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Glama

YouSpot

by com.youspot

Get connection details

get_connection_details
Read-only

Get the full profile for one of the user's LinkedIn connections: work history, education, skills, and their About summary. Use this after search_connections when you need depth on a specific person. Identify them by name, or by linkedin_url for an exact match. A found:false response carries the user's imported-connection count: if no_imported_data is set, nothing was searched, so report the missing import rather than a missing person.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoThe connection's name (full or partial).
linkedin_urlNoExact LinkedIn profile URL, if known.

Schema Changelog

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

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Beyond the readOnlyHint annotation, the description discloses subtle response behavior: found:false carries the imported-connection count, and no_imported_data means nothing was searched, so the agent should report a missing import rather than a missing person. This is valuable non-obvious behavior that annotations alone do not provide.

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?

Four sentences, each carrying distinct value: what the tool returns, when to use it, how to identify the target, and the important edge-case response. The most important information is front-loaded, and there is no filler.

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?

The description covers the tool's purpose, scope, invocation guidance, and the key false-response edge case despite having no output schema. It does not fully describe the success response structure, but the listed profile fields provide enough framing for an agent to understand what will come back.

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%, so the baseline is 3, but the description adds matching semantics: name is the general identifier and linkedin_url is specifically for an exact match. This helps the agent choose between the two optional parameters more effectively than the schema alone.

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 clearly identifies the resource (a LinkedIn connection profile) and the specific data returned (work history, education, skills, About summary). It distinguishes the tool from the broader search_connections sibling by framing this as the depth-focused follow-up.

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 explicitly says to use this after search_connections when depth on a specific person is needed. It also gives practical guidance on how to identify the target (by name or linkedin_url), making the invocation context clear.

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.

Resources