instagram-mcp

get_chat_log

Retrieve an Instagram thread as a readable chronological plain-text chat log optimized for LLM analysis, smaller than JSON; use for text summaries, not structured fields.

Instructions

Get conversation as a readable chat log. Optimized for LLM analysis.

Plain-text chronological log, ~5x smaller than JSON. Use get_messages for structured fields.

Args: thread_id: ID of the thread to get messages from. amount: Maximum number of messages to fetch (default: 50). offset: Skip the N most recent messages (default: 0).

Returns: Object with 'thread_id', 'log' (plain text), '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_chat_log"
  }
}

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

A4.6/5.0

Behavior4/5

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

No annotations are provided, so the description carries the burden; it discloses the output medium (plain text vs JSON), the size tradeoff, and the pagination behavior ('offset' skips the most recent messages). It does not cover permissions, prerequisites, or thread-not-found behavior, but for a read-only fetch these are minor omissions.

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 purpose and the key differentiator in the first two lines, then a compact Args list. The Returns block is partly redundant with the existing output schema, but it costs little and the whole description is short.

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

Completeness4/5

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

For a three-parameter read tool with an output schema present, the definition covers purpose, alternative, all parameter semantics, and result shape. Only behavioral edges like permission requirements or empty-thread behavior are absent, which is acceptable given the output schema handles return values.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must compensate and does: thread_id is identified as the thread to pull messages from, amount is the max messages with its default, and offset's non-obvious semantics (skip the N most recent, not the first N) are spelled out.

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?

States a specific verb+resource ('Get conversation as a readable chat log') and immediately characterizes the output form (plain-text, ~5x smaller than JSON). It explicitly distinguishes itself from the sibling get_messages, which returns structured fields, so an agent can route without opening either schema.

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?

Names the alternative ('Use get_messages for structured fields') and gives the selecting condition (readable log optimized for LLM analysis, ~5x smaller). That is explicit when-to-use plus an alternative, leaving little to inference.

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