Table of Contents
Overview
In the rapidly evolving landscape of AI agents and Large Language Model (LLM) tooling, building robust and reliable tools can often be a complex endeavor, fraught with protocol intricacies and infrastructure management challenges. Enter Gram Functions, a TypeScript-native framework announced by Speakeasy on November 17, 2025, designed to dramatically streamline the creation and deployment of AI agent tools. It empowers developers to define sophisticated tools directly in TypeScript code and effortlessly deploy them as MCP (Model Context Protocol) servers to the Gram cloud platform, abstracting away the complexities of protocol knowledge and infrastructure management. If you’re a TypeScript developer looking to extend the capabilities of AI agents working with Claude Desktop, Cursor, ChatGPT, or other MCP-compatible clients, Gram Functions offers an elegant and efficient solution.
Key Features
Gram Functions delivers comprehensive capabilities designed to make AI tool development intuitive, powerful, and production-ready for enterprise environments.
TypeScript-Native Tool Definition: Define AI agent tools directly in TypeScript code using a clear, minimal structure comprising four components: name (unique identifier), description (human-readable explanation helping agents understand usage), inputSchema (Zod schema defining expected parameters with type safety), and async execute function (containing business logic), ensuring full type safety and developer familiarity.
Zod Schema Validation: Leverage the power of Zod (specifically zod/mini for minimal bundle size) for robust input schema validation, ensuring that your tools receive well-formed and expected data from AI agents with compile-time type checking.
One-Command Deployment: Simplify your deployment pipeline with straightforward two-command workflow: npm run build to compile TypeScript project, followed by npm run push to deploy to Gram platform, making it incredibly easy to push tools to production.
Serverless Managed Infrastructure: Benefit from Gram’s serverless hosting on AWS, which handles all infrastructure management, automatic scaling to millions of requests instantly with sub-second cold starts, and maintenance, allowing you to focus purely on tool logic without operational overhead.
MCP Protocol Abstraction: Gram Functions completely abstracts the underlying Model Context Protocol communication layer, freeing developers from needing deep JSON-RPC 2.0 protocol knowledge to build MCP-compatible tools accessible across the AI ecosystem.
Open-Source Framework on GitHub: The framework is open source under permissive license, fostering community contributions, transparency in development, and allowing developers to see implementation details, contribute improvements, or adapt for custom use cases.
Tool Remixing Across Functions: Facilitates reuse and combination of existing tools across different function projects, enabling more complex and modular agent capabilities through toolset composition without code duplication.
OpenAPI Integration: Seamlessly integrate with OpenAPI specifications, allowing developers to combine OpenAPI-generated tools with custom Gram Functions in single MCP servers, providing flexibility to wrap existing APIs alongside custom business logic.
Complex Workflow Orchestration: Design and implement sophisticated multi-step workflows that call multiple APIs, query databases, perform business logic, and synthesize results, allowing AI agents to perform intricate sequences of actions that would require multiple separate tool calls otherwise.
Database Query Support: Empower AI agents with ability to directly query databases through custom functions, opening possibilities for data-driven interactions and insights beyond HTTP API limitations.
Production-Grade Observability and Security: Built with enterprise needs in mind, Gram Functions provides robust observability features including request/response pair visibility, tool usage tracking, user behavior understanding, plus security measures including OAuth 2.1 compliance with PKCE flows, API key authentication, and optional bring-your-own OAuth provider.
Local Development with MCP Inspector: Run Gram Functions locally as MCP server using pnpm run dev command with MCP Inspector for testing and development before deployment, enabling rapid iteration cycles.
Branch Deployments and Versioning: Automatic preview servers for pull requests, version control for deployments, and ability to test changes before production, supporting modern CI/CD workflows.
How It Works
Gram Functions streamlines AI tool development through an intuitive workflow designed for TypeScript developers familiar with modern development practices.
Getting started begins by scaffolding a new project using npm create @gram-ai/function command (or pnpm create @gram-ai/function@latest –template gram), which creates project structure with example tools and minimal framework setup ready for development.
