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arcus-agent-gateway

arcus-agent-gateway

by alekskram
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Market Status

market_status
Read-only

Check overall market status for tokenized equities: active, untradable, halted token counts, extended-hours open, and regular market-hours status, using cached asset data with no price fetches.

Instructions

Market-wide health from assets() only - never fetches 194 prices. total_tokens, active (ASSET_STATUS_ACTIVE), untradable (fractional untradable among active), halted (only tokens whose quote is ALREADY in the price cache - opportunistic; call quotes() on suspect symbols for a real halt scan), extended_hours_open and market_hours_status. Example: market_status()

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "market_status"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already indicate read-only and non-destructive behavior, and the description adds important behavioral nuances: it relies only on assets(), avoids fetching hundreds of prices, and treats halted as opportunistic rather than exhaustive. It honestly discloses the limitation of the halted field and points to the appropriate fallback.

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 dense but every clause adds value: it explains the data source, scope, key fields, limitations, and a corrective alternative. There is no filler or repetition of schema/annotation information.

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?

For a zero-parameter read-only tool with an output schema present, this description is complete. It covers what the tool computes, what it intentionally excludes, how halted detection works, and how to get more accurate halt data when needed.

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 and the schema fully describes this, so the description does not need to explain parameters. The inclusion of an example call, "market_status()", reinforces the no-argument invocation.

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 reports "Market-wide health" and specifies the exact data source and computed fields. It differentiates itself from sibling pricing tools by explicitly saying it "never fetches 194 prices," making its scope unambiguous.

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

Usage Guidelines5/5

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

It gives clear context for when to use this tool: for market-wide aggregates from assets() rather than per-symbol pricing. It also names an alternative action—"call quotes() on suspect symbols for a real halt scan"—so an agent knows exactly when to route elsewhere.

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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