Renor

Index statistics

memory_status

Read-onlyIdempotent

Counts of what this workspace holds: notes (files; a daily log is one note per day), chunks (index size), entities, facts (superseded values included), and whether semantic search can answer now (semantic): while the embeddings are being built (after a provider is configured or changed, or notes are imported) semantic is false and embeddingRebuild counts the chunks left, and keyword search works meanwhile. The engine's own ledger is not counted as a note, entity or fact. With semantic search configured, chunksWithoutVectors counts the chunks search by meaning cannot find yet because they have no vector (a save whose embedding failed: the next save or search embeds them; imported notes not embedded yet). No embeddings API call.

Input Schema

Name	Required	Description	Default
No arguments

Output Schema

Name	Required	Description	Default
`facts`	Yes
`notes`	Yes
`renor`	Yes
`chunks`	Yes
`digests`	No
`storage`	No
`degraded`	No
`entities`	Yes
`semantic`	Yes
`relations`	Yes
`embeddingDims`	Yes
`capture_features`	No
`embeddingRebuild`	No
`chunksWithoutVectors`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedNov 1, 2026

addedOutput schema / properties / capture_features

Added value: +{
+  "additionalProperties": false,
+  "properties": {
+    "capture_server": {
+      "type": "boolean"
+    },
+    "pinned_rules": {
+      "type": "boolean"
+    },
+    "rule_approvals": {
+      "type": "boolean"
+    },
+    "save_moment": {
+      "enum": [
+        "off",
+        "instructions",
+        "tool",
+        "both"
+      ],
+      "type": "string"
+    },
+    "session_index": {
+      "type": "boolean"
+    },
+    "status_ledger": {
+      "type": "boolean"
+    }
+  },
+  "required": [
+    "save_moment",
+    "status_ledger",
+    "pinned_rules",
+    "rule_approvals",
+    "session_index",
+    "capture_server"
+  ],
+  "type": "object"
+}

Changed1 schema field changedOct 29, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "additionalProperties": {},
+  "properties": {
+    "chunks": {
+      "type": "number"
+    },
+    "chunksWithoutVectors": {
+      "type": "number"
+    },
+    "degraded": {
+      "additionalProperties": false,
+      "properties": {
+        "keyword": {
+          "type": "string"
+        },
+        "semantic": {
+          "type": "string"
+        }
+      },
+      "type": "object"
+    },
+    "digests": {
+      "additionalProperties": {},
+      "propertyNames": {
+        "type": "string"
+      },
+      "type": "object"
+    },
+    "embeddingDims": {
+      "type": [
+        "number",
+        "null"
+      ]
+    },
+    "embeddingRebuild": {
+      "additionalProperties": false,
+      "properties": {
+        "chunks": {
+          "type": "number"
+        },
+        "chunksLeft": {
+          "type": "number"
+        }
+      },
+      "required": [
+        "chunksLeft",
+        "chunks"
+      ],
+      "type": "object"
+    },
+    "entities": {
+      "type": "number"
+    },
+    "facts": {
+      "type": "number"
+    },
+    "notes": {
+      "type": "number"
+    },
+    "relations": {
+      "type": "number"
+    },
+    "renor": {
+      "type": "string"
+    },
+    "semantic": {
+      "type": "boolean"
+    },
+    "storage": {
+      "additionalProperties": {},
+      "properties": {
+        "ledgerBytes": {
+          "type": "number"
+        },
+        "vectorMirror": {
+          "additionalProperties": {},
+          "properties": {
+            "bytes": {
+              "type": "number"
+            },
+            "capBytes": {
+              "type": "number"
+            },
+            "copy": {
+              "enum": [
+                "f32",
+                "none"
+              ],
+              "type": "string"
+            },
+            "overCapSearches": {
+              "type": "number"
+            },
+            "rows": {
+              "type": "number"
+            },
+            "simd": {
+              "type": "boolean"
+            }
+          },
+          "required": [
+            "copy",
+            "rows",
+            "bytes",
+            "capBytes",
+            "overCapSearches",
+            "simd"
+          ],
+          "type": "object"
+        },
+        "vectors": {
+          "type": "number"
+        }
+      },
+      "required": [
+        "vectors",
+        "ledgerBytes"
+      ],
+      "type": "object"
+    }
+  },
+  "required": [
+    "notes",
+    "chunks",
+    "entities",
+    "facts",
+    "relations",
+    "embeddingDims",
+    "semantic",
+    "renor"
+  ],
+  "type": "object"
+}

First observedOct 17, 2026

TDQS

A4.2/5.0

Behavior5/5

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

Annotations already establish readOnlyHint, idempotentHint, openWorldHint=false, and destructiveHint=false. Beyond that, the description adds substantial behavioral context: semantic is false while embeddings rebuild, keyword search works meanwhile, embeddingRebuild counts remaining chunks, the engine ledger is excluded from counts, chunksWithoutVectors explains unsearchable chunks, and no embeddings API call is made. This is exactly the kind of operational nuance annotations cannot convey.

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

Conciseness3/5

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

The main purpose is front-loaded with 'Counts of what this workspace holds,' but the rest is a dense run-on sentence with nested parentheticals covering semantic rebuild behavior, ledger exclusions, and missing vectors. It is information-rich but could be structured more cleanly for faster parsing.

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?

An output schema exists, so the description does not need to explain return-value structure. It nevertheless explains the meaning of the key status fields (semantic, embeddingRebuild, chunksWithoutVectors) and caveats around what is counted, making it largely complete for a diagnostics tool.

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 tool has zero parameters, so there is no parameter semantics for the description to explain. The baseline for a parameterless tool is 4, and the schema is empty, so no additional compensation is needed.

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: counts of what the workspace holds across notes, chunks, entities, facts, and semantic availability. It clearly distinguishes a diagnostic index-statistics tool from retrieval or search siblings by emphasizing counts and state rather than content operations.

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 this is a status/diagnostic tool and explains the meaning of semantic=false during embedding rebuilds, which helps an agent interpret results. However, it does not explicitly say when to call this instead of siblings such as search_memory, retrieve_memory, or index_memory, nor does it state prerequisites or exclusions beyond the transitional semantic behavior.

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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