Provides translation services through the Hebline intelligent routing engine, allowing agents to perform text translation with high accuracy.
Enables geocoding capabilities, allowing agents to convert addresses into geographic coordinates via the Google Maps API.
Integrates translation capabilities into the Hebline routing system, providing an additional provider for automated language processing tasks.
Provides free geocoding services through Nominatim, allowing agents to resolve location queries into spatial data without requiring an API key.
Your agents overpay for every API call. We fix that.
Hebline routes every API call — including LLM calls — to the best service at the right price. Free when it's enough. Paid when it matters. It knows the difference.
Every other router earns a margin on your paid calls. Routing you to free alternatives kills their revenue. No margin on your API calls. Ever.
Your agents are bleeding money. One task triggers 5–10 paid API calls across different providers. No transparency, no cost control. Hebline fixes that:
Route free first — Most calls don't need the best model. Hebline learns precisely when it matters — and keeps learning as the market changes.
No margin. Honest routing. — We don't earn when you pay more. So we're the only router built to actually save you money.
Provider Abstraction — Your agent says what it needs ("geocode this address"), not which service to use. Swap providers without changing agent code.
Cost Transparency — Every call is logged with service used, latency, and cost. Know exactly what your agents spend.
Learns from usage — Hebbian learning strengthens what works, weakens what doesn't. Your broker gets smarter every day.
BYOK (Bring Your Own Key) — Paid services use your API keys via environment variables. No key? The service is automatically excluded from routing.
GDPR compliant — Only anonymized metadata logged. No API call content stored. Self-hosted option for zero data leaving your network.
Open Source — Core MCP server is MIT licensed. Community-driven adapter system.
Related MCP server: Clawy MCP Server
Your AI Agent ←→ Hebline MCP Server ←→ Best API (Nominatim, DeepL, Google Maps, ...)
│
Smart Routing
Cost Logging
Provider ScoringYour agent connects to Hebline as an MCP server. Instead of calling APIs directly, it uses Hebline's tools — execute, compare, or categories. Hebline scores all available services, picks the best one, makes the call, and returns the result with full metadata.
Tool | Description |
| Route to the best service and make the API call. Returns result + metadata (service, cost, latency). |
| Show all available services for a capability with scores. See what's available before committing. |
| List all supported capabilities and their services. |
Add to claude_desktop_config.json:
{
"mcpServers": {
"hebline": {
"command": "npx",
"args": ["-y", "-p", "@hebline.ai/mcp-server", "hebline-mcp"]
}
}
}Add to .mcp.json:
{
"mcpServers": {
"hebline": {
"command": "hebline-mcp"
}
}
}Add to .cursor/mcp.json:
{
"mcpServers": {
"hebline": {
"command": "npx",
"args": ["-y", "-p", "@hebline.ai/mcp-server", "hebline-mcp"]
}
}
}Add to ~/.codeium/windsurf/mcp_config.json:
{
"mcpServers": {
"hebline": {
"command": "npx",
"args": ["-y", "-p", "@hebline.ai/mcp-server", "hebline-mcp"]
}
}
}Add to .vscode/mcp.json:
{
"servers": {
"hebline": {
"type": "stdio",
"command": "npx",
"args": ["-y", "-p", "@hebline.ai/mcp-server", "hebline-mcp"]
}
}
}npm install -g @hebline.ai/mcp-serverSet environment variables for any paid providers you want to use:
GOOGLE_MAPS_API_KEY=your-key-here
DEEPL_API_KEY=your-key-here
LIBRETRANSLATE_API_KEY=your-key-hereNo keys? No problem — Hebline routes to free alternatives automatically.
Category | Free | Paid (BYOK) |
LLMs | Groq (Llama 3.3 70B), Google Gemini Flash | OpenAI GPT-4o-mini ( |
Geocoding | Nominatim (OpenStreetMap) | Google Maps ( |
Translation | MyMemory | DeepL ( |
Web Scraping | Fetch Scraper | Firecrawl ( |
Currency | ExchangeRate-API | Fixer.io ( |
OCR | OCR.space | Google Vision ( |
Weather | Open-Meteo | OpenWeatherMap ( |
Web Search | DuckDuckGo | Brave Search ( |
News | HackerNews | NewsAPI.org ( |
9 categories, 20 services. Free services work instantly — no API key needed. LLMs route through the Hebline proxy when no local key is set (50 free calls/day).
