Enables on-chain wallet functionality, fee estimation, and chain synchronization for Bitcoin transactions
Supports configuration through environment variables and .env files for network settings and logging levels
Provides integration with GitHub for issues, discussions, and project contributions
Offers iOS-specific integration patterns for Lightning wallets, including keychain security, background processing, push notifications, and biometric authentication
Provides comprehensive Lightning Network capabilities including invoice generation, payment processing, channel management, and routing
Uses npm for dependency management and running build/test commands
Provides production-ready Swift code examples and bindings for implementing Lightning functionality in iOS applications
Offers TypeScript interfaces for Lightning operations and iOS integration tools
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@LDK MCP Server generate a test Lightning invoice for 5000 sats with a coffee payment description"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Real-time Lightning Development Kit (LDK) expertise for accelerating iOS Lightning wallet development. This MCP server provides instant access to LDK APIs, Swift patterns with proper Bindings namespace usage, comprehensive event handling, NetworkGraph with RapidGossipSync, and multiple chain synchronization methods.
Invoice Generation - Create real Lightning invoices with proper encoding
Payment Processing - Test payment flows with simulated Lightning operations
Channel Management - Open, monitor, and close Lightning channels
Balance Tracking - Monitor channel liquidity and wallet balances
Fee Estimation - Calculate routing fees and on-chain costs
Keychain Security - iOS Keychain patterns for secure key storage
Background Processing - Lightning sync in iOS background tasks
Push Notifications - Payment notification implementation
Biometric Auth - Touch/Face ID integration for Lightning operations
Swift Code Examples - Production-ready LDK Swift implementations
Project Structure - Recommended iOS Lightning wallet architecture
Security Patterns - Best practices for key management and crypto
Testing Scenarios - Complete test scenarios for Lightning operations
Related MCP server: Apple RAG MCP
Clone the repository:
git clone https://github.com/StevenGeller/ldk-mcp.git
cd ldk-mcpInstall dependencies:
npm installBuild the project:
npm run buildConfigure MCP Client:
Add to your MCP client configuration file:
Configuration varies by MCP client. For example configurations, see the documentation.
{
"mcpServers": {
"ldk-mcp": {
"command": "node",
"args": ["/path/to/ldk-mcp/dist/index.js"],
"env": {
"NETWORK": "testnet"
}
}
}
}Restart Claude Desktop
ldk_generate_invoiceGenerate Lightning invoices for testing payment flows.
await ldk_generate_invoice({
amountSats: 10000,
description: "Test payment",
expirySeconds: 3600
})ldk_pay_invoiceSimulate Lightning payments for development.
await ldk_pay_invoice({
invoice: "lnbc10000n1...",
maxFeeSats: 50
})ldk_create_channelOpen Lightning channels with configuration options.
await ldk_create_channel({
remotePubkey: "02abc...",
capacitySats: 1000000,
pushSats: 100000,
isPublic: false
})ldk_channel_statusMonitor channel states and balances.
await ldk_channel_status({
includeOffline: true
})ios_keychain_testTest iOS Keychain integration for secure storage.
await ios_keychain_test({
keyType: "seed",
testValue: "test_data"
})ios_background_testImplement Lightning background sync.
await ios_background_test({
taskType: "sync"
})ios_biometric_authIntegrate Touch/Face ID with Lightning operations.
await ios_biometric_auth({
operation: "send_payment",
requireAuth: true
})ldk_get_swift_codeGet production-ready Swift code examples.
await ldk_get_swift_code({
operation: "channel_manager_setup"
})ldk_get_architectureAccess architectural patterns and best practices.
await ldk_get_architecture({
topic: "security_architecture"
})ldk_test_scenarioRun complete Lightning development scenarios.
await ldk_test_scenario({
scenario: "multi_hop_payment"
})// 1. Use MCP to generate a test invoice
const invoice = await ldk_generate_invoice({
amountSats: 5000,
description: "Coffee payment"
})
// 2. Get the Swift implementation
const swiftCode = await ldk_get_swift_code({
operation: "payment_handling"
})
// 3. The MCP server provides complete SwiftUI code with:
// - QR code generation
// - Invoice display
// - Payment handling
// - Error management// 1. Get architecture guidance
const architecture = await ldk_get_architecture({
topic: "channel_management"
})
// 2. Create test channel
const channel = await ldk_create_channel({
remotePubkey: "02...",
capacitySats: 500000
})
// 3. Run test scenarios
const test = await ldk_test_scenario({
scenario: "channel_lifecycle"
})# Request initial setup
"Help me set up a new iOS Lightning wallet project"
# MCP provides:
# - Complete project structure
# - LDK initialization code
# - Security setup
# - Basic UI components# Request specific features
"Implement Lightning payment sending with biometric authentication"
# MCP provides:
# - Swift implementation
# - Security best practices
# - UI/UX patterns
# - Test scenarios# Test your implementation
"Run a multi-hop payment test scenario"
# MCP provides:
# - Test setup code
# - Mock Lightning network
# - Validation steps
# - Debugging guidanceNETWORK - Bitcoin network: mainnet, testnet, or regtest (default: testnet)
LOG_LEVEL - Logging verbosity: debug, info, warn, error (default: info)
Create a .env file in the project root:
NETWORK=testnet
LOG_LEVEL=debugRun the test suite:
npm testRun specific test scenarios:
npm test -- --testNamePattern="Lightning payment"Fork the repository
Create your feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request
This project is licensed under the MIT License - see the LICENSE file for details.
