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Glama

noticed

by so.noticed.mcp

create_list

Create a list — a described set of people (e.g. 'Seed investors', 'Design candidates'). The description is REQUIRED and is the goal noticed matches people against, so write it specifically ('pre-seed investors who back dev tools in Europe'): noticed then proposes people from the network to add, and the user confirms or rejects each one. The title is auto-generated from the description unless you pass a name. Returns the new list_id.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoShort title for the list; auto-generated from the description when omitted.
ai_enabledNoWhether noticed suggests people for this list (default true).
descriptionYesWhat the list is FOR, in the user's words — the goal noticed matches people against. REQUIRED. Be specific; a vague description means vague suggestions.
organization_idNoCreates a TEAM list owned by that organization (from account_status.teams). Omit for a personal list.

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: +{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "additionalProperties": true,
+  "properties": {
+    "data": {
+      "additionalProperties": true,
+      "description": "The operation result when ok is true.",
+      "properties": {
+        "ai_enabled": {
+          "type": "boolean"
+        },
+        "description": {
+          "type": [
+            "string",
+            "null"
+          ]
+        },
+        "list_id": {
+          "type": "string"
+        },
+        "member_count": {
+          "type": "number"
+        },
+        "name": {
+          "type": "string"
+        },
+        "organization_id": {
+          "$ref": "#/properties/data/properties/description"
+        },
+        "pending_count": {
+          "type": "number"
+        }
+      },
+      "required": [
+        "list_id",
+        "name"
+      ],
+      "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

A4.6/5.0
Behavior5/5

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

Annotations only indicate non-readOnly, non-idempotent behavior. The description goes beyond that by disclosing the full workflow: noticed proposes people from the network and the user confirms/rejects each one. It also states the return value (list_id) and the auto-generation of the title, providing transparent behavioral context that annotations do not.

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 moderately long but each sentence serves a purpose: it defines purpose, stresses the criticality of the description, explains the matching flow, and mentions the auto-title. It is front-loaded with the core purpose and does not ramble, though it could be tightened slightly.

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 create operation with an output schema and complete parameter documentation, the description supplies everything an agent needs: the purpose, the required parameter's semantics, the optional parameters' behavior, the workflow, and the return value. No essential information 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?

Schema coverage is 100%, but the description adds meaning: it explains why the description parameter is required and what happens if it is vague, and it reiterates the auto-title behavior for the name parameter. This goes beyond the schema's own property descriptions and helps the agent craft effective input.

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 clear verb-resource pair ('Create a list') and defines what a list is ('a described set of people'), distinguishing it from sibling tools like add_to_list (which adds to an existing list) and delete_list. The resource and action are unambiguous.

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 gives strong guidance on how to write the required description parameter (be specific, as it drives matching) and mentions that name is optional because the title auto-generates. It does not explicitly contrast with alternatives, but the create action is self-evident and the context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All noticed toolsInspect
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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