An agent asks: "Geocode the Brandenburg Gate in Berlin"
Hebline receives:
{
"capability": "geocoding",
"input": { "query": "Brandenburger Tor, Berlin" },
"constraint": "free"
}Hebline responds:
{
"success": true,
"data": {
"lat": 52.5163,
"lon": 13.3777,
"displayName": "Brandenburger Tor, Pariser Platz, Berlin, 10117, Deutschland"
},
"meta": {
"service": "Nominatim (OpenStreetMap)",
"costUsd": 0,
"latencyMs": 258,
"score": 0.702,
"free": true
}
}The agent got coordinates, knows it was free, and Hebline logged the call for future analysis.
mcp-server/
├── src/
│ ├── index.ts # MCP server entry point (stdio transport)
│ ├── types.ts # Shared TypeScript types
│ ├── registry.ts # Service definitions (capabilities, costs, scores)
│ ├── router.ts # Weighted scoring engine (Hopfield-ready)
│ ├── logger.ts # Append-only JSONL call log (~/.hebline/calls.jsonl)
│ ├── adapters/ # One adapter per service
│ │ ├── nominatim.ts # Free geocoding
│ │ ├── google-maps.ts # Paid geocoding (BYOK)
│ │ ├── mymemory.ts # Free translation
│ │ ├── libretranslate.ts # Paid translation (BYOK)
│ │ └── deepl.ts # Paid translation (BYOK)
│ └── tools/ # MCP tool definitions
│ ├── execute.ts # Route + call best service
│ ├── compare.ts # Score all services
│ └── categories.ts # List capabilitiesEvery API call is logged to ~/.hebline/calls.jsonl:
{"timestamp":"2026-03-29T09:36:37Z","capability":"geocoding","serviceId":"nominatim","latencyMs":212,"success":true,"costUsd":0}No content is logged — only metadata. This data will power Hebbian Learning in future versions.
Core MCP server with stdio transport
Weighted scoring router
Geocoding adapters (Nominatim, Google Maps)
Translation adapters (MyMemory, LibreTranslate, DeepL)
BYOK key management
Append-only call logging
CI/CD with GitHub Actions
Hebbian Learning — router learns from call history
Hopfield network scoring (replaces weighted scoring)
More categories (web scraping, currency, OCR, email)
Community adapter system
SSE transport for remote deployments
Web dashboard for cost analytics
Budget alerts and spending limits
Multi-agent cost attribution
Contributions are welcome! Adding a new adapter is straightforward — implement the ServiceAdapter interface and register it.
git clone https://github.com/hebline/mcp-server.git
cd mcp-server
npm install
npm run build
npm testBuilt by Hebline — Route free first. Only pay when necessary.
List all capabilities Hebline supports and which services are available for each.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It implies a read-only operation ('List'), but doesn't specify whether it requires authentication, has rate limits, returns structured data, or involves pagination. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's function without fluff. It's front-loaded with the core action ('List') and resource, making it easy to parse. Every word contributes to understanding, achieving ideal conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, no output schema), the description is adequate but not fully complete. It explains what the tool does but lacks details on return format, error handling, or behavioral constraints. With no annotations to fill gaps, it meets minimum viability but leaves room for improvement in guiding agent usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters, and schema description coverage is 100%, so there are no parameters to document. The description doesn't need to compensate for missing param info. A baseline of 4 is appropriate as it avoids redundancy and focuses on the tool's purpose without unnecessary parameter details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'List all capabilities Hebline supports and which services are available for each.' It uses specific verbs ('List') and identifies the resource ('capabilities Hebline supports'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools (compare, execute), which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'compare' or 'execute'. It doesn't mention prerequisites, timing, or contextual triggers. While the purpose is clear, the lack of comparative or conditional guidance limits its utility for an agent deciding between tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Compare all available services for a capability. Shows scores, costs, and Hebline's recommendation.