Lightning Dev Kit for the amazing Lightning implementation
Bitcoin Dev Kit for on-chain wallet functionality
The Lightning Network community for protocol development
Issues: GitHub Issues
Discussions: GitHub Discussions
Lightning Dev Chat: LDK Discord
Built with ⚡ for the Lightning Network community
Test Lightning background processing and channel monitoring
| Name | Required | Description | Default |
|---|---|---|---|
| taskType | No | Type of background task to test | sync |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'test' but doesn't clarify if this is a read-only diagnostic, a simulation that might affect state, or has side effects like network calls. No details on permissions, rate limits, or output behavior are included, leaving significant gaps.
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 purpose without unnecessary words. It's front-loaded and appropriately sized for a simple tool, though it could be slightly more informative without losing 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 complexity of testing Lightning network operations, no annotations, and no output schema, the description is incomplete. It lacks details on what the test does, expected outcomes, or error handling, making it insufficient for an agent to understand the tool's full context and behavior.
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 input schema has 100% description coverage, with 'taskType' well-documented via enum and description. The description adds no additional parameter semantics beyond what the schema provides, such as explaining the implications of each task type. Baseline 3 is appropriate since the schema handles the heavy lifting.
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 'Test Lightning background processing and channel monitoring' specifies a verb ('Test') and resource ('Lightning background processing and channel monitoring'), but it's vague about what 'test' entails—whether it's validation, simulation, or debugging. It distinguishes from siblings by focusing on Lightning network tasks, but lacks specificity on the exact operation.
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 explicit guidance on when to use this tool versus alternatives is provided. The description implies usage for testing Lightning-related background tasks, but it doesn't specify contexts, prerequisites, or exclusions, such as when to choose this over other iOS or LDK tools for similar functions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Integrate Touch/Face ID with Lightning operations
| Name | Required | Description | Default |
|---|---|---|---|
| operation | No | Operation requiring biometric auth | send_payment |
| requireAuth | No | Whether to require biometric auth |
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 mentions integration but doesn't specify whether this tool triggers biometric prompts, handles authentication results, requires specific permissions, has side effects like modifying data, or what happens on failure. This is inadequate for a tool that likely involves sensitive operations like payments or seed export.
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 gets straight to the point without unnecessary words. It's appropriately sized for a tool with two parameters, though it could be more front-loaded with specific actions to improve clarity.
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 complexity of biometric authentication and Lightning operations, with no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits, error handling, security implications, and integration specifics, making it insufficient for an agent to use this tool effectively in sensitive contexts.
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 input schema has 100% description coverage, clearly documenting the 'operation' enum and 'requireAuth' boolean. The description adds no additional meaning beyond the schema, such as explaining the context of each operation or the implications of biometric requirements. With high schema coverage, the baseline is 3, as the description doesn't compensate but doesn't detract either.
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 'Integrate Touch/Face ID with Lightning operations' states the general purpose of integrating biometric authentication with Lightning Network operations, but it's vague about what specific actions it performs. It doesn't specify whether it initiates authentication, verifies it, or handles the biometric flow, and it doesn't clearly distinguish from sibling tools like ios_keychain_test which might also handle security aspects.
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 prerequisites, context for biometric integration, or compare it to sibling tools like ios_keychain_test or ldk_pay_invoice, leaving the agent with no usage context beyond the tool name and description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Validate private key storage patterns with iOS Keychain
| Name | Required | Description | Default |
|---|---|---|---|
| keyType | Yes | Type of key to test | seed |
| testValue | No | Test value to store (will be encrypted) |
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 mentions that the test value 'will be encrypted', which adds some context about security handling. However, it lacks critical details such as whether this is a read-only test, if it modifies the Keychain, what permissions are required, potential side effects, or how results are returned. For a tool involving iOS Keychain operations, this is a significant gap in transparency.
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 purpose without unnecessary words. It is front-loaded with the core action and context. However, it could be slightly more structured by including brief usage hints, but as-is, it earns a high score for conciseness with minimal waste.
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 complexity of iOS Keychain operations and the lack of annotations and output schema, the description is incomplete. It doesn't cover behavioral aspects like safety, permissions, or result format, which are crucial for an agent to use this tool correctly. While the schema covers parameters well, the overall context for secure storage validation is insufficient, especially compared to siblings that may have more detailed descriptions.
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 both parameters (keyType with enum and testValue). The description adds no additional meaning beyond the schema, such as explaining what 'Validate private key storage patterns' entails in terms of these parameters or providing examples. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.
