Decompose
B+ 86 completed
Ai Ml
cli / html · small
82
Files
10,211
LOC
1
Frameworks
9
Languages
Pipeline State
completedRun ID
#298960Phase
doneProgress
1%Started
Finished
2026-04-13 01:31:02LLM tokens
0Pipeline Metadata
Stage
CatalogedDecision
proceedNovelty
72.80Framework unique
—Isolation
—Last stage change
2026-05-10 03:35:24Deduplication group #65837
Member of a group with 2 similar repo(s) — canonical #82676 view group →
Top concepts (6)
RepositorytestingStrategyLoggingSearchTesting
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🧪 Code Distillation
Browse all specs →Sample distilled functions (click for full spec)
call_toolExecutes a specified tool based on the provided name and arguments dictionary. If the name is "decompose_text", it processes text content; if it is "decompose_url", it fetches content from a URL before processing it. The function returns a list containing a single TextContent object, which holds the
list_toolsReturns a list containing definitions for two available tools. These tools are named "decompose_text" and "decompose_url," each providing a structured description and an input schema detailing required parameters. The function takes no arguments and produces a list of Tool objects, which are self-co
_fetch_urlRetrieves the content from a specified web address, accepting a URL string and an optional timeout integer. It sends a request with a specific user agent and attempts to return the body content directly if the server indicates markdown or plain text. Otherwise, it processes the received HTML content
AI Prompt
Create a command-line tool using Python that provides deterministic text classification for AI agents. I need it to take text input, either piped from a file or provided inline, and output a structured JSON format. The tool should support options like `--pretty` for readable output, or `--compact` for a smaller JSON structure. Additionally, I want to expose functionality to decompose content fetched from a URL, and I'd like to include a library function example showing how to call `decompose_text` and use `filter_for_llm` to reduce token count.
python cli text-classification ai-agents json
Generated by gemma4:latest
Catalog Information
This project provides a deterministic text classification system for use in AI agents, enabling structured intelligence from unstructured text.
Description
Decompose-MCP is a text classification tool designed to provide structured intelligence to AI agents. It enables the extraction of meaningful information from any text, making it a valuable resource for applications that require accurate and reliable text analysis. The project's focus on determinism ensures consistent results, reducing the risk of errors or biases in decision-making processes.
الوصف
هذا المشروع يقدم نظام تصنيف نصي محدد بشكل دقيق للاستخدام في एजENTS الذكية، مما يسمح بتحليل النصوص غير المنظمة وتحويلها إلى معلومات موصوفة. يعتبر هذا المشروع مفيدًا للأنظمة التي تتطلب تحليلًا دقيقًا وثابتًا للنصوص.
Novelty
7/10Tags
text-classification deterministic-models structured-intelligence ai-agents natural-language-processing
Claude Models
claude-opus-4.6
Quality Score
B+
86.4/100
Structure
98
Code Quality
84
Documentation
86
Testing
85
Practices
68
Security
100
Dependencies
80
Strengths
- CI/CD pipeline configured (github_actions)
- Good test coverage (73% test-to-source ratio)
- Code linting configured (ruff (possible))
- Consistent naming conventions (snake_case)
- Good security practices \u2014 no major issues detected
- Properly licensed project
Security & Health
4.6h
Tech Debt (B)
Medium
DORA Rating
A
OWASP (100%)
Repobility · MCP-ready · https://repobility.com
PASS
Quality Gate
A
Risk (1)
MIT
License
2.5%
Duplication
Languages
Frameworks
pytest
Symbols
variable41
function32
constant24
class7
method5
Concepts (6)
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| design_pattern | Repository | Found repository-named files | 80% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| arch_layer | testing | Detected testing layer | 70% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| design_pattern | Strategy | Found strategy/policy-named files | 60% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| business_logic | Logging | Detected from 18 related files | 50% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| business_logic | Search | Detected from 3 related files | 50% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| business_logic | Testing | Detected from 12 related files | 50% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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