Developers then define their AI agent tools using TypeScript with the Gram Functions API. Each tool definition contains four essential components structured as an object: name as unique string identifier that agents use to call the tool, description as human-readable string explaining tool purpose and when agents should use it, inputSchema as Zod schema object defining expected parameters with full type safety and validation, and async execute function receiving context (ctx) and validated input parameters containing core business logic.
A minimal tool example looks like:
import { Gram } from "@gram-ai/functions";
import * as z from "zod/mini";
const gram = new Gram().tool({
name: "greet",
description: "Greet someone special",
inputSchema: { name: z.string() },
async execute(ctx, input) {
return ctx.text(`Hello, ${input.name}!`);
},
});
export default gram;
Within the scaffolded project structure, developers write specific logic for their tools in TypeScript, leveraging the full power of the language ecosystem including async/await, external libraries, database connections, and complex business logic. Tools can call multiple APIs, perform calculations, query databases, synthesize information, and return structured results as JSON or text.
Once tool logic is complete, deployment is streamlined. Developers run npm run build to compile their TypeScript project into executable format, followed by npm run push to deploy compiled code to Gram’s cloud platform. The deployment process uploads code, registers tools, and makes them immediately available.
Gram then automatically hosts these tools as MCP servers on its serverless infrastructure built on AWS. This means all complexities of server management, load balancing, scaling to demand, uptime monitoring, and infrastructure maintenance are handled transparently by the platform.
Once deployed, these tools become immediately accessible to any MCP-compatible AI client including Claude Desktop (Anthropic), Claude Web, Cursor IDE, ChatGPT with MCP support, and other AI assistants implementing the Model Context Protocol, significantly extending their capabilities through custom business logic.
For complex workflows, Gram Functions excels at orchestrating multi-step operations. A single tool can fetch payment details, query customer history, check card expiry status, calculate risk scores, and synthesize all information into coherent response—operations that would otherwise require AI agents to make four separate tool calls, each potentially failing and compounding error rates.
Developers can organize tools flexibly—splitting functions into multiple projects for convenience, keeping every tool in one file, or structuring however makes sense for their workflow. MCP servers are then created by combining tools into toolsets within the Gram dashboard, enabling reusable tool composition across different server configurations.
Use Cases
Gram Functions enables diverse applications across industries, empowering AI agents with custom capabilities tailored to specific business needs and workflows.
Building AI Agent Tools for LLMs: Create custom tools allowing LLMs to interact with external systems, databases, and data sources beyond their training data, enabling agents to take real-world actions rather than just generating text.
Wrapping Internal APIs for AI Consumption: Easily expose your organization’s internal APIs to AI agents with appropriate abstractions, enabling automation of internal tasks, access to proprietary data, and integration with legacy systems not designed for AI interaction.
Orchestrating Multi-Step Workflows: Design complex sequences of actions that AI agents can execute atomically, such as processing orders from start to finish across CRM, inventory, payment, and shipping systems, reducing multiple tool calls to single reliable operation.
Database Query Tools for Agents: Develop tools allowing AI agents to fetch, insert, update, or delete data from databases directly through SQL queries or ORMs, enabling data-driven interactions beyond HTTP API limitations.
Payment Processing Automation: Create tools for AI agents to initiate and manage payment transactions through Stripe, PayPal, or internal payment systems, streamlining financial operations with proper security controls.
Customer Service Tool Creation: Empower AI assistants with tools to look up customer information from CRMs, manage support tickets in systems like Zendesk or Jira, retrieve order histories, process refunds, or provide personalized assistance based on customer context.
Complex Business Logic Encoding: Encapsulate intricate business rules and processes into tools that AI agents can invoke, ensuring consistent and compliant operations without requiring agents to understand nuanced business logic embedded in prompts.
MCP Server Development: For developers specifically targeting the Model Context Protocol ecosystem, Gram Functions provides high-level framework for building compliant servers without low-level protocol implementation details.
AI Assistant Capability Extension: Broaden the range of tasks and interactions that any MCP-compatible AI assistant can perform, from calendar management and email automation to data analysis and report generation.