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 as 'Validate private key storage patterns with iOS Keychain', which specifies the action (validate), target (storage patterns), and technology context (iOS Keychain). It distinguishes itself from siblings like ios_background_test or ios_biometric_auth by focusing on key storage validation rather than background processes or authentication. However, it doesn't explicitly differentiate from all siblings, such as ldk_backup_state which might involve storage, keeping it from 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. It doesn't mention prerequisites, such as needing iOS Keychain access or specific device conditions, nor does it compare to siblings like ios_biometric_auth for related security tasks. Without any usage context or exclusions, the agent must infer when this tool is appropriate based on the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Test payment notification flows and push setup
| Name | Required | Description | Default |
|---|---|---|---|
| notificationType | No | Type of notification to simulate | payment_received |
| amountSats | No | Amount in satoshis (for payment notifications) |
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 testing flows and push setup, but doesn't disclose key behavioral traits such as whether this is a read-only or mutative operation, if it requires specific permissions, what side effects occur (e.g., sends actual notifications), or any rate limits. This leaves significant gaps for an agent to understand the tool's 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 purpose without unnecessary words. It's front-loaded and appropriately sized for a simple tool, though it could be slightly more informative without losing 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 complexity (testing notifications with 2 parameters), no annotations, and no output schema, the description is incomplete. It doesn't cover what the tool returns, error conditions, or behavioral details, making it inadequate for an agent to fully understand and use the tool effectively.
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 the two parameters (notificationType and amountSats). The description adds no additional meaning beyond the schema, such as explaining how these parameters interact or their impact on the test. Baseline 3 is appropriate since the schema handles the heavy lifting.
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 states the tool is for testing payment notification flows and push setup, which gives a general purpose but lacks specificity about what exactly it does (e.g., simulates iOS push notifications). It doesn't clearly distinguish from sibling tools like ios_background_test or ios_biometric_auth, which might also involve iOS testing.
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 implies usage for testing flows and setup, but provides no explicit guidance on when to use this tool versus alternatives (e.g., vs. ios_background_test or ldk_test_scenario). There's no mention of prerequisites, context, or exclusions, leaving the agent with minimal direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Test channel backup and restore flows
| Name | Required | Description | Default |
|---|---|---|---|
| action | Yes | Action to perform | |
| backupData | No | Base64 encoded backup data (required for restore) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden but provides minimal behavioral insight. It mentions 'test' and 'flows', hinting at non-production or diagnostic use, but doesn't disclose critical traits like whether it's read-only, destructive, requires specific permissions, or has side effects (e.g., affecting channel state). This is inadequate for a tool with potential mutation implications.
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 with no wasted words. It's front-loaded with the core purpose, though it could be more structured (e.g., specifying it's for testing Lightning channels). It earns a 4 for being concise but loses a point for lacking clarity.
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 complexity (testing backup/restore flows for channels), no annotations, and no output schema, the description is incomplete. It doesn't explain what 'backup data' entails, the format of results, or error conditions. For a tool with potential state changes, this leaves significant gaps for an AI agent.
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 parameters are fully documented in the schema. The description adds no additional meaning beyond implying 'backup' and 'restore' actions, which are already clear from the enum. Baseline score of 3 is appropriate as the schema handles parameter semantics adequately.
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 'Test channel backup and restore flows' states a general purpose (testing backup/restore) but lacks specificity about what 'channel' refers to (e.g., Lightning Network channels) and doesn't distinguish it from siblings like 'ldk_channel_status' or 'ldk_close_channel'. It's vague about the exact resource being tested.
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. It doesn't mention prerequisites, context (e.g., during development or debugging), or when to choose it over other LDK tools like 'ldk_channel_status' for monitoring. Usage is implied only by the name and description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get chain synchronization implementations for LDK with Electrum/Esplora
| Name | Required | Description | Default |
|---|---|---|---|
| syncMethod | Yes | Chain synchronization method to get implementation for |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states what the tool does but lacks critical details: whether this is a read-only operation, if it requires network connectivity, potential performance impacts, or error handling for invalid syncMethod values. This leaves significant gaps in understanding the tool's 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 purpose without unnecessary words. It's front-loaded with the core action and resource, making it easy to parse quickly.
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 complexity of blockchain synchronization and the lack of annotations and output schema, the description is insufficient. It doesn't explain what 'implementations' entail (e.g., code snippets, configuration details), return format, or error cases, leaving the agent with incomplete context for effective use.
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 input schema has 100% description coverage, with the single parameter 'syncMethod' well-documented via an enum. The description adds no additional parameter semantics beyond implying it fetches implementations for LDK, which aligns with but doesn't expand upon the schema. This meets the baseline for high schema 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 action ('Get') and resource ('chain synchronization implementations for LDK with Electrum/Esplora'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'ldk_network_graph' or 'ldk_node_info' that might also provide blockchain-related information, preventing 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. It doesn't mention prerequisites, context for selecting the syncMethod, or compare it to sibling tools like 'ldk_network_graph' for broader network data, leaving usage ambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Monitor channel states and balances while coding
| Name | Required | Description | Default |
|---|---|---|---|
| includeOffline | No | Include offline/unusable channels |
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 monitoring states and balances, but doesn't disclose behavioral traits such as whether this is a read-only operation, if it requires authentication, rate limits, what 'monitor' entails (e.g., real-time vs. snapshot), or potential side effects. The description is too vague to provide adequate transparency.
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 with no wasted words. It's appropriately sized for a simple tool and front-loaded with the core purpose. Every word earns its place.
