noticed

suggest_identity_match

Destructive

Merge two records in your network that are the SAME person — e.g. an email-only contact and their LinkedIn profile. Pass person_a and person_b as ids from search_people / get_person / resolve_person; this tool does NOT resolve names, so use resolve_person FIRST to clear up any ambiguity about who you mean. The person you name first (person_a) is kept as the surviving record and person_b is folded into it; your own person always survives. The merge is REVERSIBLE — an admin can undo it. If noticed has evidence the two may be DIFFERENT people, it does NOT merge — it returns a needs-confirmation response laying out the conflicting evidence plus a confirmation_token; share the evidence with the user, and ONLY if they review it and still want to merge, call again passing that confirmation_token back to override. The token only comes from that response — never pre-set it based on what the user said before seeing the evidence. When a VERIFIED SIGN-IN shows an identity belongs to someone else, the merge isn't available from chat at all — simply tell the user an admin needs to review that pair on the dashboard (don't explain the internal mechanics). Use once you're confident two entries are one human.

Input Schema

TableJSON Schema

Name	Required	Description
`reason`	No	Optional short note on why they're the same person.
`person_a`	Yes	person_id of the FIRST person — kept as the surviving record after the merge (your own person always survives).
`person_b`	Yes	person_id of the SECOND person — merged into person_a.
`confirmation_token`	No	Returned BY a needs-confirmation response alongside the evidence. Pass it back to merge anyway — only after the user has reviewed the conflicting evidence and still says they're the same person. Cannot be guessed or pre-set.

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

A4.7/5.0

Behavior5/5

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

Beyond the destructiveHint annotation, the description thoroughly discloses behavioral traits: person_a is the surviving record, the merge is reversible by an admin, conflicting evidence triggers a needs-confirmation response rather than a merge, the confirmation_token cannot be guessed, and verified sign-in conflicts are barred from chat. This goes far beyond what annotations convey.

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 long, but almost every sentence carries necessary safety-critical information for a destructive merge operation. It front-loads the core purpose and then lays out the confirmation flow and edge cases. A bit of restructuring into clearer paragraphs would improve scanability.

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 destructive nature and complex confirmation flow, the description is remarkably complete: prerequisite tools, ordering constraints, failure behavior, override mechanics, and a restricted-access edge case are all covered. An agent has enough context to invoke this tool correctly without external documentation.

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?

Although schema coverage is 100%, the description substantially enriches the parameters: it explains that person_a becomes the surviving record, person_b is folded in, and confirmation_token must come from a prior needs-confirmation response and must never be pre-set. This adds critical operational meaning that raw schema definitions do not provide.

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 states a specific action ('Merge two records') on a specific resource (person records), and clarifies which record survives and which is folded. It clearly distinguishes this merge operation from query/creation tools and from related identity tools in the sibling list.

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 when-to-use guidance: use it only after confirming two entries are the same human, use resolve_person first to disambiguate names, and do not use it at all for verified sign-in conflicts. It does not explicitly name sibling tools like accept_identity_match or dismiss_identity_match as alternatives, so it falls just short of full routing 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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