Corporate Travel Assistance: Build AI-powered travel assistants that check availability, query booking systems, enforce travel policies, submit expense reports, and automate end-to-end travel workflows.
Failed Transaction Investigation: Create diagnostic tools that investigate payment failures by querying transaction details, customer history, card status, and risk scores, then synthesizing comprehensive analysis with recommended actions.
Real-Time Data Integration: Enable AI agents to access live data from APIs, databases, or internal systems, ensuring responses are based on current information rather than static training data.
Pricing
Gram Functions operates on usage-based pricing model with generous free tier designed to encourage experimentation and rapid iteration.
Free Tier: 1,000 tool calls per month included at no cost, allowing developers to test, develop, and deploy Gram Functions without upfront investment. Suitable for prototyping, personal projects, and low-volume production workloads.
Usage-Based Pricing: Beyond free tier, pricing scales based on actual tool call volume. Specific pricing tiers not publicly disclosed as of November 2025 launch—contact Speakeasy sales for custom quotes based on anticipated usage patterns.
Enterprise Features: Available on higher tiers and enterprise plans with custom pricing. Includes OAuth 2.1 compliance with Dynamic Client Registration and PKCE flows, bring-your-own OAuth authorization server integration, SSO and SCIM integration for team management, directory sync capabilities, role-based access control (RBAC), detailed audit trails for compliance, dedicated support, SLA guarantees, and self-hosted data plane options to keep API traffic within customer VPCs.
Infrastructure Costs: Serverless model means pay-only-for-actual-usage with sub-second cold starts and no persistent server costs, avoiding overprovisioning and minimizing waste compared to traditional always-on server deployments.
No Hidden Fees: No separate charges for infrastructure management, scaling, load balancing, monitoring, or platform maintenance—all included in usage-based pricing model.
Note: As Gram Functions launched November 2025, pricing structure may evolve based on market feedback and platform maturation. Check official Speakeasy/Gram website for current pricing details and tier specifications.
Pros and Cons
Understanding both advantages and limitations provides clarity for evaluating Gram Functions’ fit for AI agent tool development workflows.
Advantages
TypeScript-Native with Full Type Safety: Developers leverage existing TypeScript knowledge and benefit from compile-time type checking through Zod schemas, reducing runtime errors and improving code quality with IntelliSense support and refactoring capabilities.
Abstracts MCP Protocol Complexity: Eliminates need for developers to understand intricate details of Model Context Protocol JSON-RPC 2.0 specification, transport layers (stdio, HTTP, SSE), or message formatting, significantly lowering barrier to entry for AI tool development.
One-Command Deployment: Simplifies deployment process to two straightforward commands (npm run build, npm run push), making it fast and efficient to get tools live in production without complex CI/CD pipeline configuration.
Open-Source Framework: The framework’s open-source nature on GitHub promotes transparency, enables community collaboration and contributions, allows inspection of implementation details for security audits, and provides ability to adapt or fork for specialized use cases.
Handles Complex Multi-API Workflows: Designed specifically to manage and orchestrate intricate workflows involving multiple external API calls, database queries, and business logic within single tool execution, enabling sophisticated agent behaviors that would otherwise require multiple tool invocations with compounding failure rates.
Serverless Scalability: Automatically scales to meet demand spikes without manual intervention or capacity planning, ensuring high availability and performance from zero to millions of requests with pay-only-for-usage economics preventing overspending.
Production-Ready Observability: Built-in request/response visibility, tool usage analytics, and user behavior tracking move beyond simple status codes to provide actionable insights for debugging and optimization.
Enterprise Security Features: OAuth 2.1 compliance, API key authentication, bring-your-own OAuth provider support, role-based access control, and audit trails provide enterprise-grade security without custom implementation.
Local Development Support: Ability to run functions locally with MCP Inspector before deployment enables rapid iteration, testing, and debugging cycles without constant cloud deployments.
Disadvantages
Requires TypeScript Knowledge: While advantage for TypeScript developers, presents learning curve for teams unfamiliar with TypeScript ecosystem, typing systems, or modern JavaScript development practices.
