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crypto-deep-research

crypto-deep-research

by alikula37
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

list_analyses

Retrieve the available analysis keys for crypto-deep-research to select valid analyses and run supported market research workflows.

Instructions

Kullanilabilir analiz anahtarlarini listeler.

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": "list_analyses"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden. 'Listeler' implies a safe read, but nothing is said about ordering, whether the list is exhaustive, or whether keys are stable identifiers. With an output schema present, return-format disclosure is not required, but the behavioral profile is still thin.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

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

A single short sentence with no waste, and the action is front-loaded. It is terse but not padded, so it earns its brevity.

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?

For a parameterless tool with a full output schema, the description is minimally adequate: it says what is returned without needing to describe the shape. It still omits the crucial context of what the returned keys are used for (e.g., as input to run_analysis), which is the main thing an agent needs to act on the result.

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 takes zero parameters, which is the baseline-4 case. The description correctly implies a no-argument enumeration, matching the empty schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a clear verb and resource ('lists available analysis keys'), which is more than a tautology. However, it offers no differentiation from siblings like list_reports, list_research_items, or list_analyses' natural partner run_analysis, so an agent cannot tell from the text alone why it would pick this over the other listers.

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

Usage Guidelines2/5

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

There is no guidance on when to call this tool, no prerequisites, and no mention of alternatives. The agent must infer from context that these keys feed run_analysis.

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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