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arcus-agent-gateway

arcus-agent-gateway

by alekskram
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Sector View

sector_view
Read-only

Get sector map with per-sector size and live averages for 13 sectors. Optionally warm the cache for fresh quotes or filter to a single sector.

Instructions

Sector map (13 sectors, static validated classification) with per-sector size and live averages.

warm=False (default): cheap snapshot - averages appear only for quotes ALREADY cached ('warmed': false; no requests made). warm=True: fan out quotes() first (parallel batches of 20, upstream rate-limit safe), then report - averages are live. With sector= 'Name' only that sector is fetched and returned; an unknown name raises with the valid list. requests_made counts the price-cache misses at the start of the warm pass (each miss = one upstream request; fresh cache hits and cached-again symbols cost nothing). Example: sector_view(warm=True, sector="Crypto/Digital Assets")

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": "sector_view"
  }
}

Input Schema

NameRequiredDescriptionDefault
warmNo
sectorNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A5/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint, openWorldHint, destructiveHint=false), the description discloses important runtime behavior: warm=False makes no upstream requests, warm=True fans out quotes() with parallel batches, requests_made counts cache misses, and unknown sector names raise with the valid list. This adds meaningful transparency about side effects and performance.

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 well-structured, with each sentence adding value: the one-line summary, the warm=False behavior, the warm=True behavior, the sector filtering/error case, and a concrete example. It is front-loaded with the core purpose and does not waste words.

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 an output schema exists, the description doesn't need to explain return shapes. It covers the parameter semantics, edge cases, caching/rate-limit behavior, and error handling. Nothing essential is missing for the agent to select and invoke the tool correctly.

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

Parameters5/5

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

Schema description coverage is 0%, but the description fully compensates. It explains the exact meaning and consequences of warm, the behavior of sector including the 'Name' string format, the unknown-name error, and the example sector value 'Crypto/Digital Assets'. All two parameters are semantically covered.

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 opens with a specific, actionable statement: 'Sector map (13 sectors, static validated classification) with per-sector size and live averages.' This clearly identifies the resource and the kind of data returned, and distinguishes it from sibling tools like token_list or quote by describing a sector-level aggregate view.

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?

The description explicitly distinguishes warm=False and warm=True, telling the agent when to use the cheap snapshot versus the live data path, and explains the rate-limit-safe batching behavior. It also covers the sector filter and error behavior for unknown names, giving clear decision rules.

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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