Focused Subset of MCP Protocol: While abstracting complexity improves developer experience, it might not expose every granular detail, advanced feature, or edge case capability of full MCP specification for highly specialized use cases requiring low-level protocol control.
Newer Platform with Evolving Features: As relatively new platform launched November 2025, features and capabilities may still be evolving, which could mean occasional breaking changes, API updates, or additions requiring code modifications during platform maturation.
Requires Gram Platform for Hosting: Tools built with Gram Functions are designed specifically to be hosted on Gram’s cloud platform, meaning it’s not standalone framework for self-hosting MCP servers on own infrastructure, creating platform dependency and vendor lock-in considerations.
Limited Language Support: Framework exclusively supports TypeScript/JavaScript, not Python, Go, Rust, or other languages that may be preferred by certain development teams or required for specific use cases like performance-critical operations.
Cold Start Latency: While sub-second, serverless architecture inherently includes cold start delays for infrequently-called tools compared to always-warm dedicated server deployments, potentially affecting latency-sensitive applications.
Debugging Complexity: Serverless environments can make debugging more challenging than local development, particularly for complex multi-step workflows with external dependencies, though local development mode mitigates this concern.
How Does It Compare?
The MCP tool development and serverless deployment landscape in 2025 features diverse solutions serving various developer needs, deployment preferences, and organizational requirements. Understanding Gram Functions’ unique positioning requires examining specific alternatives.
Raw MCP SDK (Official Anthropic SDK)
The official Model Context Protocol SDK provided by Anthropic offers low-level implementation in Python, TypeScript, and Go. Requires developers to have deep knowledge of MCP specification including JSON-RPC 2.0 protocol details, transport layer management (stdio, HTTP with Server-Sent Events), message formatting, error handling, and server lifecycle management. Provides maximum flexibility and control but demands significant protocol expertise and custom infrastructure setup.
Gram Functions differentiates through high-level TypeScript-native framework that completely abstracts protocol complexity, allowing developers to focus on business logic rather than communication standards. While raw SDK provides granular control for specialized implementations, Gram Functions offers dramatically simplified development experience with built-in deployment and hosting, trading low-level control for rapid development velocity. Raw SDK suits teams requiring maximum customization or multi-language support, while Gram Functions targets TypeScript developers prioritizing speed to production.
AWS Lambda with Amazon Bedrock AgentCore
AWS Lambda provides general-purpose serverless compute for running arbitrary code with event triggers. Amazon Bedrock AgentCore (preview as of November 2025) extends Lambda for AI agent development with Strands Agents SDK, supporting Model Context Protocol integration through Gateway pattern combining tools from multiple sources including Tavily API and custom Lambda functions.
Building AI agent tools on AWS Lambda requires significant custom development including protocol handling implementation, deployment pipeline configuration with SAM or CDK, IAM role and policy creation, API Gateway or ALB setup for HTTP access, CloudWatch monitoring configuration, and cold start optimization. While powerful and flexible with full AWS ecosystem integration, it involves substantial operational overhead and infrastructure expertise.
Gram Functions provides purpose-built AI tool framework with MCP abstraction and integrated deployment eliminating manual infrastructure provisioning. One-command deployment contrasts with multi-step AWS setup requiring CloudFormation templates or infrastructure-as-code. Gram’s managed platform handles scaling, monitoring, and security by default versus manual AWS service configuration. AWS Lambda suits organizations deeply invested in AWS ecosystem requiring full control, while Gram Functions serves teams prioritizing rapid development with minimal operational burden.
Vercel Functions
Vercel Functions offers serverless function hosting integrated with Vercel’s edge network and Next.js framework, providing fast global deployment with automatic scaling. Designed primarily for web application backends with excellent frontend integration, automatic HTTPS, and branch deployments.
While Vercel Functions provides excellent developer experience for web applications, it lacks specific AI agent tool framework, MCP protocol abstraction, and integrated deployment tailored for LLM tool development that Gram provides. Building MCP-compatible tools on Vercel requires custom protocol implementation, manual HTTP/SSE transport handling, and separate tooling for MCP server registration with AI clients. Vercel excels for full-stack web applications with serverless API routes, while Gram Functions specializes in AI agent tool development with MCP ecosystem integration.
