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AlgoChains MCP Server

AlgoChains MCP Server

Official
by AlgoChains
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Python
Hybrid

ingest_json_signals

Idempotent

Import pre-computed signals, ML features, labels, or regime tags from a JSON file into AlgoChains for ML training. Supports entry/exit signals, feature vectors, classification labels, and regime classifications.

Instructions

Ingest a JSON file of pre-computed signals, ML features, labels, or regime tags into AlgoChains. Supports entry/exit signals, feature vectors, classification labels, and regime classifications. Data becomes available for ML training.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "file_path": "<file_path>",
      "signal_type": "<signal_type>",
      "symbol": "<symbol>"
    },
    "name": "ingest_json_signals"
  }
}

Input Schema

NameRequiredDescriptionDefault
symbolYesTicker symbol the signals are for.
file_pathYesAbsolute path to the JSON file.
signal_typeYesType of signal data.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv22.7.1

TDQS

A4/5.0
Behavior3/5

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

Annotations already provide idempotent and non-destructive hints. The description adds that data becomes available for ML training, but does not disclose potential side effects like overwriting existing data or required permissions.

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?

Two sentences with no fluff. The first sentence front-loads the core action and resource, making it efficient.

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

Completeness4/5

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

The description covers the main purpose and supported data types, but does not mention return values or error conditions. Given no output schema, this is a minor gap.

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?

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the meaning of each signal_type enum value, which is not fully detailed in the schema.

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 action (ingest), the resource (JSON file of signals into AlgoChains), and the purpose (for ML training). It lists supported signal types, distinguishing it from other ingestion tools.

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

Usage Guidelines3/5

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

The description implies when to use the tool (for ingesting JSON signal data), but does not explicitly state when to use alternatives like 'ingest_csv_data' or provide exclusion criteria.

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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