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twig

twig

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github.com/Kind-lingDeveloper Tools0Updated 30w ago

About twig

Twig is a CLI and hosted API for evaluating and improving descriptions for MCP servers, A2A Agent Cards, and other tool catalogs. It scores intent match, specificity, and differentiation, and offers generated rewrites plus paid monitoring and competitive intelligence.

Key features

  1. 01Score descriptions across three dimensions
  2. 02Generate three optimized description variants
  3. 03Monitor weekly scores and positioning
  4. 04Compare category rankings and intent trends
  5. 05Measure changes against x402 revenue

Use cases

  1. 01Review an MCP server’s tool descriptions before release
  2. 02Refresh descriptions when agent query trends shift
  3. 03Track competitive position within a service category
  4. 04Compare optimized descriptions with x402 revenue changes

MCP support

Twig analyzes MCP servers by reading JSON-RPC tools/list, alongside other supported sources. Its hosted API also accepts MCP server URLs for analysis; it is an analysis service, not a server exposing business tools.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
"mcpServers": {
"kind-ling-twig": {
"command": "npx",
"args": [
"-y",
"@kind-ling/twig"
]
}
}
}
Run commandnpx -y @kind-ling/twig

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
npm@kind-ling/twig0.4.2

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

Twig

Agents choose you or they don't. Twig makes them choose you.

CI npm License: MIT

Part of Kindling — agent SEO for the agent economy.


Free audit. Paid monitoring. Premium intelligence.

TierFeaturesPrice
Freetwig analyze — score your descriptions. twig optimize — get better variants.Always free
MonitorWeekly re-scoring, competitive position, intent trend alerts$0.50–2.00/report via x402
IntelligenceCategory benchmarks, intent corpus, competitive index$0.50–1.00/query via x402
Enterprise15% of measured x402 revenue increasePerformance-based, zero if no improvement

The Problem

Every MCP tool has a description. That description is what an LLM reads when deciding whether to call your service. It's the only signal between "selected" and "skipped".

Most descriptions are garbage.

ToolCurrent DescriptionTwig Score
exa/answer"AI-powered answer"F (28/100)
wordspace/invoke"Run wordspace AI agent loop"F (31/100)
exa/find-similar"Find similar pages"F (33/100)
jupiter/portfolio"Wallet portfolio positions"D (42/100)

These tools are useful. Their descriptions are not. And getting indexed in x402-discovery or MCPay doesn't help if agents skip you because they can't tell what you do.

Getting indexed is table stakes. Twig helps you get chosen.


Quick Start

npm install -g @kind-ling/twig
# Free — score your descriptions
twig analyze https://myservice.com/.well-known/agent.json

# Free — get optimized variants
twig optimize https://myservice.com/.well-known/agent.json

# Paid — set up weekly monitoring
twig monitor https://myservice.com/.well-known/agent.json

# Paid — see how you rank in your category
twig competitive crypto-defi

# Paid — see what agents are actually searching for
twig intents crypto-defi

Live API

Twig is available as a hosted API at https://twig.kind-ling.com

# Health check
curl https://twig.kind-ling.com/health

# Analyze any MCP server
curl -X POST https://twig.kind-ling.com/analyze \
  -H "Content-Type: application/json" \
  -d '{"url": "https://your-mcp-server.com/mcp"}'

# Optimize descriptions
curl -X POST https://twig.kind-ling.com/optimize \
  -H "Content-Type: application/json" \
  -d '{"url": "https://your-mcp-server.com/mcp", "category": "crypto-defi"}'

Response format:

{
  "ok": true,
  "url": "https://your-mcp-server.com/mcp",
  "output": "🔍 Analyzing...\n  Score: 72/100 (C)\n  ..."
}

No API key required for public endpoints. Rate limits apply.


Scoring Model

Three dimensions, 0–100 each:

DimensionWeightWhat it measures
Intent Match40%Similarity to top agent queries for your category
Specificity35%Output format, parameters, constraints, examples present?
Differentiation25%Do you stand out from similar services in your category?

Grades: A (85+) · B (70+) · C (55+) · D (40+) · F (<40)

Score below 40 = agents are skipping you.


What Twig Monitors

Your descriptions — re-scored weekly against a live intent corpus. Score changes alert you when your positioning drifts.

Your competitive position — how you rank vs. similar services in your category. Know when a competitor improves and overtakes you.

Intent trends — what agents are searching for this week vs. last. The queries change. Your descriptions need to keep up.

Volume correlation — description changes mapped to x402 volume changes. Close the loop between optimization and revenue.


Optimization Variants

Twig generates 3 variants per tool:

Functional — Action verb + what + return type:

"Answers questions with real-time web sources. Returns a cited response with source URLs, confidence score, and supporting quotes. Best for research and fact-checking."

Structured — Input → output with constraints:

"Web Q&A. Input: question string. Returns: answer (string), sources (URL array), confidence (0-1). Supports: factual queries, current events, research synthesis."

Contextual — Use case → action → output (highest LLM selectability):

"Use when you need a factual answer with sourced citations. Queries the web in real-time and synthesizes a response with URLs and supporting quotes. Best for research, fact-checking, and current events."


Before / After

exa/answer

Before: AI-powered answer ← F (28/100)

After (Contextual): Use when you need a factual answer with sourced citations. Queries the web in real-time and synthesizes a response with source URLs and supporting quotes. Best for research, fact-checking, and current events. ← B (72/100)

wordspace/invoke

Before: Run wordspace AI agent loop ← F (31/100)

After (Structured): Executes a Wordspace multi-step agent workflow. Input: task description and optional context. Returns: completed output, intermediate steps, tool calls made. Best for complex tasks requiring multiple tool invocations. $2/call. ← B (74/100)


Revenue Model

Standard (Monitoring)

Weekly reports are gated via x402 on Base. Set up once, runs automatically.

twig monitor 0xYourWallet --url https://myservice.com/.well-known/agent.json
Enterprise (Performance-based)

For services with existing x402 volume:

  1. twig measure 0xWallet --baseline — record 7-day baseline
  2. Deploy Twig-optimized descriptions
  3. twig measure 0xWallet --compare — measure delta (14–30 days later)
  4. Pay 15% of the revenue increase in USDC on Base
  5. Revenue flat or down? Pay nothing.

Everything measured via on-chain USDC transfer logs. No self-reporting.


Supported Sources

  • A2A Agent Cards (/.well-known/agent.json)
  • MCP servers (JSON-RPC tools/list)
  • OpenAPI specs
  • Local JSON schema files
  • x402-discovery catalog (for competitive analysis)

CLI Reference

twig analyze <url>              Score descriptions (0-100, free)
twig optimize <url>             Generate 3 optimized variants (free)
twig monitor <url>              Weekly monitoring (paid via x402)
twig competitive <category>     Category ranking (paid via x402)
twig intents <category>         Top agent queries (paid via x402)
twig measure <wallet>           Measure x402 revenue
twig measure <wallet> --baseline   Save baseline
twig measure <wallet> --compare    Compare to baseline
twig report <wallet>            Revenue report with fee calculation

Categories: crypto-defi, data-feeds, research, computation, media, communication, general

Twig v0.1.0 · Kind-ling · Permanent Upper Class · MIT

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.