Building an MCP Server from Scratch with the TypeScript SDK
A practical walkthrough of MCP server architecture, tool definitions, resource handling, and testing using the official TypeScript SDK.
March 21, 2026
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Blog
Articles, tutorials, and updates about the AI tools ecosystem.
The Security Implications of Connecting LLMs to External Tools
Giving AI models access to external tools introduces real security considerations. A practical look at the risks and how to mitigate them without giving up the benefits.
March 21, 2026
securitymcpai-safetyrisk-management
Building a Notification System Powered by AI Agents
An AI agent that monitors your systems and sends you smart, contextual notifications can replace the firehose of alerts that everyone ignores.
March 21, 2026
ai-agentsnotificationsautomationmonitoring
What I Learned Running MCP Servers in a Team Environment
Individual MCP setups are straightforward. Getting a whole team on the same MCP configuration, with consistent behavior and shared standards, introduces challenges nobody warned me about.
March 21, 2026
mcpteamscollaborationlessons-learned
Why Most AI Agents Fail at Multi-Step Reasoning
AI agents frequently break down when tasks require more than a few sequential steps. The failure modes are predictable and, once you understand them, largely avoidable.
March 20, 2026
ai-agentsreasoningreliabilitydebugging
Testing AI Skills: Approaches That Actually Work
Testing AI skills is fundamentally different from testing deterministic software. The outputs are variable, quality is subjective, and edge cases are infinite. Here are approaches that produce practical results.
March 20, 2026
ai-skillstestingqualitydevelopment
How to Debug an AI Agent That Keeps Making Mistakes
When an AI agent produces wrong results or gets stuck in loops, systematic debugging techniques reveal the root cause faster than guessing. Here is a practical approach.
March 20, 2026
ai-agentsdebuggingreliabilitydevelopment
How Error Messages from MCP Servers Affect Agent Behavior
When an MCP server returns an error, the quality of that error message determines whether the AI agent can recover or just gives up. Most servers write terrible error messages.
March 20, 2026
mcperror-handlingreliabilitydevelopment
How to Set Up AI-Powered Code Review with MCP
AI-powered code review catches bugs, style issues, and security problems before human reviewers see the PR. Here's how to wire it up with MCP servers and your existing Git workflow.
March 20, 2026
mcpcode-reviewgitautomation
Tool Use in AI Agents: Current Capabilities and Limitations
AI agents can use tools to read data, execute code, call APIs, and modify systems. But tool use is not as seamless as it appears, and understanding the gaps matters for building effective agents.
March 19, 2026
ai-agentstoolsmcpcapabilities
How Skill Libraries Reduce AI Development Time
Reusing proven AI skills instead of building from scratch compresses development timelines. Skill libraries provide tested, ready-to-use capabilities that accelerate AI-powered feature development.
March 19, 2026
ai-skillsdevelopmentproductivitylibraries
How AI Tools Are Adapting to Multimodal Capabilities
As AI models gain the ability to process images, audio, and video alongside text, the tools ecosystem is evolving to support these new modalities. The implications are broader than you might expect.
March 19, 2026
multimodaltrendscapabilitiesecosystem
The Difference Between AI Agents and AI Assistants
Everyone uses these terms interchangeably, but agents and assistants are genuinely different things. The distinction matters when you're deciding what to build.
March 19, 2026
ai-agentsai-assistantsarchitectureconcepts
The Consolidation Pattern in AI Tool Marketplaces
As the AI tool ecosystem matures, consolidation is beginning. Multiple tools serving the same niche are competing for users, and the dynamics favor a few winners per category.
March 19, 2026
ecosystemconsolidationmarketplacetrends
Comparing MCP Servers for the Same Use Case
When multiple MCP servers serve the same purpose, choosing between them requires comparing features, quality, security, and fit. A structured comparison process saves time and reduces regret.
March 18, 2026
comparisonmcpevaluationpractical-guide
How AI Agents Handle Ambiguity in User Instructions
Users rarely give perfectly clear instructions. How an agent interprets vague requests, fills in gaps, and decides when to ask for clarification determines whether the experience feels helpful or frustrating.
March 18, 2026
ai-agentsuxambiguitydesign
How to Contribute to the MCP Server Ecosystem
You've built something useful. Maybe it's an MCP server for a tool nobody else has integrated yet. Here's how to share it with the ecosystem and make it discoverable.
March 18, 2026
open-sourcecontributingmcpcommunity
MCP vs Function Calling: Understanding the Tradeoffs
Function calling and MCP both let AI models use external tools, but they solve different problems. When each approach makes sense depends on what you are building.
March 18, 2026
mcpfunction-callingarchitecturecomparison
How to Build an AI Agent for Competitive Intelligence
An AI agent that monitors competitors, tracks product changes, and surfaces relevant market signals can give you an information advantage without the manual research grind.
