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Glama

YouSpot

by com.youspot

Delete brain object

delete_graph_object

Delete an object from the user's graph. Use only when the user explicitly asks to delete or remove a specific object — never to tidy up on your own initiative. The delete is soft: the object disappears from lists, search, and chat, but its data is retained and a restore is possible later. Identify the object by object_id (search_graph_objects returns it). Only objects the user owns can be deleted, and never their root user node. In chat, this tool does not delete directly — it returns instructions for showing the user a confirmation card, and the card performs the delete.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
object_idYesThe object_id of the object to delete.

Schema Changelog

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

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Beyond the readOnlyHint=false annotation, the description discloses key behavioral traits: soft delete, data retention, restore possibility, ownership restrictions, the root user node exclusion, and the special chat behavior where it returns confirmation instructions rather than deleting directly. This is exceptionally transparent.

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?

Every sentence earns its place: purpose, usage boundary, soft-delete semantics, parameter identification, ownership restriction, and chat-specific behavior are all covered with no redundancy. The description is dense but well-structured and front-loaded.

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 simple one-parameter schema and no output schema, the description provides all necessary context: what the tool does, when to use it, how to identify the target, what constraints exist, and what happens in chat. Nothing critical is missing for correct invocation.

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

Parameters4/5

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

Schema coverage is 100% and the single parameter is documented. The description adds value by explaining that object_id can be obtained from search_graph_objects and clarifying the object ownership constraint, which helps the agent select the correct value.

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 verb and resource ('Delete an object from the user's graph') and clearly defines scope. It distinguishes itself from related tools like purge_graph_object by explicitly noting the delete is soft and data is retained, so an agent can tell this tool apart from hard-delete alternatives.

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 gives explicit when-to-use guidance: only when the user explicitly asks to delete or remove a specific object, and never as proactive cleanup. It also names how to identify the object via search_graph_objects, providing a concrete usage route.

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

A4/5.0
Disambiguation5/5

Each tool targets a distinct resource and action, with clear boundaries even within overlapping domains like LinkedIn (search vs. free-form query vs. profile vs. summary) and graph deletion (soft single, bulk soft, permanent single). Descriptions explicitly cross-reference related tools to prevent misselection.

Naming Consistency4/5

The vast majority follow a consistent verb_noun pattern (get_, list_, search_, create_, delete_, etc.). A few noun-phrase exceptions like linkedin_analytics, mutual_connections, similar_objects, and what_needs_attention deviate slightly, but they are still descriptive and do not create confusion.

Tool Count2/5

At 66 tools this is far beyond the 25+ threshold considered too many, even though the server covers many integration domains. Each domain has a coherent subset, but the overall surface is heavy for agents to navigate and would benefit from consolidation or namespacing.

Completeness4/5

The set provides deep read/search coverage across Gmail, Slack, Calendar, LinkedIn, HubSpot, Obsidian, Twitter, and a graph store, with core write operations for calendar, drafts, Slack, and graph objects. Minor gaps exist—notably no calendar delete, no direct Gmail send to third parties (only drafts), and no LinkedIn post/message actions—but these appear deliberate and do not block typical workflows.

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