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Glama

delete_view

Destructive

Permanently delete a saved View. This removes only the saved definition; matching people remain in the network.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
view_idNoThe View id. Pass this OR view_name.
view_nameNoThe View name, matched case-insensitively. Pass this OR view_id.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okYesWhether noticed completed the operation.
dataNoThe operation result when ok is true.
errorNoA human-readable error when ok is false.

Schema Changelog

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

  1. Changed1 schema field changed
    • 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": {
      +        "deleted": {
      +          "type": "boolean"
      +        },
      +        "view_id": {
      +          "type": "string"
      +        }
      +      },
      +      "required": [
      +        "view_id",
      +        "deleted"
      +      ],
      +      "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"
      +}
  2. First observed

TDQS

A4/5.0
Behavior4/5

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

The description clarifies that the operation is non-cascading and permanent, which is critical behavioral information beyond what the annotations provide. However, it doesn't mention failure cases, return behavior, or idempotency, which would be relevant for a destructive operation.

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?

Two short, dense sentences with minimal fluff. Every word contributes to the meaning, and the key qualifiers (typo: 'only') are front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple two-parameter destructive operation, the description covers the essential context needed to understand its scope and side effects. A couple of minor aspects (e.g., what happens on success, idempotency) are already covered by the presence of an output schema and the annotations, so they aren't critical gaps.

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?

The description doesn't add to the parameter-level descriptions already present in the schema, but the schema is 100% complete with descriptions like "The View id. Pass this OR view_name." and "The View name, matched case-insensitively. Pass this OR view_id." The description is largely redundant with the schema, so it adds no new parameter-level context.

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?

"Permanently delete a Saved View" provides an explicit, specific verb and resource, going beyond the title and generic name. It clearly communicates the destructive nature and the object of the operation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clarifies the scope of the deletion ("only the saved definition"), but it doesn't explicitly contrast with sibling operations like update_view, create_view, or get_view. It lacks explicit 'use when' or 'use instead of' guidance, relying on the reader to infer when to choose this tool.

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.

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