TwitterAPIs vs Sf MCP — MCP Server Comparison | AllMCPs
Side-by-Side Model Context Protocol Comparison
TwitterAPIs vs Sf MCP
In-depth architectural comparison of the TwitterAPIs and Sf MCP MCP servers. Compare execution transports, security boundaries, tool capabilities, quality scores, and ready-to-paste client installation snippets for Claude, Cursor, Windsurf, and VS Code.
At a Glance & Executive Verdict
TwitterAPIs
Social Media · Local stdio
Quality: 52/100 (Good) | Auth: No auth required
Sf MCP
Social Media · Local stdio
Quality: 57/100 (Good) | Auth: other
Verdict Summary: Choose TwitterAPIs if you need specialized Social Media tools running via a local process. Choose Sf MCP if your workspace requires Social Media integration with local subprocess execution. Both servers can be configured concurrently in your client's mcpServers manifest.
Which MCP Server Should You Choose?
Choose TwitterAPIs when:
You need dedicated capabilities in the Social Media domain.
You prefer local stdio subprocess transport architecture.
Your security boundary fits: No auth required (Free / Open Source).
Official MCP server for twitterapis.com. Read and write Twitter/X: search, users, tweets, DMs.
Connect AI agents to Signal Found's proprietary Reddit outreach network. Find prospects posting about problems your product solves, send personalized DMs at scale via your own Reddit account or a managed bot network of hundreds of accounts, and manage a full outreach CRM — all without leaving your AI client.
Search tweets with X operators (`from:`, `min_faves:`, `since:`, `filter:links`, etc.)
twitter_user_search
Find user accounts by name or keyword
twitter_user_info
Full profile by handle (bio, counts, verification, location)
twitter_user_info_by_id
Full profile by numeric user id
twitter_user_status
Is an account alive, suspended, or deleted
twitter_user_about
A user's structured About object (category, professional/business labels, verification + identity-verification flags, joined date, and X's 'About this account' transparency panel)
Ready-to-Paste Client Configurations
Paste either (or both) of these JSON server blocks into your client config file (e.g. claude_desktop_config.json or ~/.cursor/mcp.json).
TwitterAPIs is categorized under Social Media and uses a local stdio subprocess. In contrast, Sf MCP belongs to Social Media using local stdio subprocess. Select TwitterAPIs when you need capabilities focused on social media and Sf MCP when you require tools for social media.
Follow relationship between two user ids (who follows whom)
twitter_user_tweets
A user's recent original tweets (replies excluded)
twitter_user_tweets_and_replies
A user's full timeline (tweets + replies)
twitter_user_tweets_complete
A large batch of a user's tweet history per call (see [paging note](#paging-twitter_user_tweets_complete))
twitter_user_media
Images and videos a user has posted
+68 more tools listed on main page
Sf MCP Tools (40)
sf_health
Preflight check for MCP -> onboard_api connectivity and auth context.
Use this first in a new session to confirm:
- backend is reachable
- current MCP session auth state
- whether a default client id is configured
login_with_client_id
Authenticate MCP session to a Signal Found client account.
Run this at the start of each session before business tools.
Most tools require authenticated context and will use this session client id unless
you pass an explicit `client_id` argument.
current_client_context
Return the currently authenticated client context for this MCP server session.
agent_quickstart
Zero-context onboarding playbook for agents using this MCP server.
Returns the recommended call sequence, common guardrails, and recovery hints.
logout_client_context
Clear the active authenticated client context for this MCP server session.
list_products
List all products for a client (slug, display_name, product_unique, folder_id).
get_product_tree
Get nested folders and products for a client, equivalent to frontend product tree.
create_new_account
Create a brand-new Signal Found client account for onboarding.
Requires:
- `business_name`
- `email`
Returns created `client_id` and (by default) logs this MCP session into it.
Next step after success:
- call `create_new_product`
create_new_product
Create a product and initialize the agent onboarding session context.
The response includes:
- product creation result
- context packet (existing artifacts + screenshot uri)
- prompt pack + version metadata
Prerequisite:
- authenticated session via `login_with_client_id` (or provide `client_id` explicitly)
Next step after success:
- call `run_full_agentic_onboarding` (or run staged tools manually)
get_onboarding_prompt_pack
Fetch server-curated prompt contracts that define required onboarding outputs.
Use this when an agent needs exact formatting/expectations before generating:
- clarifications
- conversation transcript
- market positioning
- keywords/subreddits
Use `artifact` for focused contracts:
- clarifications
- market_position
- conversation
- keywords
- subreddits
submit_onboarding_artifacts
Validate and persist core onboarding artifacts for a product/session.
Prerequisites:
- prompt pack must be acknowledged for `session_id`
- payloads should match the artifact schemas below
Common use:
- called by `run_full_agentic_onboarding`
- can also be used for staged/recovery runs
Expected formats:
- market_position: patch object with market position keys
- conversion_notes: {'Product Name','Payment Terms/Plans','General Notes'}
- funnels: [{'url','description','primary_use_case', optional 'qualification'}]
submit_agent_targeting
Persist targeting artifacts (keywords/subreddits) and return policy/preview.
Prerequisites:
- prompt pack must be acknowledged for `session_id`
- artifacts should already be saved for best results
Typical next step:
- approve targeting (done automatically by `run_full_agentic_onboarding` when enabled)
Formats:
- keywords: list[str]
- subreddit_groups: [{'subreddits': ['name1','name2']}]