Skip to main content
Glama
Sign In

X (Twitter) MCP by InsightfulPipe

by com.insightfulpipe
OverviewDiscussionsAdmin

Server Details

Hosted X (Twitter) MCP server for Claude, ChatGPT and Cursor, with each action switched per account.

Ownership verified
Status
Healthy
Uptime
100.0% over the last 24 hours
OAuth
Works in Glama
Last Tested
Transport
Streamable HTTP · MCP 2025-11-25
URL
Repository
Insightful-Pipe/x-organic-mcp-server
GitHub Stars
0
ChatGPT
–
Claude
–

TDQS

A3.9/5.0

Scored across 4 tools

Disambiguation4/5

The four tools have reasonably distinct roles: query_contexts for discovery, query_data for reads, execute_action for writes, and prompts_menu for prompt catalog access. However, query_data and execute_action share nearly identical workflow preambles, which could cause some confusion for an agent deciding which final step to use.

Naming Consistency4/5

Three tools follow a clear verb_noun pattern (execute_action, query_contexts, query_data), while prompts_menu is a minor deviation (noun_noun). The overall convention is still predictable and readable.

Tool Count4/5

Four tools are appropriate for a gateway-style server that bundles a multi-step workflow into distinct phases. The count is lean but each tool has a clear purpose, though the heavy reliance on query_contexts for metadata makes the surface feel slightly indirect.

Completeness4/5

The surface covers discovery, read queries, write actions, and prompt browsing, which are the core needs of the stated workflow. However, the generic execute_action and query_data tools depend on an external available_actions catalog, leaving explicit lifecycle operations (e.g., delete, update) implicit rather than exposed.

Available Tools

4 tools
execute_actionX Execute ActionA
Destructive
Inspect

Execute X action (Step 3 (FINAL) of workflow).

REQUIRED: Complete these steps first using query_contexts:

  1. request='accounts' - Get workspace_id, brand_id, account_id AND available_actions

  2. request='actions_details' with actions=['xxxxxx', 'xxxxxx', 'xxxxxx'] - Get detailed body structure for specific action(s) using actions from available_actions

IMPORTANT: Always call step 2 to see the exact body structure before executing.

Rely on info from steps 1-2 to construct the action body. Follow the schema exactly.

Inputs: body (required). Returns: object

prompts_menuX PromptsA
Read-only
Inspect

Browse the X prompt catalog.

  1. request='menu' – List public/system prompts (optionally pass workspace_id + prompt_type).

  2. request='prompt_details' – Fetch prompt body by prompt_id. Set is_custom=True + workspace_id for workspace prompts.

Inputs: request (required), is_custom, prompt_id, prompt_type, +1 more. Returns: object

query_contextsX Setup HelperA
Read-only
Inspect

Helper for planning InsightfulPipe X queries and write actions.

Step 1: request='accounts' – Retrieve workspace_id, brand_id, account_id, other source metadata, AND available_actions (all tables/actions you can query or execute).

Step 2: request='actions_details' with actions=['xxxxxx', 'xxxxxx', 'xxxxxx'] – REQUIRED before querying. Replace with actions from available_actions to get the exact request body, required identifiers, fields, time ranges, and notes for each action.

Optional: request='available_actions' – Standalone call to list all available tables/actions (already included in accounts response).

Optional: request='brands' with brand_id + workspace_id to pull brand guidelines.

Inputs: request (required), actions, brand_id, platform, +1 more. Returns: object

query_dataX Data QueryA
Read-only
Inspect

Query X data (Step 3 (FINAL) of workflow).

REQUIRED: Complete these steps first using query_contexts:

  1. request='accounts' - Get workspace_id, brand_id, account_id AND available_actions

  2. request='actions_details' with actions=['xxxxxx', 'xxxxxx', 'xxxxxx'] - Get detailed body structure for specific action(s) using actions from available_actions

IMPORTANT: Always call step 2 to see the exact body structure before querying.

Rely on info from steps 1-2 to construct the query body. Follow the schema exactly.

Inputs: body (required). Returns: object

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    Related MCP Connectors

    Related MCP Servers

    • X MCP Server

      siddheshutd
      A
      license
      B
      quality
      D
      maintenance
      MCP server for interacting with the X platform (Twitter) via MCP clients like Claude, Cursor AI, and Windsurf AI.
      20
      18 npm
      6
      MIT

    • x-mcp

      rthomas24
      A
      license
      B
      quality
      D
      maintenance
      An MCP server for operating an X (Twitter) account via an agent, with built-in For You algorithm rules, pay-per-use API cost tracking, and human approval handoffs for restricted actions.
      23
      MIT

    • Crossbar

      prantikmedhi
      A
      license
      Not graded
      quality
      C
      maintenance
      Turns a user's own X (Twitter) account into an MCP server and CLI so AI agents can read timelines, search, inspect threads, mentions, and DMs, and perform actions such as posting, replying, liking, reposting, bookmarking, following, muting, blocking, and messaging as the signed-in user without an API key or developer account.
      1
      MIT
    Try in Browser
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    Glama MCP Gateway

    Add one secure layer between your agents and this server.

    Resources