tool-selection

1207 public resources for Jev, TypeSafe AI's System One decision model, indexed by decision pattern. Source citations, dated link checks and scheduled call-site text checks; runtime and performance are not independently tested here. EN/中文, JSON schema and platform compatibility.

Context engineering for AI agents. ~80% fewer tokens. Fix tool overload. Skills and memory with in-process BM25 and semantic retrieval. Progressive Disclosure. No vector DB.

Dynamically expose tools from proxied servers based on an Agent Persona

Pre-inference tool and agent routing for MCP, A2A and tool-rich LLM systems. Reduce candidate tools before model inference with policy-aware routing and recovery.

Local decision model with calibrated probabilities: send a state and yes/no, choice or score questions, get a probability for every option. 0.8B GGUF on CPU, Jev-style API.

Search 1,000 MCP tools through two interfaces in DeepSeek Harness. Load exact schemas on demand and keep structured results usable across tool calls.

🧰 Tool Search Engine for Efficient Tool Use in AI Agents

Explore Functions, Tools and Agents with LangChain along with LangChain Expression Language

Wald-Q4B: open-weight 4B decision model. Calibrated probability for every option, Jev-compatible /v1/systemone API, self-hosted. Weights on Hugging Face.

Paper Implementation: Semantic Tool Discovery for Large Language Models: A Vector-Based Approach to MCP Tool Selection https://arxiv.org/html/2603.20313v1

Your agent has too many tools. Hybrid search + a fast decision model (Jev, Laya) or an LLM pick the right one, or say "none". Pydantic AI integration and a ToolRet benchmark.

Open-source decision layer for AI agents. Typed task, tool, skill and review routing through TypeSafe Jev, with local policy gates and auditable receipts.

The selection layer for agents: which tool an agent can use, should use, and pays — between discovery (MCP/ARD) and settlement (x402/AP2)

A new package that helps users compare and choose the right data analysis tool by providing structured, expert-level insights. Users input their specific data analysis needs, project requirements, or

Local-first MCP context budget and tool-selection verifier for agentic coding environments.

repick

A tool that lets you run many tools at the same time with Ai for bug bounty !!

Open-source RAG router for MCP — fronts all your MCP servers and exposes only the relevant tools per query, killing context overload.

In-process Go library for intent-aware MCP tool selection: keyword intent detection, priority-ordered selection rules, token-budget pruning, and a per-tool enrichment pipeline.

Tool and skill suggestions for Codex Desktop and CLI, with local-first privacy and optional Jev ranking