Context7
Up-to-date docs for AI agents
作者提供的说明(英文原文)
Evidence-first entity and knowledge graph MCP server for AI agents to verify claims.
这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。
我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。
选择你使用的 AI 应用,然后复制设置。
作者没有在注册中心列出安装信息。请查看下方作者的 README。
它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。
我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日
注册名称已通过 GitHub 账户 AI-BuildInfra 验证,该账户拥有此仓库
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
它在你的电脑上运行;我们只能读取互联网端点的工具列表
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。
它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。
注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。
它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。
GitHub 仓库最后更新于–,最新版本(1.0.0)于43天前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。
同一分类中有:Context7, Codebase Memory, Exa
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i
Up-to-date docs for AI agents
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Connect AI agents to Exa for web search, content fetching, and multi-step research.
按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
Verify Claims • Trace Provenance • Understand Entities • Retrieve 90% Less Context
Published by [AI Build Infra](https://aibuildinfra.com/) • Official MCP Identifier: io.github.AI-BuildInfra/proof-graph
Explore Features • Quick Start • MCP Tools • Token Benchmark • Architecture
Traditional Retrieval-Augmented Generation (RAG) dumps 5,000 to 20,000 unverified tokens of noisy HTML, sidebars, and duplicate paragraphs into LLM prompts. This causes context bloat, higher token costs, latency spikes, and hallucination amplification.
ProofGraph replaces fuzzy text dumping with Minimum Sufficient Context backed by Cryptographic Provenance:
Question ──► Entity Resolution ──► Claim Verification ──► Evidence Ranking ──► Token Optimizer ──► Compact Proof Packet
🎯 ProofGraph retrieves proof, not just text.
| Feature | Description | Benefit |
|---|---|---|
| 🛡️ Evidence-First Verification | Cryptographic SHA-256 hashes & timestamped citations | Eliminates unsupported hallucinations |
| 🔍 Multi-Signal Entity Resolution | Combines Jaro-Winkler, domain, GitHub org & npm scope | Resolves aliases without false-positive merges |
| ⚡ Token Budget Optimizer | Enforces strict budget caps (500–5,000 tokens) | ~90.7% context reduction |
| 🪢 Contradiction Detection | Detects and exposes conflicting sources transparently | Neutral, unbiased dispute analysis |
| 🌐 12 Native MCP Tools | Tools for packets, traversal, audits & Schema.org JSON-LD | Instant drop-in for Claude, Antigravity, Cursor |
| 🔒 Enterprise SSRF Protection | Blocks loopback, RFC 1918 subnets, and cloud metadata | Safe for automated agentic execution |
npxNo installation required:
npx -y @aibuildinfra/proofgraph
npm install -g @aibuildinfra/proofgraph
Add ProofGraph to your MCP configuration file (claude_desktop_config.json or Antigravity/Cursor MCP settings):
{
"mcpServers": { "proofgraph": { "command": "npx", "args": ["-y", "@aibuildinfra/proofgraph"] } } }
ProofGraph equips AI agents with 12 powerful verification and retrieval tools:
┌────────────────────────────────────────────────────────────────────────────┐
│ PROOFGRAPH MCP TOOLS │ ├─────────────────────────┬──────────────────────────────────────────────────┤ │ get_evidence_packet │ Primary retrieval: Minimal sufficient proof │ │ search_entities │ Multi-signal fuzzy & alias entity matching │ │ get_entity │ Canonical record, relationships & Schema.org │ │ verify_claim │ Verify assertions against cryptographic proof │ │ find_evidence │ Token-budgeted evidence extraction for a claim │ │ find_relationships │ 1-hop, 2-hop, 3-hop graph traversal paths │ │ explain_entity │ Concise factual entity profile & key edges │ │ trace_claim │ Step-by-step provenance audit trail │ │ compare_claims │ Impartial discrepancy detection across claims │ │ analyze_web_presence │ Digital footprint analysis across 8 platforms │ │ audit_entity_consistency│ Detect mismatches in names, domains & metadata │ │ export_graph │ Export graph to JSON, JSON-LD, CSV, GraphML │ └─────────────────────────┴──────────────────────────────────────────────────┘
