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crypto-deep-research

crypto-deep-research

by alikula37
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

deep_research

Run a 66-item crypto market analysis on a chosen coin, platform, timeframe, and lookback period to generate scores, probabilities, a report, and an optional prompt.

Instructions

66 maddelik arastirmayi calistirir; skor, olasilik, rapor ve prompt uretir.

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": {
      "coin": "<coin>"
    },
    "name": "deep_research"
  }
}

Input Schema

NameRequiredDescriptionDefault
coinYes
platformNogeneric
timeframeNo1d
lookback_daysNo
include_promptNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.7/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It does disclose the artifacts produced (score, probability, report, prompt), which is useful, but says nothing about runtime, cost, permissions, or whether the operation has side effects on stored data.

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 compact sentence with the action and its outputs front-loaded and no filler. It is efficient, though the brevity shades into under-specification rather than true conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists so return values need no explanation, but for a 5-parameter, unannotated tool the description leaves parameter meaning, usage context, and behavioral traits almost entirely undocumented.

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

Parameters2/5

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

Schema description coverage is 0% across 5 parameters, so the description must compensate and largely does not. It never explains coin, platform, timeframe, or lookback_days; only the word 'prompt' loosely hints at include_prompt.

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 names a concrete verb ('calistirir' / runs) and resource (a 66-item research) and lists its outputs (score, probability, report, prompt). However, '66 maddelik arastirma' is too vague to tell the agent what domain this covers, and it gives no differentiation from the sibling run_analysis, leaving overlap unresolved.

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 when-to-use guidance, no prerequisites, and no mention of alternatives such as run_analysis or list_analyses. The agent must infer the intended context entirely on its own.

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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