March 18, 2026
ai-agentscompetitive-intelligencebusinessautomation
What Nobody Tells You About MCP Server Latency
MCP server response times vary wildly depending on how the server is built, what it connects to, and how the AI client handles the round trip. The numbers matter more than you'd expect.
March 18, 2026
mcpperformancelatencyoptimization
How AI Agents Handle Rate Limits Across Multiple APIs
When your AI agent calls five different APIs, each with its own rate limits, things get complicated fast. Here's how to handle rate limiting gracefully without failed tool calls piling up.
March 17, 2026
ai-agentsrate-limitsapisreliability
How Skillful.sh Tracks Ecosystem Health Over Time
Ecosystem health isn't just a snapshot. Tracking growth rates, security trends, maintenance patterns, and community activity over time reveals whether things are getting better or worse.
March 17, 2026
skillfulanalyticsecosystemplatform
Supply Chain Risks in the AI Tool Ecosystem
AI tools depend on libraries, APIs, and data sources that create supply chain dependencies. Understanding these dependencies helps you manage risk without slowing down adoption.
March 17, 2026
securitysupply-chaindependenciesrisk
How AI Agents Learn from Failed Tool Calls
A failed tool call isn't just an error. It's information. The best agents use failures to adjust their approach, try alternatives, and avoid repeating the same mistakes. Here's how that works.
March 17, 2026
ai-agentserror-handlinglearningreliability
The Role of Pulse Tracking in Ecosystem Monitoring
Tracking the pulse of the AI tool ecosystem reveals trends, shifts, and opportunities that point-in-time snapshots miss. Continuous monitoring provides context that makes individual data points meaningful.
March 17, 2026
pulsemonitoringecosystemanalytics
How to Contribute to the Open-Source AI Tool Ecosystem
Contributing to the AI tool ecosystem is more accessible than most people think. You do not need to build a complete MCP server from scratch. There are many ways to contribute that match different skill levels.
March 17, 2026
open-sourcecommunitycontributingdevelopment
Why MCP Server Quality Varies So Much (And How to Evaluate)
Not all MCP servers are created equal. Quality ranges from production-ready tools to weekend experiments that never got finished. Here's how to tell the difference before you invest time in a setup.
March 16, 2026
mcpqualityevaluationbest-practices
Why MCP Server Documentation Should Include Failure Cases
Most MCP server docs tell you what the server can do. Very few tell you what it can't do, what it struggles with, or when it'll break. The missing failure documentation costs users hours.
March 16, 2026
mcpdocumentationdeveloper-experiencebest-practices
The Future of AI Tool Interoperability Standards
MCP was a start, but the challenge of making AI tools work seamlessly together is far from solved. Here's where interoperability standards are heading and why it matters for everyone building on these tools.
March 16, 2026
standardsinteroperabilitymcpfuture
What AI Ecosystem Growth Rates Tell Us About Adoption
The growth curves of different AI tool categories reveal which capabilities developers are actually adopting versus which are getting attention without traction.
March 16, 2026
ecosystemgrowthadoptionanalytics
Why MCP Server Discovery Is Harder Than It Looks
Finding the right MCP server for your needs seems straightforward until you try it. The discovery problem reveals deeper challenges about quality signals, trust, and information overload.
March 16, 2026
mcpdiscoveryecosystemdeveloper-experience
How AI Agents Decide When to Ask for Human Help
The best agents know their limits. Figuring out when to escalate versus when to keep trying is one of the hardest design problems in agent architecture.
March 16, 2026
ai-agentsescalationhuman-in-the-loopdesign-patterns
How the MCP Ecosystem Compares to Browser Extensions
The MCP server ecosystem shares structural similarities with browser extensions: a platform, a protocol, a marketplace, and an active developer community. The parallels offer useful lessons.
March 16, 2026
mcpecosystembrowser-extensionscomparison
How MCP Servers Differ from Traditional APIs
MCP servers look like APIs on the surface, but the interaction model is fundamentally different. Understanding the distinction helps you decide when each approach makes sense.
March 15, 2026
mcpapiarchitecturecomparison
How Skillful.sh Tracks the MCP Server Ecosystem in Real Time
The MCP ecosystem changes fast. New servers launch daily, existing ones get updated, and some go dormant. Here's how Skillful.sh keeps up with all of it so you don't have to.
March 15, 2026
skillfulplatformmcpecosystem
The Future of AI Tool Discovery and Curation
As the ecosystem grows past hundreds of thousands of tools, discovery and curation will evolve. The approaches that work now will need to scale, adapt, and become more intelligent.
