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Synapse Layer — Trust Infrastructure for AI Agents

by io.github.SynapseLayer
OverviewDiscussionsAdmin

process_text

Extract candidate memories from free-form text with governance filters and sanitization.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesFree-form text to scan for auto-save triggers.
sourceNoSource identifier (default: mcp).
projectNoForce a specific project (e.g., SYNAPSE_LAYER, OFFLY). Auto-detected if omitted.
agent_idNoAgent identifier. Defaults to "default".

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
extractedYes
storedCountYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "extracted": {
+      "items": {
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "storedCount": {
+      "type": "number"
+    }
+  },
+  "required": [
+    "extracted",
+    "storedCount"
+  ],
+  "type": "object"
+}
  2. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -{
-  "additionalProperties": true,
-  "properties": {
-    "events_detected": {
-      "type": "number"
-    },
-    "message": {
-      "type": "string"
-    },
-    "pipeline": {
-      "additionalProperties": true,
-      "type": "object"
-    },
-    "results": {
-      "items": {
-        "additionalProperties": true,
-        "properties": {
-          "intent": {
-            "type": "string"
-          },
-          "memory_id": {
-            "type": "string"
-          },
-          "trust_quotient": {
-            "type": "number"
-          }
-        },
-        "type": "object"
-      },
-      "type": "array"
-    },
-    "status": {
-      "description": "'processed' on success; 'error' with a message on rejected input.",
-      "type": "string"
-    },
-    "timestamp": {
-      "type": "string"
-    }
-  },
-  "required": [
-    "status"
-  ],
-  "type": "object"
-}New value: +null
  3. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "properties": {
+    "events_detected": {
+      "type": "number"
+    },
+    "message": {
+      "type": "string"
+    },
+    "pipeline": {
+      "additionalProperties": true,
+      "type": "object"
+    },
+    "results": {
+      "items": {
+        "additionalProperties": true,
+        "properties": {
+          "intent": {
+            "type": "string"
+          },
+          "memory_id": {
+            "type": "string"
+          },
+          "trust_quotient": {
+            "type": "number"
+          }
+        },
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "status": {
+      "description": "'processed' on success; 'error' with a message on rejected input.",
+      "type": "string"
+    },
+    "timestamp": {
+      "type": "string"
+    }
+  },
+  "required": [
+    "status"
+  ],
+  "type": "object"
+}
  4. First observed

TDQS

A3.5/5.0
Behavior3/5

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

The description mentions governance filters and sanitization, which adds context beyond the annotations (readOnlyHint=false, openWorldHint=false). The description does not contradict annotations. However, it does not detail what the governance filters do, how sanitization works, or what side effects occur (e.g., does it actually save anything or just extract?). The return value details are covered by the output schema.

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 is a single clear sentence that conveys the core purpose and key features. It could potentially mention the output schema or provide a brief example, but it's well-structured and front-loaded.

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 output schema exists (covering return values), the description appropriately addresses the tool's operation. The four parameters are well-documented in the schema. The description could mention that the tool has auto-detection of projects, but that's covered in the schema description. Overall, quite complete for this complexity.

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?

The input schema has 100% coverage, describing each parameter. The description adds value by contextualizing the 'text' parameter as being scanned for 'auto-save triggers', and 'project' as having auto-detection with specific examples (SYNAPSE_LAYER, OFFLY). Baseline 3 is appropriate since the schema already covers details.

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 uses a specific verb-resource pair ('Extract candidate memories') and mentions governance filters and sanitization, which distinguishes it from sibling tools like save_memory or store_memory. However, it could more explicitly differentiate itself from search or recall tools that also deal with text.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the tool is for processing free-form text in an auto-save context, but provides no explicit guidance on when to use this vs. related tools like save_memory or store_memory. No alternatives or when-not-to-use instructions are given.

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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