lpxpoly-mcp is an MCP server providing AI-powered analysis of Polymarket prediction markets, with payments via Bitcoin Lightning Network (~50 sats per analysis using a spend token from lightningprox.com).
Find Edge Opportunities (get_edge_opportunities): Discover mispriced markets where AI probability assessments disagree with current prices, with configurable limits and minimum edge thresholds.
Analyze Specific Markets (analyze_market): Get a deep AI assessment of a specific market by searching with a query (e.g. 'Fed rate cut March 2025'), including probability prediction, reasoning, and recommended position.
Browse Top Markets (get_top_markets): View the most active Polymarket markets ranked by volume, with optional filtering by category (politics, crypto, sports, economics, etc.).
Check Balance (check_balance): View your current LightningProx wallet balance in sats to ensure sufficient funds for analyses.
Enables users to pay for prediction market analysis using Bitcoin sats via the Lightning Network and provides a dedicated tool for checking their Lightning account balance.
MCP server for LPXPoly — AI-powered Polymarket prediction market analysis. Find mispriced markets, get edge opportunities, and analyze specific markets. Pay per analysis in Bitcoin sats via Lightning.
npx lpxpoly-mcpRelated MCP server: SimpleFunctions
Go to lightningprox.com
Pay a Lightning invoice to fund your balance
Get your spend token
~50 sats per analysis
{
"mcpServers": {
"lpxpoly": {
"command": "npx",
"args": ["lpxpoly-mcp"],
"env": {
"LIGHTNINGPROX_SPEND_TOKEN": "your_token_here"
}
}
}
}Config location:
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
Linux: ~/.config/claude/claude_desktop_config.json
Tool | Description |
| Find mispriced markets with AI edge |
| Deep analysis of a specific market |
| Browse top markets by volume |
| Check your Lightning balance |
"Find edge opportunities on Polymarket"
→ Returns top mispriced markets with AI probability vs market probability
"Analyze the Fed rate cut market"
→ Returns AI assessment, reasoning, and recommended position
"What are the top crypto markets on Polymarket?"
→ Returns top markets filtered by category~50 sats per analysis. Pay with Bitcoin Lightning. No subscription.
LPXPoly is discoverable via the AIProx open agent registry:
curl https://aiprox.dev/api/agents/lpxpolyAIProx Registry: https://aiprox.dev
LightningProx (Bitcoin Lightning AI): npx lightningprox-mcp
SolanaProx (Solana USDC AI): npx solanaprox-mcp
AIProx Registry: npx aiprox-mcp
Autonomous agent demo: https://github.com/unixlamadev-spec/autonomous-agent-demo
Built by LPX Digital Group LLC
Get AI analysis of a specific Polymarket prediction market. Returns probability assessment, reasoning, and recommended position.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Market question or search term (e.g. 'Fed rate cut March 2025', 'Trump approval rating') |
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 the tool returns analysis and recommendations but fails to describe critical traits like whether it's a read-only operation, if it requires authentication, rate limits, error handling, or how it interacts with the market data. 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 concise and front-loaded, consisting of a single sentence that directly states the tool's function and return values. There is no wasted text, but it could be slightly improved by structuring usage guidance separately for better 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 lack of annotations and output schema, the description is incomplete for a tool that performs AI analysis on markets. It does not explain the format or structure of the returned analysis, potential limitations, or how the 'recommended position' should be interpreted, leaving the agent with insufficient 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 'query' parameter well-documented as a market question or search term. The description adds no additional meaning beyond this, such as formatting examples or constraints not in the schema. 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 tool's purpose with a specific verb ('Get AI analysis') and resource ('specific Polymarket prediction market'), and mentions the return content ('probability assessment, reasoning, and recommended position'). However, it does not explicitly distinguish this tool from its siblings (e.g., 'get_top_markets'), 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 'get_top_markets' or 'get_edge_opportunities'. It lacks context about prerequisites, such as whether the market must exist or if authentication is needed, and offers no explicit when-not-to-use scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Check your LightningProx balance in sats. Required to pay for LPXPoly analyses.
| 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. It discloses that this is a read operation ('Check') and hints at a prerequisite for analyses, but lacks details on authentication needs, rate limits, error conditions, or return format. For a tool with no annotations, this is a moderate gap, scoring 3 as it provides basic behavioral context but misses key operational details.
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 two concise sentences with zero waste. The first sentence states the purpose, and the second adds crucial usage context. Every word earns its place, making it highly 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 simplicity (0 parameters, no output schema, no annotations), the description is reasonably complete for its purpose. However, it lacks details on return values (e.g., balance format, units) and doesn't fully address behavioral aspects like error handling. For a balance-checking tool, this is adequate but leaves some gaps, scoring 3.
