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noticed

by so.noticed.mcp

accept_list_suggestion

Destructive

Accept one pending add/remove suggestion for a list. A suggested add becomes a member; a suggested removal is removed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
list_idNoThe list id. Pass this OR list_name.
list_nameNoThe list name, matched case-insensitively. Pass this OR list_id.
person_idYesThe person id on the pending suggestion.

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": {
+        "list_id": {
+          "type": "string"
+        },
+        "person_id": {
+          "type": "string"
+        },
+        "remaining_pending": {
+          "type": "number"
+        }
+      },
+      "required": [
+        "list_id",
+        "person_id",
+        "remaining_pending"
+      ],
+      "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

A4.1/5.0
Behavior4/5

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

Annotations already declare destructiveHint=true and idempotentHint=false, so mutability is covered. The description adds behavioral clarity by specifying the effect: 'A suggested add becomes a member; a suggested removal is removed.' This goes beyond annotations by clarifying the actual outcome of the operation, which is useful context for the agent.

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 two sentences with no redundancy. It leads with the core purpose, then clarifies the two possible outcomes. Every word earns its place; it is concise and well-structured.

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?

Given the tool's simplicity, the output schema (which defines return values), and the annotations covering safety, the description is complete. It covers the action, the scope ('one pending'), and the result. There is no missing information needed to call the tool correctly.

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 description coverage is 100% for all three parameters (list_id, list_name, person_id) with explanations for each. The description does not add any parameter-specific guidance beyond what the schema already states. Since the schema fully documents the parameters, the baseline of 3 applies.

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 the action: 'Accept one pending add/remove suggestion for a list.' It specifies the resource (a pending suggestion on a list) and distinguishes the outcome for adds vs. removals. This differentiates it from sibling tools like reject_list_suggestion, add_to_list, and remove_from_list by focusing on accepting a suggestion rather than directly modifying membership or rejecting.

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 usage context: it is for accepting pending suggestions on a list. However, it does not explicitly name alternatives or provide when-not-to-use guidance. For instance, it does not mention that reject_list_suggestion should be used instead to decline, or that add_to_list/remove_from_list are for direct actions. The guidance is adequate but not explicit about exclusions.

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