When an AI agent queries:
{
"question": "Is AI Build Infra associated with HumanCraft and ProofGraph?" }
ProofGraph returns a compact, citation-grounded evidence packet:
{
"entities": [ { "id": "entity:organization:ai-build-infra", "name": "AI Build Infra", "canonical_url": "https://aibuildinfra.com/" } ], "claims": [ { "claim": "AI Build Infra develops MCP servers and open-source agent tooling.", "status": "supported", "confidence": 0.98 } ], "relationships": [ { "source_id": "entity:organization:ai-build-infra", "relationship": "DEVELOPS", "target_id": "entity:project:proofgraph" }, { "source_id": "entity:organization:ai-build-infra", "relationship": "DEVELOPS", "target_id": "entity:project:humancraft" } ], "evidence": [ { "source": "MCP Registry", "url": "https://registry.modelcontextprotocol.io/servers/io.github.AI-BuildInfra/humancraft-ui", "excerpt": "Server identifier io.github.AI-BuildInfra/humancraft-ui registered under AI Build Infra publisher identity.", "confidence": 0.99 } ], "provenance": [ { "source_id": "source:registry:mcp-official", "content_hash": "10c56a410a5b22e6f3037126be38073b20e950c2ded2ee36815f20072041b889", "retrieved_at": "2026-09-25T12:00:00Z" } ], "metrics": { "raw_estimated_tokens": 4500, "returned_tokens": 485, "compression_ratio": 0.108 } }
ProofGraph dramatically cuts LLM context consumption without sacrificing retrieval precision:
| Benchmark Query | Traditional Raw RAG | ProofGraph Packet | Token Savings | Verified Citations |
|---|---|---|---|---|
| "Who is AI Build Infra?" | ~4,500 tokens | 999 tokens | 77.8% | 4 primary proofs |
| "What is HumanCraft?" | ~4,500 tokens | 219 tokens | 95.1% | Grounded profile |
| "What evidence connects HumanCraft to AI Build Infra?" | ~4,500 tokens | 221 tokens | 95.1% | Relationship path |
| "Is AI Build Infra associated with ProofGraph?" | ~4,500 tokens | 485 tokens | 89.2% | Direct proof + rels |
| "What services does AI Build Infra provide?" | ~4,500 tokens | 164 tokens | 96.4% | Grounded service claim |
| Cumulative Total | ~22,500 tokens | 2,088 tokens | 90.7% Context Reduction | 100% Traceable |
Run the benchmark locally anytime:
proofgraph benchmark
flowchart TD
Agent[AI Agent / Claude / Antigravity / Cursor] <--> MCP[ProofGraph MCP Protocol Layer]
subgraph Engine [Deterministic Verification Engine] MCP <--> Optimizer[Token Budget & Deduplication Engine] MCP <--> Resolver[Multi-Signal Entity Resolver] MCP <--> Verifier[Claim & Contradiction Verifier] MCP <--> Traversal[1/2/3 Hop Graph Traversal] end
subgraph Storage [Cryptographic Provenance Store] Entities[(Canonical Entities)] Claims[(Verified Claims)] Evidence[(Evidence & SHA-256 Hashes)] Sources[(Authoritative Sources)] Relationships[(Typed Graph Multigraph)] end
Engine <--> Storage
subgraph Security [Security & Safety Boundary] SafeFetch[SSRF Protection\nBlocks RFC1918 & Cloud Metadata] Robots[Robots.txt & Rate Limiting] end
Engine <--> Security
# Initialize seed graph
proofgraph init
proofgraph search "AI Build Infra"
proofgraph verify "AI Build Infra develops MCP servers"
proofgraph trace "AI Build Infra develops MCP servers"
proofgraph audit "AI Build Infra"
proofgraph discoverability "AI Build Infra"
proofgraph export jsonld
Explore graph topologies, entities, claims, and audit diagnostics visually:
npm run dashboard
ProofGraph is built under strict open-source safety principles:
127.0.0.0/8, 10.0.0.0/8, 172.16.0.0/12, 192.168.0.0/16), link-local IPs, and Cloud Metadata (169.254.169.254, metadata.google.internal).ProofGraph is developed and maintained by [AI Build Infra](https://aibuildinfra.com/).