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 no annotations, no output schema, and a vague description, the description is incomplete. It doesn't explain what 'channel' refers to, what 'states' and 'balances' mean, the return format, or how monitoring works. For a tool in a context with many siblings (like 'ldk_get_balance'), more detail is needed to be fully helpful.
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 the single parameter 'includeOffline'. The description doesn't add any parameter-specific information beyond what's in the schema, but with high coverage, the baseline is 3. No additional semantic context is provided for the parameter.
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 'Monitor channel states and balances while coding' states a purpose (monitoring) and resources (channel states, balances), but is vague about what 'channel' means in this context and doesn't distinguish from siblings like 'ldk_get_balance' or 'ldk_node_info'. It provides a general action but lacks specificity about what exactly is being monitored.
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. The description mentions 'while coding' which implies a development context, but doesn't specify use cases, prerequisites, or exclusions compared to sibling tools like 'ldk_get_balance' or 'ldk_node_info'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Close a Lightning channel cooperatively or force close
| Name | Required | Description | Default |
|---|---|---|---|
| channelId | Yes | Channel ID to close | |
| force | No | Force close the channel |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the two close modes but doesn't explain critical behaviors like permission requirements, irreversible effects, timing implications, or error conditions. For a destructive operation like channel closure, this lack of detail is a significant gap.
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 states the core functionality without unnecessary words. It's appropriately sized and front-loaded with the essential action, making it easy to parse quickly.
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?
For a destructive operation with no annotations and no output schema, the description is incomplete. It doesn't cover critical context like what happens after closure (funds recovery, timing), error handling, or return values. Given the complexity of Lightning channel operations, more behavioral detail is needed.
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 both parameters ('channelId' and 'force'). The description adds minimal value by implying the 'force' parameter enables force closing, but doesn't provide additional context like consequences of force vs cooperative closure. Baseline 3 is appropriate when schema does the heavy lifting.
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 specific action ('close') and resource ('Lightning channel'), distinguishing it from sibling tools like 'ldk_create_channel' (creation) and 'ldk_channel_status' (status check). It also specifies the two operational modes (cooperative vs force close), making the purpose unambiguous.
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, such as when to choose cooperative vs force close, or prerequisites like channel state. It mentions both modes but doesn't explain their implications or trade-offs, leaving usage decisions unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Open a Lightning channel with a peer node
| Name | Required | Description | Default |
|---|---|---|---|
| remotePubkey | Yes | Remote node public key (hex encoded) | |
| capacitySats | Yes | Channel capacity in satoshis | |
| pushSats | No | Amount to push to remote side (optional) | |
| isPublic | No | Whether to announce channel publicly |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. While 'Open a Lightning channel' implies a write/mutation operation, it doesn't disclose critical behavioral aspects: whether this requires specific permissions, if it's irreversible, what happens on failure, network implications, or confirmation requirements. For a financial operation with no annotation coverage, this is a significant gap.
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 gets straight to the point with zero wasted words. It's appropriately sized for a tool with good schema documentation and follows the principle of front-loading the core purpose.
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?
For a financial channel creation tool with no annotations and no output schema, the description is insufficient. It doesn't explain what happens after channel creation, what the tool returns, error conditions, or integration with the broader Lightning Network context. Given the complexity and lack of structured metadata, more guidance is needed.
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%, providing complete parameter documentation. The description adds no additional parameter semantics beyond what's in the schema - it doesn't explain relationships between parameters (e.g., how pushSats relates to capacitySats) or provide usage examples. With high schema coverage, baseline 3 is appropriate.
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 action ('Open') and resource ('Lightning channel with a peer node'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'ldk_close_channel' or explain what distinguishes channel creation from other operations in the LDK context.
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 prerequisites (like needing a peer connection), when channel creation is appropriate versus other payment methods, or what happens if the peer is unavailable. With sibling tools like 'ldk_pay_invoice' and 'ldk_close_channel', this lack of differentiation is problematic.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Decode and validate Lightning invoices
| Name | Required | Description | Default |
|---|---|---|---|
| invoice | Yes | BOLT11 Lightning invoice to decode |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'decode and validate' but doesn't explain what validation entails (e.g., format checks, signature verification, expiration), whether it's read-only or has side effects, or what happens with invalid inputs. This leaves significant gaps for a tool that likely involves cryptographic operations.
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 extremely concise (4 words) and front-loaded with all necessary information. Every word earns its place by specifying both the action and the resource without any wasted text, making it efficient for quick understanding.
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 complexity of Lightning invoice processing (cryptographic validation, format parsing) and the absence of both annotations and an output schema, the description is insufficient. It doesn't explain what 'validate' means, what output to expect, or error handling, leaving the agent with incomplete context for proper tool invocation.
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 schema has 100% description coverage, with the single parameter 'invoice' clearly documented as a 'BOLT11 Lightning invoice to decode.' The description adds no additional parameter semantics beyond what's in the schema, so it meets the baseline for high schema coverage without compensating value.
