kimmo-agent-friendly-score
Score developer tools and SaaS products for AI agent compatibility. Use when evaluating how well a devtool works with AI coding assistants, or when optimizing a product for the agent era.
Best use case
kimmo-agent-friendly-score is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Score developer tools and SaaS products for AI agent compatibility. Use when evaluating how well a devtool works with AI coding assistants, or when optimizing a product for the agent era.
Teams using kimmo-agent-friendly-score should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/kimmo-agent-friendly-score/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How kimmo-agent-friendly-score Compares
| Feature / Agent | kimmo-agent-friendly-score | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Score developer tools and SaaS products for AI agent compatibility. Use when evaluating how well a devtool works with AI coding assistants, or when optimizing a product for the agent era.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Agent-Friendly Score - DevTool Evaluation
Evaluate developer tools and SaaS products for compatibility with AI coding assistants (Cursor, Claude, GitHub Copilot).
## When to Use
- User asks "How agent-friendly is [tool]?"
- User wants to evaluate a devtool for AI compatibility
- User is building a devtool and wants to optimize for AI assistants
- User is comparing tools and AI compatibility matters
## The Agent Era Context
85% of developers now use AI tools regularly. When a developer asks Cursor to "add email functionality," the AI picks the service, writes the integration, and runs the install command.
**The New Funnel**:
- Traditional: Marketing → Landing → Docs → Trial → Conversion
- Agent: Problem → AI suggestion → `npm install` → Subscription
Tools that AI can easily work with get recommended. Tools it can't work with become invisible.
## Scoring Framework
### Category 1: SDK & API Design (30 points)
| Criterion | Points | How to Check |
| --------------------------------- | ------ | ----------------------------------- |
| SDK available for major languages | 10 | Check docs for JS, Python, Go, etc. |
| Consistent, predictable API | 10 | Review API reference for patterns |
| Complete TypeScript definitions | 5 | Check npm package for .d.ts files |
| Clear error messages | 5 | Test error responses |
**Scoring guide**:
- 25-30: Excellent - AI can generate correct code first try
- 15-24: Good - AI mostly succeeds, occasional fixes needed
- 0-14: Poor - AI struggles to generate working code
### Category 2: Documentation Quality (25 points)
| Criterion | Points | How to Check |
| ------------------------------ | ------ | ------------------------------ |
| Docs lead with working code | 10 | First thing on quickstart page |
| Copy-paste examples work | 5 | Try the first 3 examples |
| Parseable structure (H1→H2→H3) | 5 | View page source/outline |
| No login walls on docs | 5 | Access docs without account |
**Scoring guide**:
- 20-25: AI can extract and apply correctly
- 10-19: AI needs some interpretation
- 0-9: AI will likely hallucinate or fail
### Category 3: Training Data Presence (20 points)
| Criterion | Points | How to Check |
| ---------------------------- | ------ | ------------------------------ |
| GitHub repos using this tool | 8 | Search GitHub for imports |
| Stack Overflow presence | 6 | Search SO for [tool] questions |
| Tutorial/blog coverage | 6 | Search "[tool] tutorial" |
**Scoring guide**:
- 15-20: Strong training data signal
- 8-14: Moderate presence
- 0-7: AI may not know this tool well
### Category 4: MCP Integration (15 points)
| Criterion | Points | How to Check |
| -------------------------- | ------ | ------------------------------- |
| Official MCP server exists | 10 | Check mcp.so, official docs |
| MCP server is maintained | 3 | Recent commits, version updates |
| MCP server is discoverable | 2 | Listed on MCP.so or npm |
**Scoring guide**:
- 12-15: Full agent workflow integration
- 5-11: Partial integration
- 0-4: Not in agent workflow
### Category 5: Time to Working (10 points)
| Criterion | Points | How to Check |
| ------------------------------- | ------ | ---------------------------------- |
| Install to "hello world" <5 min | 5 | Time yourself following quickstart |
| No complex onboarding | 3 | Can start without account? |
| Sensible defaults | 2 | Works without config? |
**Scoring guide**:
- 8-10: Instant productivity
- 4-7: Reasonable setup
- 0-3: Significant friction
## Evaluation Workflow
### Step 1: Identify the Tool
Get from user:
- Tool name and URL
- Category (email, auth, database, etc.)