Cloudflare Workers
Cloudflare Workers delivers serverless compute running on Cloudflare’s global edge network with sub-millisecond cold starts, Workers KV for storage, and Durable Objects for stateful applications. Provides excellent performance at edge locations worldwide with competitive pricing.
Similar to Vercel, Cloudflare Workers offers general serverless platform lacking AI-specific tooling, MCP abstraction, and integrated LLM client connectivity. Developers must implement full MCP protocol layer, manage tool discovery and invocation patterns, and handle deployment to AI clients separately. Cloudflare Workers excels for latency-sensitive edge compute and global distribution, while Gram Functions provides purpose-built environment for AI tool development with simplified MCP server deployment.
OpenAI Agents SDK
OpenAI Agents SDK provides Python-first framework for building autonomous agents tightly integrated with OpenAI’s API ecosystem. Features swarm pattern for orchestrating agent handoffs, pre-built tools including web search and file access, and direct integration with Responses API for minimal setup overhead.
OpenAI Agents SDK focuses on agent orchestration within OpenAI ecosystem using proprietary tool format, while Gram Functions emphasizes vendor-agnostic MCP tool creation compatible across Claude, ChatGPT, Cursor, and other MCP clients. OpenAI SDK suits teams deeply committed to OpenAI models and ecosystem, while Gram Functions serves developers seeking interoperability across multiple AI platforms through standardized protocol.
Google Agent Development Kit (ADK)
Google ADK combines Gemini models with task router and A2A protocol featuring first-class MCP support. Announced at Google Cloud NEXT 2025, it provides production-ready reliability, deep GCP ecosystem integration, distributed tool handling with single agents managing multiple MCPToolsets, and observability integration with Cloud Trace and Comet.
ADK offers enterprise-focused architecture for Google Cloud customers with comprehensive GCP service integration including Vertex AI, BigQuery, Cloud Run, and managed authentication. Requires Python implementation (TypeScript support pending) and GCP familiarity representing learning curve for non-Google cloud users. Gram Functions provides cloud-agnostic TypeScript solution with simpler onboarding, while ADK delivers comprehensive platform for Google Cloud-committed organizations.
LangGraph with MCP Support
LangGraph provides graph-based orchestration framework for building stateful, multi-actor AI applications with cyclic workflows and human-in-the-loop patterns. Part of LangChain ecosystem with MCP integration enabling standardized tool discovery and execution within LangGraph workflows.
LangGraph emphasizes complex agent orchestration with graph-based state machines, conditional edges, and multi-agent coordination, while Gram Functions focuses specifically on individual tool creation with simplified deployment. LangGraph suits applications requiring sophisticated workflow control and state management, while Gram Functions targets developers needing to rapidly create and deploy individual tools consumed by various AI clients.
Stainless MCP Server Generation
Stainless automates TypeScript MCP server generation from OpenAPI specifications as part of CI/CD pipelines, providing type-safe client SDKs and server stubs with integrated testing frameworks. Focuses on code generation approach rather than runtime framework.
Stainless excels at generating MCP servers from existing OpenAPI documentation with automated updates from spec changes, while Gram Functions enables custom business logic implementation beyond HTTP API wrappers. Stainless suits teams with well-documented APIs seeking automated server generation, while Gram Functions serves developers writing custom tools with database queries, multi-step workflows, and business logic beyond simple API proxying.
Serverless Container Framework (SCF) v2
Serverless Container Framework v2 by Serverless Inc. provides unified development experience across AWS Lambda and ECS Fargate for hosting AI agents with flexible compute options, built-in API setup, Slack integration, EventBridge connections, and streaming response support from both Lambda and Fargate.
SCF v2 focuses on hosting complete AI agents with variable workloads (short-lived tools on Lambda, long-running agents on Fargate), while Gram Functions specifically targets tool creation with serverless execution. SCF v2 suits teams building full AI agent applications requiring custom infrastructure configuration, while Gram Functions serves developers creating individual tools consumed by existing AI clients through MCP protocol.
