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AlgoChains

AlgoChains MCP Server

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by AlgoChains

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Idempotent

Subscribe to copy-trade signals from live futures strategies (MNQ, CL, MES, NQ). Set your own trade size, max contracts, and daily loss cap. Pause or leave anytime.

Instructions

Subscribe the authenticated subscriber to a strategy's published copy-trade SIGNALS (the subscriber reviews and acts on them — the platform does not auto-execute or exercise discretion). The subscriber sets their own size and can pause/leave anytime. Strategies: MNQ (micro Nasdaq scalper), CL (crude oil scalper), MES (micro S&P swing), NQ (Nasdaq swing). Enforces a seat cap per strategy — returns bot_at_capacity if full. Requires the futures risk disclosure to be acknowledged first (accept_subscriber_terms) and ALGOCHAINS_SUBSCRIBER_KEY to be set. Re-calling with an existing subscription updates size_multiplier and un-pauses.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
botYesWhich strategy's published signals to subscribe to
max_contractsNoHard cap on contracts per signal
size_multiplierNoTrade-size multiplier vs the master bot (0 < x <= 10)
daily_loss_cap_usdNoDaily loss hard limit in USD
Behavior4/5

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

The description adds behavioral details beyond annotations: it enforces a seat cap, requires specific prerequisites, and re-calling updates size_multiplier and un-pauses. Annotations indicate idempotentHint=true and destructiveHint=false, which align with the description's statement that re-calling is safe and not destructive.

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

Conciseness4/5

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

The description is efficient, packing critical information (nature of signals, strategies, prerequisites, re-call behavior) into a single dense paragraph without redundancy. Every sentence adds value, but a slight improvement could be structuring into bullet points.

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

Completeness3/5

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

The description covers key contextual aspects: prerequisites, error condition (bot_at_capacity), and re-call behavior. However, it lacks description of the successful return value structure, which is important given there is no output schema.

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

Parameters3/5

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

Schema coverage is 100% and the schema already describes parameter details (enum values, defaults, ranges). The description adds minimal extra meaning beyond listing strategies, which duplicates the schema enum. Baseline 3 is appropriate.

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 clearly states the tool subscribes to a strategy's copy-trade signals, lists the specific strategies (MNQ, CL, MES, NQ), and distinguishes the action from sibling tools like accept_subscriber_terms and get_subscriber_status by explicitly mentioning prerequisites and subscription update behavior.

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 explains when to use the tool (to subscribe to signals) and provides prerequisites (accept_subscriber_terms, set API key) and re-call behavior. It does not explicitly state when not to use it, but the mention of seat cap and 'returns bot_at_capacity if full' provides implicit guidance.

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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