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mathieubernardi

meta-mcp-py

get_ig_media_insights

Retrieve Instagram post metrics like reach, likes, saves, and shares by providing a media ID.

Instructions

Statistiques d'une publication Instagram (portée, likes, saves, partages).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
metricsNo
media_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

No annotations exist, so the description must carry the full behavioral burden. It explains what statistics are returned but does not disclose whether the operation is read-only, what permissions are required, how the optional metrics parameter behaves, or what the response structure looks like.

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 a single short sentence with no filler. The resource is front-loaded and the metric examples add useful specificity without bloat.

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?

Given that this is a Facebook/Instagram Graph API-style insights endpoint with 0% schema coverage and no annotations, the description is too thin. It omits permission requirements, media_id semantics, metrics selection behavior, and any output details, leaving an agent without enough context to invoke the tool reliably.

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

Parameters2/5

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

Schema description coverage is 0%, so the description should clarify both parameters. It gives examples of metric values (reach, likes, saves, shares), which helps understand the 'metrics' parameter, but it never explains 'media_id' or how metrics should be supplied or defaulted.

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 the resource (a single Instagram publication) and the outcome (statistics: reach, likes, saves, shares). This distinguishes it from siblings like get_ig_account_insights and get_ig_audience_demographics, though the action verb is carried by the tool name rather than the description text.

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

Usage Guidelines3/5

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

The description implies usage: call this when you need per-post Instagram performance metrics. It does not explicitly say when not to use it or name alternatives, but the singular 'publication' and the listed metrics make the intended context reasonably clear relative to account or audience insight tools.

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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