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SonAIengine/graph-tool-call

v0.4.0 Feature

This release adds 3 notable features for engineering teams evaluating rollout.

Published 3mo MCP Developer Tools
✓ No known CVEs patched
Read the diff → Tool health → What is this tool? →

✓ No known CVEs patched in this version

Topics

agent agentic ai anthropic function-calling hybrid-search
+13 more
langchain llm mcp mcp-server openai openapi python swagger token-optimization tool-calling tool-retrieval workflow-engine zero-dependency

Summary

AI summary

Added MCP annotation-aware retrieval with new model, ingest API, intent classifier, and OpenAPI automatic mapping.

Full changelog

graph-tool-call v0.4.0 — MCP Annotation-Aware Retrieval

Phase 2.5: query intent와 tool annotation alignment를 retrieval signal로 활용하는 4-source wRRF 파이프라인.

주요 기능

  • MCP Annotation 모델: readOnlyHint, destructiveHint, idempotentHint, openWorldHint
  • MCP tool ingest: tg.ingest_mcp_tools(tools, server_name="myserver")
  • Intent Classifier: 한/영 키워드 기반 zero-LLM query intent 분류
  • Annotation Scorer: intent↔annotation alignment scoring (4번째 wRRF source, weight=0.2)
  • OpenAPI 자동 추론: HTTP method → MCP annotation 매핑 (RFC 7231)
  • 255 tests (74개 신규), 기존 API 하위 호환

설치

pip install graph-tool-call              # 코어
pip install graph-tool-call[all]         # 모든 기능

Quick Start

from graph_tool_call import ToolGraph

tg = ToolGraph()
tg.ingest_mcp_tools([
    {"name": "read_file", "description": "Read a file",
     "inputSchema": {"type": "object", "properties": {"path": {"type": "string"}}},
     "annotations": {"readOnlyHint": True}},
    {"name": "delete_file", "description": "Delete a file",
     "inputSchema": {"type": "object", "properties": {"path": {"type": "string"}}},
     "annotations": {"destructiveHint": True}},
])

# "삭제" intent → destructive tool 우선
results = tg.retrieve("파일 삭제해줘", top_k=5)

Full Changelog: https://github.com/SonAIengine/graph-tool-call/compare/v0.3.0...v0.4.0

View diff on GitHub

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About SonAIengine/graph-tool-call

When tool count exceeds LLM context limits, accuracy collapses (248 tools → 12%). graph-tool-call builds a tool graph from OpenAPI/MCP specs and retrieves multi-step workflows via hybrid search (BM25 + graph traversal + embedding), recovering accuracy to 82% with 79% fewer tokens. Zero dependencies. Also works as an MCP Proxy — aggregate multiple MCP servers behind 3 meta-tools.

All releases →

Related context

Beta — feedback welcome: [email protected]