Pique Signal
OfficialProvides tools for paper trading Solana memecoins, including retrieving signals, real-time prices, simulated buy/sell orders, and portfolio management on the Solana blockchain.
Pique Signal MCP Server
Paper trade Pique Signal alerts directly in Claude Code. Test Convergence Engine signals on Solana memecoins risk-free before deploying real capital.
The Convergence Engine scores tokens across on-chain data, liquidity, holder behavior, and momentum. When enough independent signals converge, it fires a Flash Point alert. This MCP server lets you pull those alerts, paper trade them with a built-in risk engine, and track your P&L over days or weeks without risking anything.
Start with the 72-hour free trial. If the signals are finding runners on paper, subscribe and deploy real money. If not, walk away with nothing lost.
Quick Start
1. Get an API key
Send /apikey to @piquesignalbot on Telegram. The 72-hour free trial starts automatically.
2. Install
git clone https://github.com/piquesignal/piquesignal-mcp.git
cd piquesignal-mcp
npm install3. Add to Claude Code
claude mcp add piquesignal -- node /path/to/piquesignal-mcp/src/index.jsSet PIQUESIGNAL_API_KEY to your API key when prompted.
4. Start evaluating
> "Any strong signals in the last hour?"
> "That one looks good, buy it"
> "How are my positions doing?"
> "Show my portfolio"Tools
Tool | Description |
| Pull live Flash Point alerts with safety data, scores, and market metrics |
| Real-time token price in SOL via Jupiter, DexScreener fallback |
| Simulated buy with confidence-scaled sizing, liquidity checks, and exposure caps |
| Close a position at market price. Returns realized P&L and hold time |
| Open positions with live prices and unrealized P&L. Auto-triggers SL/TP |
| Balance, total P&L, win rate, completed trades, risk config |
Configuration
All configuration is via environment variables in your Claude Code MCP config:
Variable | Default | Description |
| required | Your API key |
|
| API endpoint |
|
| Starting paper balance |
|
| Max SOL per trade |
|
| Max total open exposure |
|
| Auto stop-loss percentage |
|
| Auto take-profit percentage |
|
| Min pool liquidity to trade |
Risk Management
Every paper trade runs through the risk engine before execution:
Position sizing scales with signal score (higher confidence = larger position)
Exposure cap limits total open positions to
MAX_EXPOSURE_SOLDuplicate blocking prevents buying a token you already hold
Liquidity floor rejects tokens below
MIN_LIQUIDITY_USDStop-loss / Take-profit auto-executes when thresholds hit
Slippage simulation applies realistic 0.1-0.5% slippage on fills
Paper Trading
All trades are simulated with real-time prices from Jupiter and DexScreener. State persists to ~/.piquesignal/paper-state.json so your portfolio survives across sessions.
Track your P&L over the 72-hour free trial to see if the Convergence Engine is finding runners worth trading. If the numbers work on paper, deploy real capital. If they don't, you lost nothing.
Security
API key stored in environment variables, never logged or exposed in tool outputs
HTTPS enforced on all API communication (validated at startup)
API key format validated (
pqs_prefix required)Server-side rate limit: 60 requests/min per IP. Client-side safety buffer: 30 requests/min
Zod schema validation on all tool inputs, Base58 validation on all mint addresses
Response sanitization strips internal fields via allowlist (only surfaced data returned)
No shell execution, no eval, no dynamic imports
Requirements
Node.js 18+
Claude Code
Pique Signal API key (free trial)
License
MIT
Available Tools
6 toolsget_portfolioARead-onlyIdempotent
Get full paper trading portfolio summary: balance, P&L, win rate, trade history, and risk configuration.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, covering safety. The description adds value by specifying the output contents, which provides additional behavioral context beyond annotations. No contradictions. Could mention freshness or caching, but adequate.
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?
Single sentence of 16 words, immediately states purpose and lists contents. No filler, perfectly front-loaded. 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?
For a simple read-only portfolio summary with no parameters and no output schema, the description covers the main output elements. It could be slightly more complete by specifying time period or data scope, but it is sufficient for typical 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?
Input schema has zero parameters, so no parameter documentation is needed. Schema description coverage is 100% (none). Baseline for 0 parameters is 4; description does not add parameter info but it's not required.
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?
Description clearly states it retrieves a paper trading portfolio summary, listing specific components (balance, P&L, win rate, trade history, risk configuration). It unambiguously distinguishes from sibling tools like get_positions (individual positions) and paper_buy/sell (trading actions).
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 the tool is for obtaining a high-level portfolio overview. While no explicit when-to-use or when-not-to-use guidance is given, the context of sibling tools makes the appropriate scenario clear. A minor gap is the lack of explicit exclusion or alternative references.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_positionsC
View all open paper positions with live prices. Automatically triggers stop-loss or take-profit sells if price thresholds are hit.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description states it triggers stop-loss or take-profit sells, which are destructive actions, but annotations have destructiveHint: false and readOnlyHint: false (not read-only). This is a clear contradiction, misleading the agent about side effects.
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?
Two sentences, no waste. The critical behavioral detail is included upfront. Could be better structured (e.g., separating view action from trigger condition) but still efficient.
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?
No output schema, and the description does not explain what the tool returns (e.g., list of positions with prices). The trigger condition is vague ('if price thresholds are hit'). Annotations are contradictory, reducing completeness for agent decision-making.
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?
No parameters exist, so schema coverage is 100%. The description adds no param info but none is needed. The baseline is 4 for zero-parameter tools.
