Instagram MCP Server

send_reaction

Send reactions to Instagram messages by specifying recipient, message ID, and reaction type like love, like, haha, wow, sad, or angry.

Instructions

Send a reaction to a message on Instagram

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "recipientId": "<recipientId>",
      "messageId": "<messageId>",
      "reaction": "<reaction>"
    },
    "name": "send_reaction"
  }
}

Input Schema

Name	Required	Description
`recipientId`	Yes	The user ID
`messageId`	Yes	The message ID to react to
`reaction`	Yes	Reaction type (e.g., love, like, haha, wow, sad, angry)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Dec 12, 2025

TDQS

C2.9/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action ('send') but does not clarify permissions required, rate limits, whether the reaction is reversible, or what happens on success/failure. For a mutation tool with zero annotation coverage, this is a significant gap in transparency.

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, efficient sentence that directly states the tool's purpose without unnecessary words. It is appropriately sized and front-loaded, making it easy for an agent to parse quickly.

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 the tool's complexity (a mutation with 3 parameters), lack of annotations, and no output schema, the description is incomplete. It does not address behavioral aspects like error handling, response format, or usage constraints, which are critical for an agent to invoke the tool correctly in a real-world scenario.

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 the schema already documents all three parameters with clear descriptions. The description adds no additional meaning beyond what the schema provides, such as examples of reaction usage or context for recipientId/messageId. Baseline 3 is appropriate when the schema does the heavy lifting.

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 action ('send a reaction') and target resource ('to a message on Instagram'), which is specific and unambiguous. However, it does not explicitly differentiate from sibling tools like 'send_message' or 'reply_to_comment', which might involve similar messaging contexts but different actions.

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?

The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites (e.g., needing an existing message), exclusions, or comparisons to siblings like 'send_message' or 'reply_to_comment', leaving the agent to infer usage context solely from the tool name and parameters.

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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