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 with specific verbs ('decode and validate') and resource ('Lightning invoices'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'ldk_generate_invoice' or 'ldk_pay_invoice', 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. With sibling tools like 'ldk_generate_invoice' and 'ldk_pay_invoice' available, there's no indication of when decoding/validation is appropriate versus creating or paying invoices, leaving usage context unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Derive Bitcoin addresses from seed using BIP84
| Name | Required | Description | Default |
|---|---|---|---|
| mnemonic | Yes | BIP39 mnemonic phrase | |
| accountIndex | No | Account index (default: 0) | |
| addressIndex | No | Address index (default: 0) | |
| isChange | No | Is change address (default: false) | |
| network | No | Bitcoin network | testnet |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the derivation action but fails to mention critical aspects like security implications (e.g., handling sensitive mnemonic phrases), performance characteristics, or error conditions. This leaves significant gaps in understanding the tool's 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 purpose without any unnecessary words. It is front-loaded and appropriately sized for its function, making it highly concise 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 complexity of Bitcoin address derivation, no annotations, and no output schema, the description is minimally adequate but incomplete. It covers the basic purpose but lacks details on security, output format, or error handling, which are crucial for safe and effective use in this 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?
The input schema has 100% description coverage, providing clear documentation for all 5 parameters. The description adds no additional parameter semantics beyond what's in the schema, so it meets the baseline score of 3 without compensating for any gaps.
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 action ('Derive') and resource ('Bitcoin addresses') with the specific method ('from seed using BIP84'), making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'ldk_generate_mnemonic' or 'ldk_get_balance', 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?
No guidance is provided on when to use this tool versus alternatives, such as other Bitcoin address derivation methods or related sibling tools. The description lacks context about prerequisites or typical use cases, leaving the agent without usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Estimate Lightning routing fees for a payment
| Name | Required | Description | Default |
|---|---|---|---|
| amountSats | Yes | Payment amount in satoshis | |
| targetNode | No | Target node public key (optional) |
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 states the tool estimates fees but does not reveal critical traits such as whether it requires network connectivity, how it calculates fees (e.g., based on current network conditions), or if it has rate limits. This leaves significant gaps in understanding its operation.
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, direct sentence that efficiently conveys the core function without unnecessary words. It is front-loaded with the essential action and resource, making it easy to parse and understand quickly.
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 complexity of Lightning network fee estimation and the lack of annotations and output schema, the description is insufficient. It does not cover behavioral aspects like error handling, return format, or dependencies on network state, leaving the agent with incomplete information for reliable tool invocation.
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 input schema has 100% description coverage, with clear documentation for both parameters ('amountSats' and 'targetNode'). The description adds no additional semantic context beyond what the schema provides, such as explaining fee estimation algorithms or default behaviors when 'targetNode' is omitted. Baseline 3 is appropriate as the schema handles parameter documentation adequately.
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 action ('Estimate') and the resource ('Lightning routing fees for a payment'), making the purpose immediately understandable. However, it does not differentiate from siblings like 'ldk_pay_invoice' or 'ldk_decode_invoice', which also involve payment-related operations, leaving room for potential confusion.
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. For example, it does not specify if this should be used before 'ldk_pay_invoice' to check costs or as a standalone fee estimator, nor does it mention prerequisites or exclusions, leaving usage context unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get comprehensive LDK event handling patterns and implementations
| Name | Required | Description | Default |
|---|---|---|---|
| eventType | Yes | Type of event handling to get code for |
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 states the tool 'gets' information, implying a read-only operation, but doesn't specify whether this requires authentication, has rate limits, returns structured data or code snippets, or involves any side effects. For a tool with no annotation coverage, this leaves significant behavioral gaps.
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 core purpose without unnecessary words. Every part of the sentence contributes directly to understanding the tool's function, making it appropriately concise 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 has one parameter with full schema coverage and no output schema, the description is minimally adequate. It clarifies the tool retrieves 'patterns and implementations' (likely code-related), which adds context beyond the schema. However, for a tool with no annotations and potentially complex LDK event handling, more details on output format or use cases would improve completeness.
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 input schema has 100% description coverage, with the 'eventType' parameter fully documented via enum values. The description adds no additional parameter semantics beyond what the schema provides (e.g., it doesn't explain what 'event_persistence' means or how the output varies by event type). This meets the baseline for high schema 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 tool's purpose: 'Get comprehensive LDK event handling patterns and implementations' - it specifies the action ('Get'), the resource ('LDK event handling patterns and implementations'), and scope ('comprehensive'). However, it doesn't explicitly differentiate from sibling tools like 'ldk_list_payments' or 'ldk_channel_status' which might also involve event-related operations, so it doesn't reach the highest 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. It doesn't mention any specific contexts, prerequisites, or exclusions, nor does it reference sibling tools. The agent must infer usage solely from the tool name and description without explicit direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate a Lightning invoice with real payment hash for testing
| Name | Required | Description | Default |
|---|---|---|---|
| amountSats | Yes | Amount in satoshis | |
| description | No | Invoice description | |
| expirySeconds | No | Invoice expiry time in seconds (default: 3600) |
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 mentions 'real payment hash' and 'for testing,' which hints at functionality but lacks details on critical behaviors: whether this is a read-only or write operation, if it requires network connectivity, what happens on failure, or any rate limits. For a tool that likely interacts with a Lightning network, this is insufficient.