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 it views open paper positions with live prices and mentions automatic sell triggers. This distinguishes it from siblings like paper_sell and get_portfolio. However, the dual role of viewing and triggering actions could be confusing without further clarification.
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 on when to use this tool versus alternatives like paper_sell or get_portfolio. The description does not specify when automatic sell triggers occur, nor does it exclude cases where manual selling is needed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_priceARead-onlyIdempotent
Get the current price of any Solana token in SOL. Checks Jupiter first, falls back to DexScreener.
| Name | Required | Description | Default |
|---|---|---|---|
| mint | Yes | Solana token mint address |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, idempotent behavior. The description adds the fallback order (Jupiter first, then DexScreener), which is useful behavioral context beyond annotations.
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?
Two sentences, no wasted words, front-loaded with purpose and fallback detail. 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?
The tool is simple with one parameter and no output schema. The description omits the return format (e.g., number, object with source), which would be helpful for an agent. Adequate but not 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?
Input schema has 100% coverage with a description for the only parameter 'mint'. The description adds no additional parameter semantics beyond what the schema already provides, so 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 verb 'Get' and resource 'current price of any Solana token in SOL', which is distinct from sibling tools that deal with portfolio, positions, signals, and trading.
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 mentions the fallback mechanism (Jupiter then DexScreener) but provides no explicit guidance on when to use this tool over alternatives or when not to use it. Usage is implied by sibling uniqueness.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_signalsARead-onlyIdempotent
Fetch recent trading signals from Pique Signal. Returns scored Solana token alerts with on-chain safety data, market cap, liquidity, holder metrics, and signal strength.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max signals to return (default 10) | |
| min_score | No | Only return signals with score >= this value | |
| since_minutes | No | Look back N minutes (default 60) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only and idempotent. Description adds specific return data (safety, market cap, etc.), providing behavioral context beyond annotations. No contradictions.
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?
Single sentence, front-loaded with verb and resource, no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema, but description covers return type comprehensively. Purpose, data, and parameters are sufficiently explained for a read-only data fetch tool.
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 has 100% coverage with detailed descriptions for all 3 parameters. Description does not add further parameter detail; 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?
Clearly specifies verb 'fetch', resource 'trading signals' from 'Pique Signal', and lists return types (scored Solana token alerts with on-chain safety data, etc.). Differentiates from siblings like get_positions, get_price which are separate concerns.
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?
States purpose and return value, context is clear given sibling tools are unrelated. However, lacks explicit when-not-to-use or alternative suggestions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
paper_buyA
Execute a paper (simulated) buy order for a Solana token. Uses real-time prices from Jupiter/DexScreener. Risk manager enforces position limits, exposure caps, and minimum liquidity.
| Name | Required | Description | Default |
|---|---|---|---|
| mint | Yes | Solana token mint address | |
| amount_sol | No | SOL amount to spend (default: max position size, confidence-scaled) | |
| symbol | No | Token symbol for display | |
| score | No | Signal score (used for confidence-based position sizing) | |
| liquidity_usd | No | Pool liquidity in USD (checked against minimum) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate non-read-only and non-destructive behavior. The description adds that it uses real-time prices and that a risk manager enforces position limits and liquidity checks. It could further clarify what happens when limits are exceeded, but overall it provides useful behavioral context.
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?
Two sentences that are front-loaded with the core action. Every sentence adds value; no unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 5 parameters and no output schema, the description covers the purpose and constraints but omits important details like return values (e.g., order ID or status) and error cases. It is adequate but not comprehensive.
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 coverage is 100% with descriptions for all parameters. The description adds extra meaning like 'confidence-scaled' for amount_sol, going beyond what is in the schema. No contradictions.
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 it executes a simulated buy order for a Solana token, distinguishing it from siblings like paper_sell. 'Execute a paper (simulated) buy order' is specific and actionable.
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?
Usage is implied by the name and description (use when you want to buy), but there is no explicit guidance on when not to use it, such as during high slippage or when risk limits are hit. No alternatives are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
paper_sellADestructive
Sell an open paper position. Fetches real-time exit price and calculates P&L.
| Name | Required | Description | Default |
|---|---|---|---|
| mint | Yes | Solana token mint address to sell | |
| reason | No | Reason for selling (e.g. "taking profit", "cutting loss") |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate destructiveHint=true, so the description does not need to restate that. It adds useful behavioral context about real-time price fetching and P&L calculation. No contradictions with annotations.
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 sentence that is clear and front-loaded. Every part earns its place, with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description does not explain what the tool returns (e.g., trade confirmation, P&L details). It also lacks safety notes on irreversible actions, which would be helpful for a destructive operation.
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 for both parameters (mint and reason). The description does not add meaning beyond the schema, so it meets the baseline but does not exceed it.
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 action: 'Sell an open paper position.' It also adds value by noting that it fetches a real-time exit price and calculates P&L, making the purpose specific and distinct from siblings like paper_buy and get_positions.
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 an open paper position exists, but it does not explicitly state when to use this tool versus alternatives (e.g., paper_buy or get_positions). No exclusions or prerequisites are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
Each tool has a clearly distinct purpose: portfolio overview, positions, price, signals, buy, and sell. No overlap or ambiguity.
All tools follow a consistent verb_noun snake_case pattern (e.g., get_portfolio, paper_buy), making it predictable for an agent.
6 tools is well-scoped for a paper trading server with signals. It covers essential operations without unnecessary clutter.
Core paper trading lifecycle is covered: view portfolio, positions, price, signals, buy, sell. Minor gaps like cancel order or limit order are absent, but the set is functional.
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