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Glama

noticed

by so.noticed.mcp

list_lists

Read-onlyIdempotent

The user's lists (personal + team), each with its description, how many people are on it, and how many suggestions are waiting for a decision. Start here when the user names a list — it's where the list_id comes from.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okYesWhether noticed completed the operation.
dataNoThe operation result when ok is true.
errorNoA human-readable error when ok is false.

Schema Changelog

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

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "additionalProperties": true,
+  "properties": {
+    "data": {
+      "additionalProperties": false,
+      "description": "The operation result when ok is true.",
+      "properties": {
+        "lists": {
+          "items": {
+            "additionalProperties": true,
+            "properties": {
+              "ai_enabled": {
+                "type": "boolean"
+              },
+              "description": {
+                "type": [
+                  "string",
+                  "null"
+                ]
+              },
+              "list_id": {
+                "type": "string"
+              },
+              "member_count": {
+                "type": "number"
+              },
+              "name": {
+                "type": "string"
+              },
+              "organization_id": {
+                "$ref": "#/properties/data/properties/lists/items/properties/description"
+              },
+              "pending_count": {
+                "type": "number"
+              }
+            },
+            "required": [
+              "list_id",
+              "name"
+            ],
+            "type": "object"
+          },
+          "type": "array"
+        },
+        "total": {
+          "type": "number"
+        }
+      },
+      "required": [
+        "lists",
+        "total"
+      ],
+      "type": "object"
+    },
+    "error": {
+      "description": "A human-readable error when ok is false.",
+      "type": "string"
+    },
+    "ok": {
+      "description": "Whether noticed completed the operation.",
+      "type": "boolean"
+    }
+  },
+  "required": [
+    "ok"
+  ],
+  "type": "object"
+}
  2. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare this read-only, idempotent, and non-destructive. The description adds meaningful context beyond annotations by specifying scope (personal + team), the fields returned (description, member counts, pending suggestions), and the tool's role as the entry point for resolving a list_id. No behavioral contradictions exist.

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, no filler. The first sentence defines the resource and return details; the second provides the key usage hint. All content earns its place and is front-loaded.

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?

With no parameters, an output schema, and annotations covering safety, the description is sufficient for an agent to know when to call the tool and what it returns. The usage note about list_id fills the main practical gap an agent would face.

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 100% schema description coverage, so there is nothing for the description to explain about parameters. The baseline for a parameterless tool is appropriate, and the description adds no irrelevant parameter detail.

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 that this tool returns the user's lists (personal and team) along with key metadata, and it explicitly positions itself as the source for list_id lookup. The plural scope and emphasis on list details distinguish it from siblings like get_list, create_list, and list_actions.

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 provides a clear, direct usage trigger: 'Start here when the user names a list.' This gives the agent a concrete condition for using the tool. However, it does not explicitly name alternative tools or state when not to use it, so it stops short of full exclusionary 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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TDQS

B3.4/5.0
Disambiguation3/5

The tool set is organized around distinct resources, and the descriptions work hard to separate them, but several close pairs remain easy to confuse: add_memory vs memory_save vs add_note, accept_identity_match vs suggest_identity_match, and dismiss_identity_match vs mark_different_people. An agent will often need to read very subtle signals (who originated the content, pending vs initiating a merge, soft vs durable rejection) to pick the right tool.

Naming Consistency3/5

Most tools follow a clear verb_noun snake_case pattern like create_list, update_person, and delete_view, which is readable and mostly predictable. However, the memory tools break the pattern (memory_save, memory_get, memory_search instead of save_memory/get_memory/search_memory), and a few noun-style names (my_profile, network_summary, account_status) add inconsistency.

Tool Count1/5

At 57 tools, this is an extremely large surface that exceeds the calibration threshold for an extreme mismatch. The scope is broad, but many tools are micro-specialized variations of the same concept, such as four memory-related tools and seven identity-match tools, which makes the count feel inflated rather than well-scoped.

Completeness4/5

The tool set provides thorough lifecycle coverage for the core domain: people can be added, updated, searched, and removed; lists, views, actions, and scheduled tasks have create/read/update/delete; and identity matching has accept, dismiss, differentiate, and suggest paths. Minor gaps exist, such as no direct memory/note deletion or intro deletion, but agents can generally complete workflows without hitting dead ends.

Resources