noticed

add_memory

Idempotent

Save a MEMORY about a SPECIFIC person — something YOU worked out on your own, unprompted, that the user never told you (a pattern you spotted, a fact you inferred from their calendar or email). If the user ASKED you to remember it, it's their note, not your memory — use add_note. @mention a name in content to link someone in your network. Read them back via get_person (relationship.memories). For a general fact not about one person, use memory_save instead.

Input Schema

TableJSON Schema

Name	Required	Description
`tags`	No	Optional tags.
`content`	Yes	The memory. @mention a name in your network to link them.
`person_id`	Yes	The person this memory is about.
`occurred_at`	No	The date this memory REFERS to, if different from now (ISO timestamp). Omit to anchor to write time. Use when recording a past event, e.g. 'met at conference last week'.
`captured_via`	No	The skill capturing this — pass the active skill's slug (e.g. 'research-person', 'add-person') when a skill is driving the write; omit for an ad-hoc memory.

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": true,
+      "description": "The operation result when ok is true.",
+      "properties": {},
+      "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

A4.5/5.0

Behavior4/5

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

Annotations already communicate that this is a write operation (readOnlyHint false), non-destructive (destructiveHint false), and idempotent (idempotentHint true). The description adds valuable context: memories are stored in relationship.memories, can be linked via @mention, and are read via get_person. It doesn't contradict annotations. Slightly more detail on idempotent behavior could elevate it, but it's already strong.

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?

Though a bit longer than typical, every sentence earns its place. It front-loads the core purpose, then immediately gives alternatives, linking behavior, retrieval method, and the general-fact case. There is no fluff or redundancy; the structure is logical and easy to parse.

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 complexity (5 params, 2 required) and an output schema, the description covers everything an agent needs for correct invocation: definition, distinguishing rules, linking via @mention, retrieval path, and alternative tool usage. The existence of an output schema covers return value expectations, so nothing critical is missing.

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%, so all five parameters are documented in the schema itself. The description adds limited new meaning—it reinforces that @mention in content links to your network, but this is also in the schema. Since the schema does the heavy lifting, the description adds marginal value, aligning with the baseline score of 3.

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 verb and resource: 'Save a MEMORY about a SPECIFIC person'. It further distinguishes its purpose from siblings by defining what constitutes a memory (something you inferred, not user-told) and explicitly names add_note and memory_save as alternatives. This makes the tool's role unmistakable.

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?

The description provides explicit when-to-use and when-not-to-use guidance. It states: 'If the user ASKED you to remember it, it's their note, not your memory — use add_note' and 'For a general fact not about one person, use memory_save instead.' It also tells how to read memories back via get_person. This leaves no ambiguity about selecting this tool over siblings.

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

All noticed tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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

Need Help?