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 core purpose without unnecessary words. It's appropriately sized for the tool's complexity, making it easy for an AI agent to parse quickly.
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 lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., an invoice string or error details), behavioral traits like side effects or permissions, or how it fits into the broader testing workflow. For a tool with 3 parameters and no structured output, more context is needed.
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 schema description coverage is 100%, so the input schema already documents all parameters (amountSats, description, expirySeconds) with descriptions and defaults. The description adds no additional parameter semantics beyond implying invoice generation, which aligns with the schema but doesn't provide extra value. This meets the baseline score of 3 for high schema 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 tool's purpose: 'Generate a Lightning invoice with real payment hash for testing.' It specifies the action (generate), resource (Lightning invoice), and context (for testing). However, it doesn't explicitly differentiate from sibling tools like 'ldk_pay_invoice' or 'ldk_decode_invoice', which is why it doesn't achieve 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 minimal usage guidance by mentioning 'for testing,' which implies a context but doesn't specify when to use this tool versus alternatives (e.g., 'ldk_pay_invoice' for actual payments or 'ldk_decode_invoice' for parsing). There's no explicit when/when-not advice or prerequisite information, leaving gaps for an AI agent to infer proper usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate BIP39 mnemonic for wallet initialization
| Name | Required | Description | Default |
|---|---|---|---|
| strength | No | Mnemonic strength (128 = 12 words, 256 = 24 words) | |
| wordCount | No | Number of words in mnemonic (12 or 24) |
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 states the tool generates a mnemonic but does not cover critical aspects like security implications (e.g., randomness source, storage), whether it requires user confirmation, or what the output format is. This is a significant gap for a tool handling cryptographic keys.
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 purpose without unnecessary words. It is front-loaded with the core action and resource, making it easy to parse quickly, which is ideal for 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 complexity of cryptographic key generation, lack of annotations, and no output schema, the description is incomplete. It fails to address security behaviors, output format, or integration with other tools (e.g., how the mnemonic is used in wallet initialization), leaving the agent with insufficient context for safe and effective use.
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%, with clear descriptions for both parameters (strength and wordCount) including enums and defaults. The description does not add any meaning beyond the schema, such as explaining the relationship between strength and word count or typical use cases for each option, so it meets 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 action ('Generate') and the resource ('BIP39 mnemonic'), with the purpose 'for wallet initialization' providing context. It does not explicitly differentiate from sibling tools like 'ldk_derive_address' or 'ldk_create_channel', which might also relate to wallet setup, but the specificity of generating a mnemonic is sufficient for clarity.
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, such as whether it's for initial setup versus recovery, or how it relates to sibling tools like 'ldk_derive_address'. It mentions 'wallet initialization', but this is part of the purpose statement rather than explicit usage instructions, leaving gaps in context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get iOS Lightning wallet architecture patterns and best practices
| Name | Required | Description | Default |
|---|---|---|---|
| topic | Yes | Architecture topic to explore |
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 states this is a 'Get' operation, implying read-only behavior, but doesn't clarify if it requires authentication, has rate limits, returns structured data or documentation, or involves any side effects. For a tool with no annotations, 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 purpose without unnecessary words. It is front-loaded with the key action and resource, making it easy to parse quickly. Every part of the sentence contributes essential information.
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 has one well-documented parameter (100% schema coverage) and no output schema, the description is minimally adequate but lacks depth. It doesn't explain what the output looks like (e.g., text, structured data), potential errors, or how it integrates with other tools. For a tool with no annotations and no output schema, more context on behavior and results would improve completeness.
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 input schema has 100% description coverage, with the 'topic' parameter fully documented via its enum values (e.g., 'project_structure', 'security_architecture'). The description adds no additional parameter semantics beyond what the schema provides, such as explaining what each topic entails or how topics relate. With high schema coverage, the baseline score of 3 is appropriate.
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 action ('Get') and the resource ('iOS Lightning wallet architecture patterns and best practices'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'ldk_get_swift_code' or 'ldk_get_balance', which also retrieve information but about different aspects of the Lightning wallet.
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 prerequisites, context for selecting specific topics, or how it relates to sibling tools like 'ios_background_test' or 'ldk_test_scenario' that might involve architecture testing. Usage is implied only by the tool name and description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get Lightning wallet balance and channel liquidity
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states what the tool does but lacks critical details: whether it's a read-only operation, if it requires authentication, potential rate limits, or what the return format looks like (e.g., structured data vs raw numbers). For a financial tool with zero annotation coverage, this is a significant gap.
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 core purpose ('Get Lightning wallet balance and channel liquidity'). There is zero waste—every word contributes directly to understanding the tool's function without redundancy or fluff.
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 complexity (financial/blockchain tool), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what 'balance' includes (e.g., on-chain vs off-chain), how liquidity is reported, or error conditions. For a tool in this domain with rich sibling tools, more context is needed to ensure safe and correct 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% (empty schema is fully described). The description doesn't need to add parameter semantics, so it meets the baseline of 4 for zero-parameter tools. No additional value is required or provided beyond the schema.
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 with specific verbs ('Get') and resources ('Lightning wallet balance and channel liquidity'). It distinguishes itself from siblings like ldk_list_payments (which lists payments) or ldk_channel_status (which focuses on channel status), but doesn't explicitly differentiate beyond the natural scope of 'balance' vs other operations.