| Name | Required | Description | Default |
|---|---|---|---|
| capability | Yes | Capability to compare services for, e.g. 'geocoding', 'translation' | |
| constraint | No | Cost constraint filter | any |
| region | No | Region filter, e.g. 'eu', 'us' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions outputs (scores, costs, recommendation) but lacks details on behavioral traits such as data freshness, rate limits, authentication needs, or error handling. This is inadequate for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the purpose. It could be slightly more structured by separating key points, but it avoids redundancy and wastes no words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (comparison with multiple outputs), lack of annotations, and no output schema, the description is incomplete. It hints at outputs but doesn't detail format or behavior, leaving gaps for the agent to handle mutations or errors.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all three parameters. The description adds no additional meaning beyond what the schema provides, such as examples or constraints not in the schema, meeting the baseline for high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'compare' and the resource 'all available services for a capability', with specific outputs mentioned ('scores, costs, and Hebline's recommendation'). It distinguishes from sibling tools 'categories' and 'execute' by focusing on comparison rather than listing or execution.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like 'categories' or 'execute'. The description implies usage for comparing services but doesn't specify scenarios, prerequisites, or exclusions, leaving the agent to infer context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Route to the best service and execute the API call. Returns result with metadata (service used, cost, latency).
| Name | Required | Description | Default |
|---|---|---|---|
| capability | Yes | What you need, e.g. 'geocoding', 'translation' | |
| input | Yes | Service-specific input (e.g. { query: 'Berlin' } for geocoding, { text: 'Hello', target: 'de' } for translation) | |
| constraint | No | Cost constraint: 'free' = only free services, 'cheapest' = prefer lowest cost, 'any' = best overall | any |
| region | No | Preferred region, e.g. 'eu', 'us'. Omit for global. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions that the tool returns 'result with metadata (service used, cost, latency)', which adds some behavioral context. However, it lacks details on permissions, rate limits, error handling, or side effects, which are important for a tool that executes API calls and routes services.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise and front-loaded: it states the core purpose in the first clause and adds return details in parentheses. Every sentence earns its place with no wasted words, making it efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (executes API calls with routing) and lack of annotations/output schema, the description is moderately complete. It covers the purpose and return metadata, but gaps remain in behavioral details and usage guidelines. It's adequate but has clear room for improvement in context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters. The description adds no additional meaning beyond what the schema provides (e.g., it doesn't explain parameter interactions or usage examples). Baseline is 3 when schema coverage is high and description doesn't compensate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Route to the best service and execute the API call.' It specifies the action (route and execute) and resource (API call), but doesn't distinguish it from sibling tools like 'categories' or 'compare' which have different purposes. The description is specific but lacks sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools or other contexts, and offers no explicit when/when-not scenarios. Usage is implied (e.g., for API calls with routing), but no clear alternatives or exclusions are stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Each tool has a clearly distinct purpose with no overlap: 'categories' lists capabilities and services, 'compare' analyzes service options with recommendations, and 'execute' routes and runs API calls. The separation between listing, comparing, and executing is unambiguous.
All tool names follow a consistent verb-only pattern in lowercase, with no mixing of conventions. The naming is straightforward and predictable across the set.
Three tools is well-scoped for the server's purpose of managing and executing API services through Hebline. Each tool earns its place by covering a distinct phase: discovery, comparison, and execution.
The tool set provides complete coverage for the domain: it allows agents to discover capabilities, compare service options, and execute calls with metadata. There are no obvious gaps in the workflow from start to finish.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
AI model routing on your own vendor keys: pick the best model per prompt, or route and run it.
AI service marketplace — agents discover, call, and pay for API services automatically.
Stripe-native marketplace where AI agents discover and pay per call for API services.
AI routing, memory, guardrails, and governance. Routes across Claude, GPT, Gemini.
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