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Publora MCP Server

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linkedin_account_stats

Retrieve aggregated engagement metrics for a LinkedIn account, including impressions, reactions, comments, and shares, with daily or total aggregation.

Instructions

Get aggregated engagement statistics for a LinkedIn account

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
platformIdYesPlatform connection ID, e.g. 'linkedin-XxxYyy'
queryTypesNoMetrics: ['IMPRESSION', 'REACTION', 'COMMENT', 'SHARE']
aggregationNoAggregation type (default: TOTAL)
Behavior2/5

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

With no annotations, the description bears full burden for behavioral disclosure. It only states the tool 'gets' data, but does not reveal whether it is safe/idempotent, has rate limits, or what side effects occur (none expected, but not stated).

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?

A single sentence that is front-loaded and to the point. While concise, it could be slightly more informative without losing brevity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description is too brief for a 3-parameter tool with no output schema. It does not explain the returned data structure, aggregation behavior, or whether results are paginated. With no annotations, more context is needed.

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

Parameters3/5

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

Schema description coverage is 100%, so baseline is 3. The description itself does not elaborate on parameters, but the schema includes good descriptions (e.g., platformId format, metric list, aggregation values). No additional value from the tool description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it retrieves 'aggregated engagement statistics for a LinkedIn account', specifying the resource and action. However, it does not explicitly differentiate from similar tools like 'linkedin_post_stats' or 'linkedin_followers', leaving some ambiguity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives (e.g., when to choose account-level vs post-level stats). The description lacks any context about prerequisites or typical scenarios.

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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