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 prerequisites (e.g., needing an initialized wallet), exclusions (e.g., not for historical balances), or comparisons to siblings like ldk_node_info (which might include balance info). Usage is implied by the name but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get Swift code examples for specific LDK operations
| Name | Required | Description | Default |
|---|---|---|---|
| operation | Yes | Type of operation to get Swift code for |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states it 'gets' examples, implying a read-only operation, but does not specify if it requires authentication, has rate limits, returns structured data, or any other behavioral traits. This is a significant gap for a tool with zero 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, clear sentence that directly states the tool's purpose without any fluff or redundancy. It is front-loaded and efficiently conveys the essential information, making it highly concise 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 complexity of LDK operations and the lack of annotations and output schema, the description is incomplete. It does not explain what the Swift code examples look like, their format, or any behavioral aspects, leaving gaps for the agent to understand the tool fully 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?
The input schema has 100% description coverage, with the 'operation' parameter well-documented via an enum. The description adds no additional semantic details beyond what the schema provides, such as explaining the context of each operation type. Thus, it meets the baseline for high schema coverage but does not enhance parameter understanding.
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 'Get' and the resource 'Swift code examples for specific LDK operations', making the purpose evident. However, it does not explicitly differentiate from sibling tools like 'ldk_get_architecture' or 'ldk_get_balance', which might also retrieve information, so it lacks sibling differentiation for 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, such as other LDK-related tools in the sibling list. It does not mention prerequisites, context, or exclusions, leaving the agent without usage direction beyond the basic purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List recent Lightning payments with status
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of payments to return | |
| status | No | Filter by payment status | all |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'recent' payments but doesn't define what 'recent' means (time window, count-based?). It also doesn't disclose important behavioral aspects like pagination, rate limits, authentication requirements, or what format the returned data will have.
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 extremely concise - a single sentence that communicates the core purpose efficiently. There's no wasted language or unnecessary elaboration, making it easy to parse quickly.
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?
For a payment listing tool with no annotations and no output schema, the description is insufficient. It doesn't explain what information will be returned about each payment, how 'recent' is defined, whether there are ordering guarantees, or any error conditions. The agent would need to guess about the response format and behavior.
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 schema has 100% description coverage, so the baseline is 3. The description adds no additional parameter semantics beyond what's already documented in the schema (limit and status with their enums). It doesn't explain relationships between parameters or provide usage examples.
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 action ('List') and resource ('recent Lightning payments with status'), making the purpose immediately understandable. However, it doesn't differentiate this tool from potential sibling payment-related tools (like 'ldk_pay_invoice'), which would require more specific scope definition.
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. While the sibling list shows related tools like 'ldk_get_balance' and 'ldk_pay_invoice', there's no indication of when to choose listing payments over checking balance or making payments.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get network graph operations and RapidGossipSync implementation
| Name | Required | Description | Default |
|---|---|---|---|
| operation | Yes | Network graph operation to perform | |
| nodeId | No | Node ID for node_info operation (optional) | |
| channelId | No | Channel ID for channel_info operation (optional) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'operations' and 'implementation' but doesn't specify whether this is a read-only tool, if it performs mutations, what permissions are needed, or any side effects like rate limits or data destruction. The vague terms offer minimal insight into how the tool behaves beyond its name.
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, concise sentence that efficiently states the tool's scope without unnecessary words. It's front-loaded with the main purpose, though it could be more structured by listing key operations. There's no wasted text, making it appropriately sized for a high-level overview.
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 (multiple operations via an enum parameter) and lack of annotations and output schema, the description is incomplete. It doesn't explain what each operation does, what the tool returns, or any behavioral traits. For a tool with 3 parameters and varied functionality, this minimal description leaves significant gaps for an agent to understand and use it effectively.
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 (operation, nodeId, channelId) with descriptions and enum values for 'operation'. The description adds no additional meaning about parameters, such as explaining what each operation does or how optional parameters interact. It meets the baseline of 3 since the schema does the heavy lifting, but doesn't compensate with extra context.
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 states the tool handles 'network graph operations and RapidGossipSync implementation', which gives a general purpose but lacks specificity about what it actually does. It mentions two broad categories but doesn't clarify what 'operations' entail or how RapidGossipSync is implemented. It distinguishes somewhat from siblings like ldk_node_info or ldk_channel_status by focusing on network graph functionality, but the distinction remains vague.
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 any prerequisites, exclusions, or specific contexts for use. Given siblings like ldk_node_info and ldk_channel_status that might overlap with some operations (e.g., node_info, channel_info), the lack of differentiation leaves the agent without clear usage instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get current node status and connectivity information
| 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 mentions 'Get current node status and connectivity information,' which implies a read-only operation, but it doesn't specify details like whether this requires authentication, what data is returned (e.g., format, fields), or any rate limits. For a tool with zero annotation coverage, this is a significant gap in transparency.
