Curistat Mcp

C+ 72 completed
Other
containerized / python · tiny
10
Files
364
LOC
0
Frameworks
5
Languages

Pipeline State

completed
Run ID
#1527830
Phase
done
Progress
0%
Started
2026-04-16 14:33:54
Finished
2026-04-16 14:33:54
LLM tokens
0

Pipeline Metadata

Stage
Skipped
Decision
skip_tiny
Novelty
11.98
Framework unique
Isolation
Last stage change
2026-04-16 18:15:42
Deduplication group #47284
Member of a group with 520 similar repo(s) — canonical #588649 view group →
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AI Prompt

I want to build a Python application that acts as a futures volatility intelligence server, following the Model Context Protocol (MCP). The server needs to provide various analytical tools for products like ES and NQ. Specifically, I need functions to get the daily and 5-day volatility forecasts, scan all rare volatility signals, determine the directional bias, and generate a full session plan. It should also include tools for regime detection, market condition snapshots, and research capabilities like fetching the economic calendar or finding historical days similar to the current one. Please structure it so it can be integrated with agent frameworks like CrewAI or LangChain.
python futures volatility mcp finance api agent forecasting
Generated by gemma4:latest

Catalog Information

I want to build a Python application that acts as a futures volatility intelligence server, following the Model Context Protocol (MCP). The server needs to provide various analytical tools for products like ES and NQ. Specifically, I need functions to get the daily and 5-day volatility forecasts, scan all rare volatility signals, determine the directional bias, and generate a full session plan. It should also include tools for regime detection, market condition snapshots, and research capabiliti

Tags

python futures volatility mcp finance api agent forecasting

Quality Score

C+
71.5/100
Structure
64
Code Quality
100
Documentation
65
Testing
15
Practices
78
Security
100
Dependencies
90

Strengths

  • CI/CD pipeline configured (github_actions)
  • Code linting configured (ruff (possible))
  • Consistent naming conventions (snake_case)
  • Low average code complexity — well-structured code
  • Good security practices — no major issues detected
  • Containerized deployment (Docker)
  • Properly licensed project

Weaknesses

  • No tests found — high risk of regressions

Recommendations

  • Add a test suite — start with critical path integration tests

Languages

python
41.5%
markdown
25.5%
toml
16.3%
json
11.4%
yaml
5.2%

Frameworks

None detected

Symbols

function11
constant3
variable1

Quality Timeline

1 quality score recorded.

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BinComp Dependency Hardening

All packages →
2 of this repo's dependencies have been scanned for binary hardening. Grade reflects RELRO / stack canary / FORTIFY / PIE coverage.
Nmcp1.27.0 · 0 gadgets · risk 971.5Nhttpx0.28.1 · 0 gadgets · risk 0.0