- Main competitor to compare against
### Step 2: SDK Evaluation
Check official SDK:
```bash
# Check npm for TypeScript types
npm info [package] types
# Check for SDK in multiple languages
# Visit: github.com/[org] and look for SDK repos
```
Test API consistency:
- Are endpoints predictable? (e.g., `/users`, `/users/:id`)
- Are responses consistent?
- Are errors structured?
### Step 3: Documentation Audit
Visit docs and check:
- [ ] First code example is within scroll view
- [ ] Examples include all necessary imports
- [ ] Examples actually work when copied
- [ ] Structure uses semantic headings
- [ ] No authentication required to view
### Step 4: Training Data Check
Search GitHub:
```
"import { X } from '[package]'" language:JavaScript
"from [package] import" language:Python
```
Search Stack Overflow:
```
[tool] is:question
```
### Step 5: MCP Check
Search for MCP server:
- https://mcp.so - search for tool name
- Official docs - search for "MCP" or "Model Context Protocol"
- GitHub - search "[tool] mcp server"
### Step 6: Time Test
Follow quickstart:
1. Start timer
2. Follow official quickstart exactly
3. Stop when first API call succeeds
4. Record time and friction points
## Output Template
```markdown
# Agent-Friendly Score: [Tool Name]
## Overall Score: [X]/100
| Category | Score | Max |
| ---------------- | ----- | --- |
| SDK & API Design | X | 30 |
| Documentation | X | 25 |
| Training Data | X | 20 |
| MCP Integration | X | 15 |
| Time to Working | X | 10 |
## Grade: [A/B/C/D/F]
- A (85-100): AI will recommend and integrate correctly
- B (70-84): AI will usually succeed
- C (55-69): AI needs help, may hallucinate
- D (40-54): Significant AI compatibility issues
- F (<40): AI will struggle or avoid
## Breakdown
### SDK & API Design ([X]/30)
**Strengths:**
- [What works well]
**Gaps:**
- [What's missing]
### Documentation ([X]/25)
**Strengths:**
- [What works well]
**Gaps:**
- [What's missing]
### Training Data Presence ([X]/20)
- GitHub repos found: [X]
- Stack Overflow questions: [X]
- Tutorial coverage: [High/Medium/Low]
### MCP Integration ([X]/15)
- MCP server: [Official/Community/None]
- Status: [Active/Stale/N/A]
### Time to Working ([X]/10)
- Quickstart time: [X minutes]
- Friction points: [list]
## Recommendations
### Quick Wins (High Impact, Low Effort)
1. [Recommendation]
2. [Recommendation]
### Strategic Improvements
1. [Recommendation]
2. [Recommendation]
## Competitor Comparison
| Metric | [Tool] | [Competitor] |
| --------------- | ------ | ------------ |
| Agent Score | X/100 | Y/100 |
| MCP Server | Yes/No | Yes/No |
| Time to Working | X min | Y min |
## Verdict
[One paragraph summary of whether this tool is positioned for the agent era]
```
## Benchmarks by Category
### Email APIs
| Tool | Typical Score | Notes |
| -------- | ------------- | --------------------------- |
| Resend | 85-90 | MCP, clean SDK, great docs |
| Postmark | 80-85 | MCP, enterprise-ready |
| SendGrid | 60-70 | No official MCP, legacy API |
### Authentication
| Tool | Typical Score | Notes |
| ------------- | ------------- | --------------------- |
| Clerk | 85-90 | MCP, great DX |
| Auth0 | 75-80 | MCP, but complex |
| Firebase Auth | 80-85 | MCP, Google ecosystem |
### Databases
| Tool | Typical Score | Notes |
| ----------- | ------------- | --------------------- |
| Supabase | 85-90 | MCP, hosted, great DX |
| Neon | 85-90 | MCP, serverless |
| PlanetScale | 75-80 | MCP (read-only) |
Use these as calibration when scoring.
## Key Insight
The best technical product doesn't always win anymore. The most **AI-accessible** product wins. When an AI assistant can:
1. Understand your docs
2. Generate working code
3. Integrate via MCP
...you're in the conversation. When it can't, you're invisible to the fastest-growing developer segment.
---
By Kimmo Ihanus | [kimmoihanus.com](https://kimmoihanus.com)Related Skills
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