--- title: "awesome vs pdf-reader-mcp" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/sindresorhus-awesome-vs-sylphxai-pdf-reader-mcp" tools: ["sindresorhus-awesome", "sylphxai-pdf-reader-mcp"] --- # awesome vs pdf-reader-mcp *GraphCanon updated Jul 12, 2026* ## Verdict Pick awesome when license: awesome is CC0-1.0, pdf-reader-mcp is MIT; pick pdf-reader-mcp when license: pdf-reader-mcp is MIT, awesome is CC0-1.0. [awesome](https://github.com/sindresorhus/awesome) reports 484k GitHub stars, 36k forks, and 92 open issues, last pushed Jun 30, 2026. [pdf-reader-mcp](https://sylphxai.github.io/pdf-reader-mcp/) has 815 stars, 70 forks, and 9 open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [awesome's repository](https://github.com/sindresorhus/awesome) and [pdf-reader-mcp's repository](https://github.com/SylphxAI/pdf-reader-mcp). | | [awesome](/tools/sindresorhus-awesome.md) | [pdf-reader-mcp](/tools/sylphxai-pdf-reader-mcp.md) | | --- | --- | --- | | Tagline | 😎 Curated list of awesome topics including hardware resources | 📄 The PDF intelligence layer for AI agents — Agent Document Twin, evidence-first extraction, visual crops, OCR provenance, trust reports, and benchmark-gated releases. MCP server for Claude, Cursor, | | Stars | 484,026 | 815 | | Forks | 35,799 | 70 | | Open issues | 92 | 9 | | Language | - | TypeScript | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | CC0-1.0 | MIT | | Categories | LLM Frameworks | LLM Frameworks, AI Agents, Computer Vision | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome](/tools/sindresorhus-awesome.md) | [pdf-reader-mcp](/tools/sylphxai-pdf-reader-mcp.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 11d | 0d | | Open issues (now) | 92 | 9 | | Owner type | User | Organization | | Security scan | No lockfile | No MCP manifest | | Full report | [trust report](/tools/sindresorhus-awesome/trust.md) | [trust report](/tools/sylphxai-pdf-reader-mcp/trust.md) | ## Choose when ### Choose awesome if… - License: awesome is CC0-1.0, pdf-reader-mcp is MIT. - Tags unique to awesome: resources, awesome-list. - More GitHub stars (484k vs 815) - visibility, not fit. ### Choose pdf-reader-mcp if… - License: pdf-reader-mcp is MIT, awesome is CC0-1.0. - Tags unique to pdf-reader-mcp: llm-tool, document-processing, agent-document-twin, evidence-first. - Also covers AI Agents, Computer Vision. ## When NOT to use awesome - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## When NOT to use pdf-reader-mcp - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. ## Common questions ### What is the difference between awesome and pdf-reader-mcp? awesome: 😎 Curated list of awesome topics including hardware resources. pdf-reader-mcp: 📄 The PDF intelligence layer for AI agents — Agent Document Twin, evidence-first extraction, visual crops, OCR provenance, trust reports, and benchmark-gated releases. MCP server for Claude, Cursor, . See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome over pdf-reader-mcp? Choose awesome over pdf-reader-mcp when License: awesome is CC0-1.0, pdf-reader-mcp is MIT; Tags unique to awesome: resources, awesome-list; More GitHub stars (484k vs 815) - visibility, not fit. ### When should I choose pdf-reader-mcp over awesome? Choose pdf-reader-mcp over awesome when License: pdf-reader-mcp is MIT, awesome is CC0-1.0; Tags unique to pdf-reader-mcp: llm-tool, document-processing, agent-document-twin, evidence-first; Also covers AI Agents, Computer Vision. ### When should I avoid awesome? LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### When should I avoid pdf-reader-mcp? LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. ### Is awesome or pdf-reader-mcp more popular on GitHub? awesome has more GitHub stars (484,026 vs 815). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome and pdf-reader-mcp open source? Yes - both are open-source projects on GitHub (awesome: CC0-1.0, pdf-reader-mcp: MIT). ### Where can I find alternatives to awesome or pdf-reader-mcp? GraphCanon lists graph-backed alternatives at [awesome alternatives](/tools/sindresorhus-awesome/alternatives) and [pdf-reader-mcp alternatives](/tools/sylphxai-pdf-reader-mcp/alternatives) ([awesome markdown twin](/tools/sindresorhus-awesome/alternatives.md), [pdf-reader-mcp markdown twin](/tools/sylphxai-pdf-reader-mcp/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/sindresorhus-awesome-vs-sylphxai-pdf-reader-mcp.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awesome or pdf-reader-mcp? awesome: Active. pdf-reader-mcp: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for awesome and pdf-reader-mcp? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome trust report](/tools/sindresorhus-awesome/trust); [pdf-reader-mcp trust report](/tools/sylphxai-pdf-reader-mcp/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=sindresorhus-awesome`](/api/graphcanon/graph?tool=sindresorhus-awesome) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_