Key Differentiators
Gram Functions’ unique market position centers on several distinctive capabilities. TypeScript-native MCP framework provides high-level, type-safe API specifically designed for AI tool development versus general serverless platforms or protocol-agnostic SDKs requiring manual integration.
Complete MCP protocol abstraction eliminates need for developers to understand JSON-RPC 2.0 specification, transport layer implementation, or message serialization, dramatically lowering barrier to entry compared to raw SDKs. Built-in tool hosting on managed Gram platform with automatic scaling, monitoring, and security eliminates infrastructure provisioning and operational overhead required by AWS Lambda, Vercel, or Cloudflare deployments.
One-command deployment (build + push) workflow stands in stark contrast to complex multi-step setup processes involving CloudFormation templates, API Gateway configuration, IAM policies, and monitoring setup required for custom AWS solutions. Open-source framework transparency enables community contributions, security audits, and custom adaptations while maintaining compatibility with managed platform.
For maximum flexibility and multi-language support, raw MCP SDK or low-level protocol implementation provides granular control. For comprehensive agent orchestration with complex workflows, LangGraph or OpenAI Agents SDK deliver sophisticated patterns. For Google Cloud ecosystem integration, ADK offers first-class GCP service connectivity. For general serverless hosting, AWS Lambda, Vercel, or Cloudflare provide broader application support.
However, for TypeScript developers seeking fastest path from code to production MCP servers with minimal operational complexity, vendor-agnostic AI client compatibility, built-in enterprise features, and focus specifically on tool creation rather than full agent hosting, Gram Functions presents compelling specialized solution bridging developer experience with production infrastructure.
Final Thoughts
Gram Functions represents significant advancement in AI agent tool development, particularly for TypeScript developers seeking to extend LLM capabilities through Model Context Protocol without infrastructure complexity. By abstracting MCP protocol intricacies and offering TypeScript-native, serverless framework with one-command deployment, it dramatically lowers barrier to entry for creating production-ready AI agent tools.
The November 2025 launch by Speakeasy demonstrates commitment to simplifying AI integration challenges that emerged with MCP adoption. While Gram was initially focused on API-based MCP servers generated from OpenAPI specifications, Gram Functions expands capabilities to custom business logic, database queries, and complex workflows beyond HTTP endpoints—addressing real-world requirements where existing APIs don’t cleanly map to AI agent needs.
The framework’s open-source nature fosters transparency and community collaboration while the managed platform provides enterprise-grade infrastructure, observability, and security. Trade-offs include platform dependency on Gram hosting, TypeScript language limitation, and focused subset of MCP protocol potentially restricting highly specialized use cases.
For organizations deeply embedded in specific cloud ecosystems (AWS with Bedrock AgentCore, Google Cloud with ADK), vendor-specific solutions may offer superior integration despite higher complexity. For teams requiring maximum protocol control or multi-language support, raw MCP SDKs provide necessary flexibility. For comprehensive agent orchestration beyond tool creation, frameworks like LangGraph deliver sophisticated workflow patterns.
However, for development teams prioritizing rapid iteration, production deployment velocity, operational simplicity, and TypeScript ecosystem advantages while building MCP-compatible tools accessible across Claude, ChatGPT, Cursor, and other AI clients, Gram Functions delivers exceptional value. The generous free tier (1,000 tool calls monthly) encourages experimentation without financial commitment, while usage-based scaling ensures cost efficiency as applications grow.
As AI agent capabilities become increasingly critical to business operations, product experiences, and user interactions, the ability to rapidly create, deploy, and scale custom tools represents competitive advantage. Gram Functions positions itself as infrastructure layer abstracting complexity while maintaining flexibility, enabling developers to focus on business value rather than protocol implementation and operational concerns. If you’re ready to empower AI agents with custom logic, seamless integrations, and sophisticated workflows without infrastructure burden, Gram Functions warrants serious evaluation as your MCP tool development platform.