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Glama

YouSpot

by com.youspot

Analyze LinkedIn posts

linkedin_analytics
Read-only

The user's LinkedIn post analytics from LinkedIn's own API (their connected account) — impressions, unique members reached, reactions, comments, and reshares. Use for questions like 'how many impressions did I get last month?' or 'how are my posts performing?'. Omit dates for lifetime totals; pass start_date/end_date for a window; set daily=true (with one specific metric) for a per-day series to describe trends. Numbers are live from LinkedIn, unlike get_my_linkedin_posts whose per-post counts come from a periodic sync.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dailyNoPer-day time series instead of one total. Requires start_date and a specific metric (not 'all').
metricNoWhich metric ('all' fetches every metric — the default). LinkedIn's API takes one metric per call, so 'all' costs five calls.
end_dateNoWindow end, YYYY-MM-DD (defaults to today).
start_dateNoWindow start, YYYY-MM-DD. Omit for lifetime.

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 annotation, the description discloses that numbers are live from LinkedIn, that 'all' requires five API calls because LinkedIn accepts one metric per call, and that daily series require one specific metric. These are meaningful behavioral details not present in the annotation.

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 compact yet information-dense, with purpose, metrics, example usage, date behavior, daily-mode constraints, and sibling differentiation all included in four sentences. Every sentence earns its place and the most important distinction is 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?

For a read-only analytics tool with four optional parameters and no output schema, the description covers what data is returned, how parameter combinations change behavior, and how it differs from the closest sibling. An agent has enough context to select and invoke it correctly.

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 already 100%, so the baseline is 3, but the description adds useful interpretation: omitting dates means lifetime totals, passing start/end gives a window, and daily=true is for describing trends. It reinforces and slightly extends the schema without introducing all-new semantics.

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 tool as providing LinkedIn post analytics with an explicit metric list: impressions, unique members reached, reactions, comments, and reshares. It also names the sibling get_my_linkedin_posts as different, so an agent can distinguish this analytics tool from raw post-fetching.

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 gives concrete example questions that should route an agent here, and it explicitly contrasts with get_my_linkedin_posts by noting the live vs. synced data source. It also explains when to omit dates, pass a date window, or use daily=true, which is strong usage guidance.

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