Remove a person from your active network. Soft archive — history is kept and they can be re-added.
| Name | Required | Description | Default |
|---|---|---|---|
| person_id | Yes | The person's id to remove from your network. |
| Name | Required | Description | Default |
|---|---|---|---|
| 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. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Output 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": {
+ "archived": {
+ "type": "boolean"
+ }
+ },
+ "required": [
+ "archived"
+ ],
+ "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"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds meaningful behavioral context beyond the annotations: 'Soft archive — history is kept and they can be re-added.' This tells the agent the operation is non-destructive and reversible, consistent with destructiveHint=false and idempotentHint=true. It does not mention auth or side effects, but the annotations already establish the safety profile.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with zero filler. The first sentence states the purpose; the second sentence adds the critical behavioral nuance. Everything earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter, soft-archive removal operation, the description is complete. Combined with the annotations and an output schema, the agent has enough information to select and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already provides 100% coverage for the single parameter, person_id, with a clear description. The tool description merely reiterates the resource being affected. There is no additional semantic value needed here.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific action ('Remove'), a specific resource ('a person'), and the scope ('your active network'). The 'soft archive' phrase distinguishes it from a hard delete, which helps the agent differentiate it from sibling tools like remove_from_list or permanent deletion operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description makes clear when to use this tool: removing a person from your active network. It does not explicitly name alternatives or exclusion criteria, but the scope is unambiguous. For a simple one-parameter tool, this is sufficient context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.