Polar.sh Production▌
by polarsource
Polar.sh Production — remote MCP server accessible from most AI clients for reliable, low-latency connections and simple
Polar offers a remote MCP server that you can connect to from most AI clients.
github stars
★ 1.6K
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
best for
- / Financial data analysis workflows
- / AI applications needing finance integration
- / Remote financial service access
capabilities
- / Access Polar.sh financial data
- / Stream data via HTTP transport
- / Connect from multiple AI clients
- / Query financial services remotely
what it does
Connects to Polar.sh's remote MCP server for financial data and services. Works with most AI clients through HTTP streaming.
about
Polar.sh Production is an official MCP server published by polarsource that provides AI assistants with tools and capabilities via the Model Context Protocol. Polar.sh Production — remote MCP server accessible from most AI clients for reliable, low-latency connections and simple It is categorized under finance.
how to install
You can install Polar.sh Production in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server supports remote connections over HTTP, so no local installation is required.
license
MIT
Polar.sh Production is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
FAQ
- What is the Polar.sh Production MCP server?
- Polar.sh Production is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
- How do MCP servers relate to agent skills?
- Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
- How are reviews shown for Polar.sh Production?
- This profile displays 26 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.6 out of 5—verify behavior in your own environment before production use.
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★26 reviews- ★★★★★Kofi Thompson· Dec 16, 2024
Polar.sh Production has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Ganesh Mohane· Dec 4, 2024
Strong directory entry: Polar.sh Production surfaces stars and publisher context so we could sanity-check maintenance before adopting.
- ★★★★★Sakshi Patil· Nov 23, 2024
Polar.sh Production is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Nia Tandon· Nov 7, 2024
Polar.sh Production is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Kofi Sanchez· Oct 26, 2024
We wired Polar.sh Production into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Chaitanya Patil· Oct 14, 2024
We evaluated Polar.sh Production against two servers with overlapping tools; this profile had the clearer scope statement.
- ★★★★★Benjamin Li· Sep 21, 2024
Polar.sh Production has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Min Sharma· Aug 12, 2024
According to our notes, Polar.sh Production benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Yash Thakker· Jul 15, 2024
Useful MCP listing: Polar.sh Production is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★Benjamin Kim· Jul 3, 2024
We wired Polar.sh Production into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
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