noticed

save_onboarding

Save the user's onboarding interview answers into noticed in ONE call (use after the onboard skill's questionnaire, or whenever the user shares who they are and what they want). Appends a dated identity note (name, roles, location, what they're building) to the user's own person when available, stores research answers (focus areas, value ranking, current tools, extra notes) in deduplicated long-term memory, and records onboarding completion. Calling it again appends another dated identity note while deduplicating matching memories.

Input Schema

TableJSON Schema

Name	Required	Description
`roles`	No	The user's role chips, stored as given — e.g. ['Founder', 'Product & Engineering'].
`building`	No	One line on what the user is building / working on right now.
`location`	No	Where the user is based — e.g. NYC, Lisbon, Remote.
`user_name`	No	The user's name, as they want to be addressed.
`extra_notes`	No	Anything else worth knowing — goals, who they want to meet, open context.
`focus_areas`	No	What's on the user's plate right now — e.g. ['Sales', 'Hiring', 'Fundraising'].
`current_tools`	No	How the user manages relationships today — e.g. ['Notes app', 'LinkedIn', 'Nothing'].
`value_ranking`	No	What the user wants most from noticed, ranked most → least important.
`communication_preferences`	No	How the user wants the agent to communicate — e.g. ['brief responses', 'no emojis'].

Output Schema

TableJSON Schema

Name	Required	Description
`ok`	Yes	Whether noticed completed the operation.
`data`	No	The operation result when ok is true.
`error`	No	A human-readable error when ok is false.

Schema Changelog

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

Changed1 schema field changedAug 22, 2026

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": {
+        "interview_saved": {
+          "type": "boolean"
+        },
+        "me_person_updated": {
+          "type": "boolean"
+        },
+        "memories_saved": {
+          "type": "number"
+        }
+      },
+      "required": [
+        "me_person_updated",
+        "memories_saved",
+        "interview_saved"
+      ],
+      "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"
+}

First observedAug 22, 2026

TDQS

A4.7/5.0

Behavior5/5

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

The description goes well beyond the annotations by detailing side effects: appending a dated identity note, storing deduplicated memories, recording completion, and the behavior on repeated calls (appends another note while deduplicating). It also notes the conditional 'when available' for the person. No contradiction with annotations; in fact, it enriches the idempotentHint=false annotation by explaining why it's not fully idempotent (deduplication still appends identity notes).

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 concise yet informative. It opens with the core purpose in a single sentence, then elaborates on behavior in two more sentences. There is no redundancy or fluff; every sentence contributes to understanding the tool's function and side effects. The structure is effective and 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?

For a tool with 9 parameters and multiple side effects, the description covers the essential behavioral aspects: what gets stored, where, and what happens on repeat calls. It also notes the conditional person availability. The output schema exists, so return format is implicitly covered. No critical information seems missing for an agent to execute this tool correctly.

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 schema provides 100% coverage with descriptions for all 9 parameters, so the baseline is 3. The description adds meaningful grouping: it maps identity-note fields (name, roles, location, building) and research-answer fields (focus areas, value ranking, current tools, extra notes), which helps an agent understand how parameters are used together. It doesn't repeat schema descriptions but adds contextual organization, justifying a 4.

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 tool's purpose: saving onboarding interview answers in one call. It specifies the trigger (after the onboard skill's questionnaire or when the user shares who they are) and outlines the concrete actions: appending a dated identity note, storing research answers in deduplicated memory, and recording completion. This distinguishes it from generic memory tools like add_memory and add_note.

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 explicit usage context: 'use after the onboard skill's questionnaire, or whenever the user shares who they are and what they want.' It does not explicitly mention alternatives or when not to use it, but the phrase 'in ONE call' and the dedicated nature of the tool imply it is the specialized onboarding entry point, not for ad-hoc memory saves. This is clear enough for an agent to select it appropriately.

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

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