March 15, 2026
discoveryfuturecurationecosystem
Building Your First AI Skill for Claude
AI skills for Claude are reusable prompt-tool combinations that extend what the assistant can do. Here is a practical walkthrough of building one from scratch.
March 14, 2026
ai-skillsclaudetutorialdevelopment
The Growth of Open Source AI Tools: 2026 Ecosystem Report
Open source AI tooling has exploded in 2026. More servers, more skills, more frameworks, and more contributors than ever. Here's what the numbers actually look like.
March 14, 2026
open-sourceecosystemtrends2026
How AI Agents Handle Database Migrations Safely
Database migrations are high-stakes operations where mistakes can mean data loss. AI agents can help plan and execute migrations more safely, but they need proper guardrails.
March 14, 2026
ai-agentsdatabasemigrationsdevops
The Complete Guide to AI Agent Permissions and Access Control
AI agents that can read your database, call your APIs, and send messages on your behalf need carefully scoped permissions. Here's how to think about access control for agents.
March 14, 2026
ai-agentssecuritypermissionsguide
How AI Agents Manage Multi-Environment Deployments
Dev, staging, production, and maybe a few feature environments too. AI agents can track what's deployed where, promote builds between environments, and catch configuration drift.
March 13, 2026
ai-agentsdeploymentsdevopsenvironments
How Skillful.sh Helps Teams Find and Vet AI Tools
Finding AI tools is one challenge. Vetting them as a team with shared criteria is another. Here's how Skillful.sh supports both halves of that process.
March 13, 2026
skillfulplatformteamsevaluation
The Role of GitHub Stars in Evaluating AI Projects
GitHub stars are the most visible metric for open-source projects, but they are also one of the most misunderstood. What they actually tell you about an AI tool is more nuanced than it appears.
March 13, 2026
evaluationgithubmetricsopen-source
How AI Agents Handle Secret Management Across Environments
Secrets are everywhere in deployment pipelines: API keys, database passwords, certificates. AI agents can manage them more safely than manual processes, but the setup matters.
March 13, 2026
ai-agentssecuritysecretsdevops
What the Model Context Protocol Actually Does
The Model Context Protocol gives AI models a standardized way to interact with external tools. Here is a clear look at what MCP is, how it works, and why the industry converged on it.
March 13, 2026
mcpprotocolai-toolsexplainer
The Role of AI Tools in Autonomous Coding Workflows
AI coding assistants are evolving from autocomplete into autonomous coding workflows that can write, test, and iterate on code with minimal human intervention. This changes what developer time is spent on.
March 13, 2026
codingautomationdeveloper-workflowai-agents
Understanding the AI Tool Security Scoring Methodology
Security scores are only useful if you understand what they measure and how they weight different factors. A transparent look at how security scoring works and what it captures.
March 13, 2026
securityscoringmethodologyevaluation
How AI Agents Handle Partial Failures Gracefully
A task that's 80% complete when something breaks is still valuable. Agents that handle partial failures return useful results instead of nothing.
March 12, 2026
ai-agentserror-handlingresiliencedesign-patterns
How AI Agents Learn From Failed Task Attempts
A failed task attempt isn't just a failure. It's training data. Agents that capture why things went wrong and adjust their approach get better over time.
March 12, 2026
ai-agentslearningfailure-handlingimprovement
How Skillful.sh Aggregates Data from 50+ Directories
Aggregating AI tool data from over fifty different directories involves crawling, normalization, deduplication, and enrichment. A look at how the process works and why it matters.
March 12, 2026
skillfulaggregationplatformdata
Getting Started with MCP Servers in 2026
A practical guide for developers who want to start using MCP servers with their AI assistant. Covers setup, choosing your first servers, and common patterns.
March 12, 2026
mcpgetting-startedtutorialpractical-guide
Why Open Source MCP Servers Dominate the Ecosystem
The overwhelming majority of MCP servers are open source. This is not accidental. The protocol's design and the ecosystem's dynamics strongly favor open development models.
March 12, 2026
mcpopen-sourceecosystemcommunity
How the AI Tool Ecosystem Grew So Fast
Going from a few hundred to over a hundred thousand AI tools in under two years is unusual even by technology standards. Several factors converged to make this growth possible.
March 12, 2026
ecosystemgrowthanalysistrends
The Role of Guardrails in Production AI Agents
Guardrails aren't about limiting what agents can do. They're about making sure agents do what you actually want, even when things get weird.
March 12, 2026
ai-agentsguardrailssafetyproduction
Why We Built a Comparison Engine for AI Tools
Comparing AI tools using scattered data from multiple sources is tedious. A dedicated comparison engine that aggregates signals and enables side-by-side evaluation solves a real workflow problem.
March 12, 2026
comparisonplatformskillfulproduct