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YouSpot

by com.youspot

Update brain object

set_graph_object_fields

Rename or re-describe an object in the user's graph, or set any of its type-specific fields: a contact's phone, email, headline or location, a company's funding stage, and so on. 'name' and 'description' work on every type (files included); other field names come from the type's field dictionary, which get_graph_object returns as 'editable_fields'. Read that first rather than guessing a name, because a name the dictionary does not have is skipped. Pass null to clear a field. Identify the object by object_id (search_graph_objects returns it).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fieldsYesField name -> new value (e.g. {'name': 'Q3 deck'} or {'headline': 'CTO at Acme'}).
object_idYesThe object to update (exact id).

Schema Changelog

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

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Beyond the readOnlyHint=false annotation, the description discloses two critical runtime behaviors: unknown field names are silently skipped rather than erroring, and passing null clears a field. It also notes that 'name' and 'description' are universally valid across types — exactly the failure mode and value semantics an agent needs before invoking.

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?

Purpose is front-loaded and the paragraph is dense but efficient, with each sentence carrying distinct information. Minor redundancy between 'other field names come from the type's field dictionary' and 'a name the dictionary does not have is skipped' costs a point for perfect economy.

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 2-param mutation tool with 100% schema coverage, the operational essentials are present: field-name sourcing, silent-skip failure, null semantics, and ID discovery via search_graph_objects. With no output schema, the description omits the return value and behavior for a non-existent object_id, which are the main gaps.

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?

Schema coverage is 100%, so the baseline is 3, but the description adds substantial meaning beyond the schema's example-based parameter docs: field names are validated against the type's dictionary, unknown names are silently skipped, and null clears a value. It also clarifies that object_id comes from search_graph_objects, enriching both parameters.

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?

Description opens with a specific verb+resource ('Rename or re-describe an object in the user's graph') and concrete type-specific examples (contact phone/email/headline/location, company funding stage). This clearly distinguishes it from the many graph siblings: it is neither creation (create_graph_object), deletion (delete_graph_object), reading (get_graph_object), nor search (search_graph_objects).

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?

Provides explicit prerequisites: read the type's field dictionary from get_graph_object ('editable_fields') before choosing field names, and obtain object_id from search_graph_objects. This is clear context, but there is no explicit when-not-to-use statement naming alternatives such as create_graph_object, so it stops short of a 5.

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