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% (as there are no parameters to describe). 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: 'Check your LightningProx balance in sats.' It specifies the verb ('Check') and resource ('LightningProx balance'), and distinguishes it from siblings by focusing on balance rather than market analysis or opportunities. However, it doesn't explicitly differentiate from hypothetical similar tools like 'get_balance' or 'view_balance', keeping it at 4 instead of 5.
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 clear context for when to use the tool: 'Required to pay for LPXPoly analyses.' This implies it should be used before or during payment-related operations for analyses. It doesn't explicitly state when NOT to use it or name alternatives among siblings, but the context is sufficiently clear for a 4.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find mispriced Polymarket prediction markets where AI analysis disagrees with current market probability. Returns top edge opportunities with recommended positions.
| Name | Required | Description | Default |
|---|---|---|---|
| min_edge | No | Minimum edge percentage to include (default: 5) | |
| limit | No | Maximum number of opportunities to return (default: 10) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the tool's purpose (finding mispriced markets with AI disagreement) and output (top edge opportunities with positions), but lacks details on behavioral traits like rate limits, authentication needs, or what 'mispriced' entails operationally. It adds basic context but misses deeper behavioral insights.
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 front-loaded and highly concise—two sentences with zero waste. The first sentence defines the purpose and scope, and the second specifies the return value, making it efficient and easy to parse.
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 moderate complexity (2 parameters, no output schema, no annotations), the description is reasonably complete. It covers the purpose, output, and high-level logic (AI vs. market probability), but could benefit from more detail on behavioral aspects or output structure to fully compensate for the lack of annotations and output schema.
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 both parameters. The description does not add any parameter-specific meaning beyond what the schema provides (e.g., it doesn't explain 'edge percentage' in more detail or how 'limit' interacts with ranking). Baseline 3 is appropriate as 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 clearly states the specific action ('Find mispriced Polymarket prediction markets') and resource ('edge opportunities'), distinguishing it from siblings like 'analyze_market' or 'get_top_markets' by emphasizing AI-disagreement analysis and recommended positions. It goes beyond a simple list to identify mispriced opportunities.
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 when seeking mispriced markets based on AI analysis, but does not explicitly state when to use this tool versus alternatives like 'analyze_market' or 'get_top_markets'. No exclusions or prerequisites are mentioned, leaving some ambiguity about optimal context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the most active Polymarket markets by volume with current probabilities.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of markets to return (default: 10) | |
| category | No | Filter by category (politics, crypto, sports, economics, etc.) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. It mentions 'most active by volume' and 'current probabilities' but doesn't specify data freshness, rate limits, authentication needs, or what 'most active' means (e.g., time window). For a read operation with no annotations, this leaves significant gaps in understanding tool 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 front-loads the core purpose without unnecessary words. Every part ('Get', 'most active Polymarket markets', 'by volume', 'with current probabilities') contributes directly to understanding the tool's function.
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 moderate complexity (2 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on usage context, behavioral traits, or output format. Without annotations or output schema, more completeness would be beneficial for effective 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%, so the schema already documents both parameters ('limit' and 'category') with descriptions. The description adds no additional parameter semantics beyond what the schema provides, such as explaining 'category' values or 'limit' constraints. 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 ('Get'), resource ('most active Polymarket markets'), and key attributes ('by volume with current probabilities'). It distinguishes this tool from siblings like 'analyze_market' (which likely analyzes specific markets) and 'check_balance' (which deals with user accounts).
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 when this tool is appropriate (e.g., for trending analysis vs. specific market queries) or when to prefer siblings like 'analyze_market' for detailed market examination. No exclusions or prerequisites 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: analyze_market for detailed AI analysis of a specific market, check_balance for financial status, get_edge_opportunities for finding mispriced markets, and get_top_markets for trending markets. There is no overlap or ambiguity in functionality.
All tool names follow a consistent verb_noun pattern with snake_case (e.g., analyze_market, check_balance, get_edge_opportunities, get_top_markets). The naming is predictable and readable throughout the set.
With 4 tools, the server is well-scoped for its purpose of analyzing Polymarket prediction markets. Each tool earns its place by covering distinct aspects: analysis, balance checking, opportunity discovery, and market overview.
The tool set covers core workflows for market analysis and financial management, but there are minor gaps such as no direct tool for executing trades or managing positions on Polymarket, which agents might need to work around using external methods.
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If you are the server author, to access and configure the admin panel.
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