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ApexVol MCP Server

ApexVol MCP Server

by ryansilk
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Ticker Search

search_tickers
Read-only

Check if a stock symbol is supported or resolve a company name to its ticker before running options analysis.

Instructions

Search or validate tickers against the platform's coverage universe.

Use before deep analysis when unsure a symbol is supported, or to resolve a company name to its ticker.

Args: query: Symbol or company-name fragment (e.g. "NVDA" or "nvidia") limit: Max matches to return (1-20)

Returns: Ranked matches plus exact-match/supported flags

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": {
      "query": "<query>"
    },
    "name": "search_tickers"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNo
queryYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

A4.5/5.0
Behavior4/5

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

The description adds behavioral context beyond the readOnlyHint annotation by explaining matching behavior, ranking, and exact-match/supported flags. It also implies fuzzy lookup via company-name fragments, enhancing the agent's understanding of results. No contradiction with 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 compact, well-structured with Args and Returns sections, and every sentence adds value. It avoids fluff, front-loads the core purpose, and uses clear formatting for parameters.

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?

Given the tool's simple nature and lack of output schema, the description gives a sufficient preview of return values (ranked matches, flags). It's complete for its purpose, though a bit more detail on result ordering or flag semantics could elevate it further.

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?

Despite 0% schema coverage, the description provides rich parameter semantics: it defines query as a symbol or company-name fragment with examples ("NVDA", "nvidia") and states limit is a max matches (1-20). This fully compensates for the sparse 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 it searches or validates tickers against the platform's coverage universe, with a specific verb and resource. It distinguishes itself from sibling tools by explicitly mentioning its role before deep analysis and resolving company names, differentiating from data-retrieval tools like get_stock_price.

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?

It explicitly instructs when to use the tool ('before deep analysis when unsure a symbol is supported, or to resolve a company name to its ticker'), providing clear context. While it doesn't explicitly name alternatives or say when not to use, the guidance is actionable and sets expectations.

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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