noticed

mark_different_people

DestructiveIdempotent

Mark two records as DIFFERENT people — a durable disconnect. Records that they are not the same person so noticed won't suggest (or auto-) merge them again. Reversible by an admin. Identify them by candidate_id, OR person_a + person_b, OR profile_a + profile_b (the profile pair from a review-queue row — use this to clear email→person false positives). For a soft 'not now', use dismiss_identity_match instead.

Input Schema

TableJSON Schema

Name	Required	Description
`person_a`	No	persons.id — alternative to candidate_id; pass with person_b.
`person_b`	No	persons.id — pass with person_a.
`profile_a`	No	Source-prefixed profile id (e.g. email:a@b.com) — the profile_a from a pending review-queue row. Use for the email→person review queue (candidate_id null, one side has no person). Pass with profile_b.
`profile_b`	No	Source-prefixed profile id (e.g. github:123) — the profile_b from list_identity_matches. Pass with profile_a.
`candidate_id`	No	merge_candidates id of a pending match (the candidate_id from list_identity_matches).

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": {
+        "marked_different": {
+          "type": "boolean"
+        },
+        "message": {
+          "type": "string"
+        }
+      },
+      "required": [
+        "marked_different",
+        "message"
+      ],
+      "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

A5/5.0

Behavior5/5

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

Beyond the annotations (readOnlyHint false, destructiveHint true), the description adds behavioral details: 'durable disconnect', 'so notices won't suggest (or auto-) merge them again', and 'Reversible by an admin'. This explains the lasting impact and reversibility, exceeding annotation information.

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 dense but well-organized, using clear separations (semicolons, dashes) to convey purpose, usage, and alternatives. Every sentence adds value, and it avoids unnecessary fluff while remaining readable.

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?

It covers the action's purpose, how to invoke it, its effects on future suggestions, and its reversibility. Although an output schema exists, the description doesn't need to detail return values; it provides complete operational context for an agent to use the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema descriptions cover all 5 parameters (100% coverage). The description enriches them by explaining the logical alternatives (candidate_id vs person pair vs profile pair) and the specific scenarios for each, such as handling review-queue rows, adding meaning beyond the basic field comments.

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: 'Mark two records as DIFFERENT people' and explains the durable disconnect effect. It distinguishes from the sibling tool dismiss_identity_match, which is a soft 'not now', making the purpose unambiguous.

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?

It provides explicit ways to identify the records: by candidate_id, person pair, or profile pair, with specific context for each (e.g., 'use this to clear email→person false positives'). It also references the alternative tool for a softer action, giving clear when-to-use 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

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