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crypto-deep-research

crypto-deep-research

by alikula37
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

list_research_items

Returns the 66-item research list. Use to access all research items for crypto deep research analysis.

Instructions

66 maddelik arastirma listesini dondurur.

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_research_items"
  }
}

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 behavioral burden, yet it only implies a fixed-size read (66 items). It does not state whether the call is read-only, whether results can be paginated, how the 66 items are ordered, or what authentication is needed.

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 that is front-loaded and free of filler. It is arguably over-terse, but nothing is wasted.

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?

An output schema exists, so return-value documentation is not required, and a zero-parameter list tool is simple. Still, the complete absence of annotations and of any when-to-use routing against three sibling list tools leaves a meaningful gap.

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, so there is no parameter semantics to explain; baseline 4 applies. The description correctly implies no filtering arguments are accepted.

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 verb (dondurur/returns) and a resource (arastirma listesi/research list), so the basic action is clear. However, "66 maddelik" (66-item) adds a hard-coded count instead of defining scope, and nothing distinguishes it from siblings like list_analyses or list_reports, which are also list-shaped tools.

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 versus list_analyses, list_reports, or deep_research. The agent is left to infer that this is the general research-listing entry point purely from the name.

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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