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Glama

YouSpot

by com.youspot

Delete brain objects in bulk

delete_graph_objects

Delete EVERY object of one type from the user's graph, however many there are: what to reach for when they ask to clear out a whole category ('delete all my HubSpot contacts') rather than one record. Deleting one object is delete_graph_object instead. This delete is permanent, and at this scale it is the only kind that helps: a synced portal can be millions of records. Use only when the user explicitly asked for a category to go, never on your own initiative, and name one type per call (contacts and companies are two calls). In chat this tool deletes nothing: it counts the records and returns instructions for a confirmation card, and the user's click starts the deletion.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
object_typeYesThe type to clear out, e.g. 'hubspot_contact'. search_graph_objects and the brain's type counts both report types.

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?

The description discloses that deletion is permanent, that the scale can be millions of records, and crucially that the tool does not delete in chat but returns instructions for a confirmation card that the user must click. These behaviors go well beyond the sparse annotations and help the agent set correct expectations.

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 information-dense but every sentence earns its place: purpose, alternative, permanence/scale, explicit-consent rule, cardinality, and chat confirmation behavior. The key distinction is front-loaded and the sentence order supports fast decision-making.

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?

For a dangerous bulk-delete tool with one parameter and no output schema, the description covers what is deleted, when it is appropriate, its permanence, how the confirmation flow works in chat, and the constraints on the parameter. There is no significant missing context that would cause an agent to misuse it.

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?

With 100% schema coverage, the schema already documents object_type well, but the description adds the important cardinality constraint of naming exactly one type per call and gives a concrete example. It also points to search_graph_objects and type counts as sources for valid types, adding useful context beyond the schema.

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 leads with a specific verb and resource: delete every object of one type from the user's graph. It explains the bulk category-clearing use case with an example and explicitly contrasts it with delete_graph_object for single-record deletion, distinguishing it from the most similar sibling.

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 states exactly when to use the tool (user asks for an entire category to go), when not to (single object → delete_graph_object), and adds constraints: only after explicit request, one type per call, and that in chat it only schedules deletion via a confirmation card. This is explicit guidance an agent can act on directly.

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

A3.8/5.0
Disambiguation4/5

Most tools are scoped to a distinct resource and action, and descriptions do a good job separating close pairs like search_connections vs ask_about_connections or get_my_linkedin_posts vs linkedin_analytics. However, the multiple deletion tools (delete_graph_object, delete_graph_objects, purge_graph_object) and the several file-reading tools are easy to confuse without reading the descriptions carefully.

Naming Consistency4/5

The vast majority of tools follow a clear verb_noun pattern such as create_, get_, list_, search_, send_, and delete_. A handful of noun-phrase outliers like linkedin_analytics, mutual_connections, top_message_correspondents, and what_needs_attention break the pattern, so it is highly consistent but not perfect.

Tool Count1/5

64 tools is an extreme count, far beyond the typical well-scoped 3-15 tool range and even beyond the 25+ threshold for 'too many'. While the server covers many integrations, this many tools creates a heavy navigation burden and would be better split into focused servers per domain.

Completeness3/5

Core graph/CRM operations and read-side integration coverage are strong, with search, get, list, and create tools across most domains. However, there are notable dead ends: no delete_calendar_event, no tracker management beyond create_tracker, and set_follow_up explicitly lacks a read-back query tool, so some natural user requests cannot be completed through the toolset.

Resources