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mcp

by com.bykaranteli

Recent closed trading signals with verified outcomes

get_recent_signals
Read-only

Call this when the user asks how the ByKaranteli signal engine is doing today, or wants recent closed LONG/SHORT signals with real outcomes (TP1, SL or TIMEOUT) and net basis-point results. Includes a 24h summary (wins, losses, net bps). Every signal is published with a SHA-256 receipt and results are net of fees, slippage and funding; live signals only, never backtests.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.7/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the readOnlyHint annotation, the description discloses important behavioral traits: results are net of fees, slippage and funding; every signal has a SHA-256 receipt; and only live signals are included. It also states it includes a 24h summary (wins, losses, net bps), which adds return-context beyond the annotation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loaded with the trigger condition, followed by key output details and caveats. Every sentence earns its place with no redundancy or filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no parameters and no output schema, the description is remarkably complete. It covers what the tool does, what data it returns (outcomes, net bps, 24h summary), and critical distinctions (live only, never backtests). No additional context seems missing for an agent to select and invoke this tool appropriately.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, so schema coverage is effectively 100%. The description does not need to explain parameters and instead adds context about the returned data. This matches the baseline of 4 for a 0-parameter tool.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the tool returns recent closed LONG/SHORT signals from the ByKaranteli signal engine with verified outcomes (TP1, SL, TIMEOUT) and net basis-point results. It distinguishes from sibling tools by naming the specific engine and emphasizing 'live signals only, never backtests.'

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives direct trigger conditions: 'Call this when the user asks how the ByKaranteli signal engine is doing today, or wants recent closed LONG/SHORT signals.' It also provides a clear when-not: 'live signals only, never backtests.' However, it does not name alternative tools for backtest or historical queries, so it lacks that explicit alternative mention.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.2/5.0
Disambiguation4/5

Most tools are highly specific with clear triggers in their descriptions, distinguishing metrics like funding, liquidations, and options flow. Some overlap exists—for instance, get_funding_heatmap and get_top_movers both cover extreme funding, and get_pressure_scores, get_psi_charge, and get_top_movers all address derivative stress—but the descriptions provide enough differentiation for an agent to pick correctly.

Naming Consistency5/5

All tools follow the consistent 'get_' prefix followed by descriptive snake_case names that clearly indicate the data domain (e.g., get_etf_flows, get_funding_heatmap). The pattern is uniform and predictable across the entire set.

Tool Count4/5

At 33 tools, the server is on the heavy side, but the domain of crypto derivatives and on-chain data is broad, and each tool covers a distinct niche metric or analysis. While slightly above the ideal range, the count is justified for the comprehensive scope, though it may be unwieldy for an agent to load at once.

Completeness5/5

The surface is remarkably complete for crypto market analysis: it covers sentiment (get_market_indices), derivatives (funding, open interest, liquidations, options), macro (get_macro_liquidity, get_cot_positioning), on-chain (get_network_health, get_quantum_exposure), and integrity (get_recent_signals, get_slippage). Historical context and conditional analysis are also provided (get_factor_board, get_metric_context). No major dead ends are apparent.

Resources