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, clear sentence that directly states the tool's function without any unnecessary words. It is front-loaded and efficient, making it easy to understand at a glance, which is ideal for 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 has 0 parameters, 100% schema coverage, and no output schema, the description is minimally adequate. However, it lacks details on what 'node status and connectivity information' entails (e.g., specific fields or format), which could be important for an AI agent to understand the return value. Without annotations or an output schema, the description should provide more context to be fully complete.
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 the schema description coverage is 100%, so there's no need for parameter details in the description. The description appropriately focuses on the tool's purpose without redundant parameter information, earning a high score as it efficiently handles the lack of parameters.
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 with a specific verb ('Get') and resource ('current node status and connectivity information'), making it immediately understandable. However, it doesn't differentiate this tool from potential sibling tools that might also provide node-related information, such as 'ldk_channel_status' or 'ldk_network_graph', 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. With sibling tools like 'ldk_channel_status' and 'ldk_network_graph' that might overlap in providing node or network information, there's no indication of when this tool is preferred or what specific context it serves, leaving usage ambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Test payment flows by paying a Lightning invoice
| Name | Required | Description | Default |
|---|---|---|---|
| invoice | Yes | BOLT11 Lightning invoice to pay | |
| maxFeeSats | No | Maximum fee in satoshis willing to pay |
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 'Test payment flows', hinting at non-production use, but fails to disclose critical behavioral traits: whether this is a real payment (mutative/destructive), authentication needs, rate limits, error handling, or what happens on success/failure. For a payment tool with zero annotation coverage, this is a significant gap.
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 with zero waste. It's front-loaded with the core action ('pay a Lightning invoice') and includes the testing context concisely. Every word earns its place, making it easy to parse quickly.
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 no annotations, no output schema, and a payment tool (inherently mutative/destructive), the description is incomplete. It lacks details on behavioral impact (e.g., funds transfer, irreversibility), response format, error cases, or testing constraints. The schema covers parameters well, but overall context is inadequate for safe agent use.
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%, with clear parameter docs in the schema. The description adds no additional parameter semantics beyond implying the invoice is for payment testing. It doesn't explain format constraints (e.g., BOLT11 specifics) or fee implications beyond the schema. Baseline 3 is appropriate as the schema does the heavy lifting.
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 action ('pay') and resource ('Lightning invoice'), and specifies the context ('Test payment flows'). It distinguishes from siblings like 'ldk_generate_invoice' (creation) and 'ldk_decode_invoice' (analysis), but doesn't explicitly differentiate from other payment-related tools (none listed). The purpose is specific but could be more distinct.
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 minimal guidance with 'Test payment flows', implying usage for testing rather than production. However, it lacks explicit when-to-use criteria, prerequisites (e.g., invoice validity), or alternatives (e.g., other payment methods). No comparison to siblings like 'ldk_list_payments' is made, leaving usage context vague.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Run complete Lightning development scenarios for testing
| Name | Required | Description | Default |
|---|---|---|---|
| scenario | Yes | Test scenario to run |
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 'run complete...scenarios' but doesn't disclose behavioral traits such as whether this is a read-only operation, if it modifies state, execution time, side effects, or what 'complete' entails. For a testing tool with potential complexity, this is a significant gap in transparency.
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 core purpose. It avoids unnecessary words, though it could be slightly more informative given the tool's potential complexity. It earns its place but leaves room for improvement in clarity.
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 likely involves complex testing scenarios (as hinted by enum values like 'force_close'), no annotations, and no output schema, the description is incomplete. It doesn't explain what 'run' entails, expected outputs, or behavioral implications, making it inadequate for an agent to understand the tool's full 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%, with the parameter 'scenario' fully documented in the schema including an enum of specific test scenarios. The description adds no additional parameter semantics beyond what's in the schema, so it meets the baseline of 3 for high schema coverage without compensating value.
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 states the tool runs 'complete Lightning development scenarios for testing', which provides a general purpose but lacks specificity. It mentions 'Lightning' and 'testing' context, but doesn't specify what types of scenarios or what 'complete' entails. Compared to siblings like 'ldk_channel_status' or 'ldk_pay_invoice', it's vaguely distinguished as a testing tool rather than a specific operation.
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. The description doesn't mention prerequisites, when-not scenarios, or relate to sibling tools like 'ios_background_test' or other LDK tools. It implies usage for testing but offers no context on appropriate scenarios or limitations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Most tools have distinct purposes, but some overlap exists, such as 'ldk_event_handling' and 'ldk_test_scenario' which could be confused for general testing, and 'ldk_get_architecture' and 'ldk_get_swift_code' which both provide implementation patterns. However, descriptions clarify their specific focuses, reducing ambiguity.
All tools follow a consistent snake_case naming pattern with clear prefixes: 'ios_' for iOS-specific tools and 'ldk_' for Lightning-related tools. This predictable structure makes it easy to identify tool categories and purposes at a glance.
With 23 tools, the count is borderline high for a Lightning development server, as it includes many specific testing and implementation tools. While comprehensive, it may feel heavy and could potentially be streamlined without losing core functionality.
The tool set provides complete coverage for Lightning development on iOS, including channel management (create, close, monitor), payments (invoice generation, decoding, paying), wallet operations (balance, addresses, mnemonics), testing (scenarios, background, notifications), and implementation patterns (architecture, Swift code). No obvious gaps are present for the domain.
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