Gram

Gram

Speakeasy
+
+

Related Products

  • Retool
    593 Ratings
    Visit Website
  • Cloudflare
    2,035 Ratings
    Visit Website
  • Google AI Studio
    30 Ratings
    Visit Website
  • Gemini Enterprise Agent Platform
    999 Ratings
    Visit Website
  • StackAI
    53 Ratings
    Visit Website
  • SCIKIQ
    14 Ratings
    Visit Website
  • LM-Kit.NET
    29 Ratings
    Visit Website
  • Flashcloud
    16 Ratings
    Visit Website
  • Parasoft
    148 Ratings
    Visit Website
  • Auth0
    1,067 Ratings
    Visit Website

About

Gram is an open source platform that enables developers to create, curate, and host Model Context Protocol (MCP) servers effortlessly, by transforming REST APIs (via OpenAPI specs) into AI-agent-ready tools without code changes. It guides users through a workflow: generating default tooling from API endpoints, scoping down to relevant tools, composing higher-order custom tools by chaining multiple calls, enriching tools with contextual prompts and metadata, and instantly testing within an interactive playground. With built-in support for OAuth 2.1 (including Dynamic Client Registration or user-authored flows), it ensures secure agent access. Once ready, these tools can be hosted as production-grade MCP servers, complete with centralized management, role-based access, audit logs, and compliance-ready infrastructure, including Cloudflare edge deployment and DXT-packaged installers for easy distribution.

About

Model Context Protocol (MCP) is an open protocol designed to standardize how applications provide context to large language models (LLMs). It acts as a universal connector, similar to a USB-C port, allowing LLMs to seamlessly integrate with various data sources and tools. MCP supports a client-server architecture, enabling programs (clients) to interact with lightweight servers that expose specific capabilities. With growing pre-built integrations and flexibility to switch between LLM vendors, MCP helps users build complex workflows and AI agents while ensuring secure data management within their infrastructure.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Infrastructure teams requiring a tool to transform their endpoints into curated, context-rich MCP servers that are secure, testable, and instantly usable by LLMs

Audience

Developers and businesses looking for a standardized way to integrate LLMs with various data sources and tools to build scalable AI systems

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

$250 per month
Free Version
Free Trial

Pricing

Free
Open source
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Reviews/Ratings

Overall 5.0 / 5
ease 5.0 / 5
features 5.0 / 5

Pros & Cons from Real Users

Pros

  • MCP is one of the most important pieces of AI infrastructure right now because it gives agents a standard way to plug into the outside world. Instead of every AI app needing a custom integration for every database, SaaS tool, repo, file system, or internal API, MCP creates a common connection layer. That matters a lot. It makes AI agents feel less like isolated chat windows and more like real software that can read context, call tools, retrieve data, and take useful actions. I also like that MCP has momentum across the ecosystem. It is not just an Anthropic-only idea anymore. The fact that major AI tools and developer environments are adding MCP support makes it feel like a real protocol, not just another vendor feature.

Cons

  • MCP also raises the stakes. Once agents can access tools and data, security becomes a much bigger deal. Permissions, authentication, logging, prompt injection, tool poisoning, and accidental data exposure all need to be handled carefully. It can also get messy if teams expose too many tools without structure. An agent with a giant pile of vague tools is not automatically smarter. Good MCP servers need clean design, clear scopes, strong descriptions, and thoughtful permissions.

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Speakeasy
Founded: 2022
United States
www.speakeasy.com/product/gram

Company Information

Anthropic
Founded: 2021
United States
modelcontextprotocol.io

Alternatives

Alternatives

Stainless

Stainless

Anthropic
Flowise

Flowise

Flowise AI

Categories

Categories

Integrations

ChatGPT
Claude
Devin Desktop
Slack
Agent Builder
AgentKey
Agentcy
Claw Code
DigestSEO
Graft
Inkling
Koidex
Markup AI
Modern.ai
P-Image-Ideogram
SOCRadar Extended Threat Intelligence
Supersonic
Trust Wallet
t0 by Supernomial
xpander.ai

Integrations

ChatGPT
Claude
Devin Desktop
Slack
Agent Builder
AgentKey
Agentcy
Claw Code
DigestSEO
Graft
Inkling
Koidex
Markup AI
Modern.ai
P-Image-Ideogram
SOCRadar Extended Threat Intelligence
Supersonic
Trust Wallet
t0 by Supernomial
xpander.ai
Claim Gram and update features and information
Claim Gram and update features and information
Claim Model Context Protocol (MCP) and update features and information
Claim Model Context Protocol (MCP) and update features and information