Context7
Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.
Description from the maker (original English)
Evidence-first entity and knowledge graph MCP server for AI agents to verify claims.
These three are never merged into one score: opinions, measurements and popularity are different things.
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
Pick the AI app you use, then copy the settings.
The maker has not listed install details in the registry. See the maker’s README below.
Runs on your computer; the tool list appears in your AI app after you install it.
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026
The registry name was verified with the GitHub account AI-BuildInfra, which owns the repo
We have not read this repo from GitHub yet
We have not read this repo from GitHub yet
This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
Runs on your computer; we can only read tool lists from internet endpoints
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
The registry does not list a required key, though some servers ask you to sign in on first use.
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
The GitHub repo last changed –, and the latest version (1.0.0) reached the registry 42 days ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code.
In the same category: Context7, Codebase Memory, Exa
Servers in the same category from other makers, sorted by stars.
Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.
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.
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
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/).