instagram-mcp

get_messages

Retrieve messages from an Instagram conversation thread, including sender, text, media type, and timestamps; paginate with amount and offset.

Instructions

Get messages from a thread.

Args: thread_id: ID of the thread to get messages from. amount: Maximum number of messages to fetch (default: 20). offset: Skip the N most recent messages (default: 0).

Returns: Object with 'thread_id', 'messages' (sender, text, media_type, timestamp, seen_since when viewer's, share for a shared reel/post/story), 'count', 'offset', 'has_more'.

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": {
      "thread_id": "<thread_id>"
    },
    "name": "get_messages"
  }
}

Input Schema

Name	Required	Description	Default
`amount`	No
`offset`	No
`thread_id`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 7, 2026

TDQS

B3.3/5.0

Behavior3/5

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

With no annotations, the description carries the full burden. It does reveal meaningful behavior (offset skips the most recent messages, has_more signals pagination, seen_since and share appear conditionally), but it never says whether fetching marks messages read or what access is needed — an important omission given mark_read is a sibling.

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?

Front-loaded with the one-line purpose, then a clearly ordered Args block and a Returns block. Every line is relevant and there is no filler, though the Returns enumeration is somewhat long given an output schema already exists.

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

Completeness3/5

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

For a simple three-parameter read tool with an output schema, the core information is present. The gaps are behavioral and navigational: no side-effect disclosure (does reading mark messages seen?) and no routing versus sibling read tools, which matters in a 28-tool namespace.

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 description coverage is 0%, yet the description documents all three parameters with semantics beyond the schema: amount is a maximum, offset skips the N most recent (not oldest) messages, and thread_id identifies the source thread. This compensates well for the bare schema, though it doesn't state bounds for amount.

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?

States a specific verb+resource: 'Get messages from a thread.' An agent knows exactly what it retrieves. However, it never distinguishes itself from close siblings like get_message_ids or get_chat_log, so the agent cannot tell from the description alone which of these to pick.

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?

There is no when-to-use guidance at all — no mention of when to prefer this over get_message_ids, get_chat_log, or search_threads, and no prerequisites or exclusions. The Args section documents inputs but gives no selection criteria.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.