Skip to main content
Glama
Sign In
crypto-deep-research

crypto-deep-research

by alikula37
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

get_report

Retrieves generated crypto research reports as markdown by report name, enabling AI assistants to access saved deep research analysis.

Instructions

Rapor adiyla markdown icerigini dondurur.

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": {
      "name": "<name>"
    },
    "name": "get_report"
  }
}

Input Schema

NameRequiredDescriptionDefault
nameYes

Output Schema

NameRequiredDescriptionDefault

No arguments

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. It discloses the return payload (markdown content) but says nothing about error behavior for unknown names, permissions, or whether results are cached or live.

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 the action and result front-loaded and no filler. It is efficient, though it is written in a different language than the sibling tool names, which slightly hurts scanability.

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 values need no further explanation. For a simple one-parameter getter this is nearly adequate, but with no annotations the description still omits failure modes and lookup semantics for the name.

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

Parameters3/5

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

Schema description coverage is 0%, so the description must compensate. It does clarify that the sole 'name' parameter refers to the report name ("Rapor adiyla"), which adds meaning beyond the bare schema, but gives no format, case, or exactness details.

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

Purpose4/5

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

The description states a specific verb and resource: it returns ("dondurur") the markdown content of a report identified by its name. This is clear and non-tautological, but it does not distinguish the tool from the obvious sibling list_reports, which an agent must infer on its own.

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 use this tool versus alternatives such as list_reports or get_run. The agent is left to infer that this fetches a single report's content while list_reports enumerates them.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Other Tools