Sutra

Read Sutra context

read_context

Read-onlyIdempotent

Load the user's shared Sutra identity context.

Call this before answering broad questions about the user's preferences, history, projects, or prior decisions. The result can contain sensitive personal data, so use only the details needed to answer the user's current request. For a targeted lookup, use search_memory instead.

Input Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Output Schema

TableJSON Schema

Name	Required	Description	Default
`clusters`	Yes
`memories`	Yes
`display_name`	Yes
`last_updated`	Yes
`total_memory_count`	Yes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

Changed3 schema fields changedSep 2, 2026

removedOutput schema / additionalProperties
```
Removed value: -true
```

addedOutput schema / properties

Added value: +{
+  "clusters": {
+    "items": {
+      "properties": {
+        "created_at": {
+          "type": "string"
+        },
+        "description": {
+          "anyOf": [
+            {
+              "type": "string"
+            },
+            {
+              "type": "null"
+            }
+          ]
+        },
+        "is_default": {
+          "type": "boolean"
+        },
+        "memory_count": {
+          "anyOf": [
+            {
+              "type": "integer"
+            },
+            {
+              "type": "null"
+            }
+          ]
+        },
+        "name": {
+          "type": "string"
+        }
+      },
+      "required": [
+        "name",
+        "description",
+        "is_default",
+        "created_at",
+        "memory_count"
+      ],
+      "type": "object"
+    },
+    "type": "array"
+  },
+  "display_name": {
+    "anyOf": [
+      {
+        "type": "string"
+      },
+      {
+        "type": "null"
+      }
+    ]
+  },
+  "last_updated": {
+    "anyOf": [
+      {
+        "type": "string"
+      },
+      {
+        "type": "null"
+      }
+    ]
+  },
+  "memories": {
+    "items": {
+      "properties": {
+        "cluster": {
+          "anyOf": [
+            {
+              "type": "string"
+            },
+            {
+              "type": "null"
+            }
+          ]
+        },
+        "content": {
+          "type": "string"
+        },
+        "created_at": {
+          "type": "string"
+        },
+        "importance": {
+          "type": "integer"
+        },
+        "source_agent": {
+          "type": "string"
+        },
+        "tags": {
+          "items": {
+            "type": "string"
+          },
+          "type": "array"
+        }
+      },
+      "required": [
+        "content",
+        "source_agent",
+        "importance",
+        "created_at",
+        "tags"
+      ],
+      "type": "object"
+    },
+    "type": "array"
+  },
+  "total_memory_count": {
+    "type": "integer"
+  }
+}

addedOutput schema / required

Added value: +[
+  "display_name",
+  "clusters",
+  "memories",
+  "total_memory_count",
+  "last_updated"
+]

First observedSep 2, 2026

TDQS

A4.7/5.0

Behavior4/5

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

Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds valuable context beyond annotations: the result may contain sensitive personal data and advises using only needed details. This behavioral caution is useful and not redundant.

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 three sentences with no fluff: the primary action is front-loaded, usage guidance follows, and the alternative is named in the final sentence. Every sentence contributes to correct invocation.

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?

For a zero-parameter read-only tool with an output schema, the description covers the essential operational context: what it returns, when to use it, its sensitivity implications, and when to use the sibling instead. 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?

The tool has zero parameters and schema coverage is 100% vacuously. Baseline for 0 params is 4, and the description appropriately avoids inventing parameter information. It offers no param semantics to add because there are none.

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: 'Load the user's shared Sutra identity context.' It clearly distinguishes itself from the targeted search_memory sibling by framing itself as the broad-context load, so an agent can select it correctly without opening schemas.

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

Usage Guidelines5/5

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

Explicit when-to-use guidance is given: 'Call this before answering broad questions about the user's preferences, history, projects, or prior decisions.' It also states the alternative use case with search_memory, providing a clear selection boundary.

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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TDQS

A4.4/5.0

Disambiguation5/5

Each tool has a clearly scoped purpose, and the descriptions include explicit usage rules that prevent overlap. Memory retrieval is split into read_context, search_memory, get_cluster, and export_all with distinct trigger conditions, so agents can reliably choose the right tool.

Naming Consistency5/5

All tools use a consistent lower_snake_case verb_noun pattern such as list_clusters, get_skill, search_memory, write_observation, and upload_skill. The naming is predictable and uniform across the entire set.

Tool Count5/5

Ten tools is well-scoped for a personal memory and skills server: it covers memory access, skill access, discovery, writing observations, and export. Each tool earns its place without the set feeling bloated or thin.

Completeness3/5

The server covers discovery, retrieval, saving observations, and uploading skills, but it lacks update/delete operations for memories and skill management beyond upload. This leaves a notable lifecycle gap: incorrect or obsolete saved observations cannot be removed or corrected in place.

Resources

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