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AlgoChains

AlgoChains MCP Server

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by AlgoChains

get_ai_pipeline_health

Read-onlyIdempotent

Detect quota errors, model errors, pipeline timeouts, and shadow mode in the AI ensemble pipeline. Provides advisory health status for trading decisions.

Instructions

Check AI ensemble/debate pipeline health. Detects Anthropic quota errors, Cerebras model errors (llama3.1-8b), pipeline timeout events, and shadow mode. The pipeline is ADVISORY ONLY — primary confidence gate controls all trades regardless of pipeline state.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bot_idNomnq
Behavior4/5

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

Annotations already indicate read-only and idempotent, but the description adds value by specifying the advisory nature of the pipeline and what errors are detected, which goes 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.

Conciseness5/5

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

The description is two sentences, front-loads purpose, and provides essential details without fluff.

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 explains what the tool detects but does not describe the return format or output. Since no output schema exists, this is a gap, though the tool is simple.

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

Parameters2/5

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

The schema has 0% parameter descriptions and the description does not explain the bot_id parameter (enum mnq, cl, mes, nq). With low schema coverage, the description should fill this gap but does not.

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 checks 'AI ensemble/debate pipeline health' and lists specific detections (Anthropic quota errors, etc.), making it distinct from sibling health 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 (for pipeline health monitoring) but does not explicitly differentiate from other health tools like get_system_health or specify when not to use it.

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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