ATTRACTOR Verification & State

contribute_solution

Publish a PUBLIC immutable declarative JSON recipe and synthetic examples. No personal data, secrets or executable code. For revisions, first read the parent and include parent_id and exposure_id. See https://attractor-observatory-demo.vercel.app/docs.md for the six supported transformation steps.

Input Schema

TableJSON Schema

Name	Required	Description
`problem`	Yes
`solution`	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": {
+        "artifact": {
+          "type": "object"
+        }
+      },
+      "required": [
+        "artifact"
+      ],
+      "type": "object"
+    },
+    {
+      "properties": {
+        "error": {
+          "type": "string"
+        },
+        "request_id": {
+          "type": "string"
+        }
+      },
+      "required": [
+        "error"
+      ],
+      "type": "object"
+    }
+  ],
+  "type": "object"
+}

First observedSep 10, 2026

TDQS

A4.2/5.0

Behavior4/5

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

Annotations are all false, offering no safety profile, so the description carries the burden. It discloses immutability ('immutable'), content restrictions ('No personal data, secrets or executable code'), and implies a write operation. This goes beyond annotations and clarifies behavior without contradiction.

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?

Two sentences with zero wasted words, front-loaded with the core purpose and constraints. The link to docs is an efficient way to provide additional detail without bloating the description.

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

Completeness4/5

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

Given the nested schema and presence of an output schema, the description covers the essential aspects: purpose, immutability, content restrictions, revision process, and a pointer to transformation steps. It lacks explicit error-handling or rollback behavior, but for a publish tool this is sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 50% (only attractor_trace_id and attractor_knowledge_id have descriptions). The tool description adds context about the recipe being declarative and the revision parameters, but does not explain the problem or solution structures in detail, relying on the schema. It adds some value but does not fully compensate for the coverage gap.

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 clearly states a specific verb ('Publish') and a precise resource ('declarative JSON recipe and synthetic examples'), and adds crucial constraints (public, immutable). This makes the tool's purpose unmistakable and distinct from siblings like read_solution or find_solutions, even without naming them.

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?

Provides explicit usage guidance for revisions: 'first read the parent and include parent_id and exposure_id'. It also directs users to external docs for the transformation steps. However, it does not explicitly contrast with alternative tools or state when not to use it, but the revision instruction is a strong usage guideline.

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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