

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 explicitly states when to use the tool: 'Use this tool when your workflow needs deduplicate json records.' It also motivates the choice by noting it avoids another model parsing/normalization retry. However, it does not explicitly name alternatives or state when not to use it, so it lacks full exclusion 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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