{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-sylphxai-pdf-reader-mcp","slug":"mcp-sylphxai-pdf-reader-mcp","name":"pdf-reader-mcp","type":"mcp","url":"https://github.com/SylphxAI/pdf-reader-mcp","page_url":"https://unfragile.ai/mcp-sylphxai-pdf-reader-mcp","categories":["mcp-servers","deployment-infra","testing-quality"],"tags":["ai-agent","ai-tools","document-processing","llm-tool","mcp","model-content-protocol","model-context-protocol","nodejs","parallel-processing","pdf","pdf-parse","pdf-parser","pdf-reader","performance","stdio","typescript"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-sylphxai-pdf-reader-mcp__cap_0","uri":"capability://data.processing.analysis.parallel.page.extraction.with.y.coordinate.ordering","name":"parallel-page-extraction-with-y-coordinate-ordering","description":"Extracts text content from PDF pages using Promise.all() for concurrent processing across multiple pages, then sorts extracted content by Y-coordinate (vertical position) to preserve document layout semantics. This approach achieves 5-10x speedup over sequential extraction while maintaining structural integrity of multi-column layouts and ordered content blocks. The implementation uses pdf-parse library with custom coordinate-based sorting in src/pdf/extractor.ts.","intents":["I need to extract text from a 100-page PDF in under 2 seconds for an AI agent to analyze","I want to preserve document layout (columns, sections, reading order) when extracting text for LLM context","I need to process multiple PDFs concurrently without blocking the MCP server"],"best_for":["AI agents processing multi-page documents where layout context matters (research papers, reports)","teams building document analysis pipelines that require fast turnaround on large PDFs","MCP client implementations needing non-blocking PDF operations"],"limitations":["Y-coordinate ordering assumes standard left-to-right, top-to-bottom layouts; may not preserve reading order in complex multi-column or rotated PDFs","Parallel processing is bounded by Node.js event loop; gains diminish beyond ~10 concurrent pages","Memory usage scales linearly with PDF size; large PDFs (>500MB) may cause heap pressure"],"requires":["Node.js >= 22.0.0","pdf-parse npm package (included in dependencies)","PDF file accessible via absolute or relative filesystem path"],"input_types":["file path (string)","page range specification (e.g., '1-5,10,15-20')","optional page limit parameter"],"output_types":["structured JSON with extracted text per page","per-page error warnings (non-blocking)","metadata about extraction (page count, extraction time)"],"categories":["data-processing-analysis","performance-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-sylphxai-pdf-reader-mcp__cap_1","uri":"capability://image.visual.embedded.image.extraction.with.base64.encoding","name":"embedded-image-extraction-with-base64-encoding","description":"Extracts embedded images from PDF documents and encodes them as base64-encoded PNG data URIs for direct embedding in LLM context windows. The implementation iterates through PDF page resources, identifies image objects, converts them to PNG format, and returns them as data URLs that Claude, Cursor, and other MCP clients can directly consume without additional file I/O. Handled in src/pdf/extractor.ts with image processing pipeline.","intents":["I want to pass PDF images directly to Claude for visual analysis without saving intermediate files","I need to extract charts, diagrams, and photos from PDFs for multimodal LLM processing","I want to include PDF images in the MCP context without requiring the client to handle file paths"],"best_for":["multimodal AI agents analyzing documents with visual content (reports, presentations, technical specs)","teams building document understanding pipelines that combine text and image analysis","Claude Desktop and Cursor users who want seamless image extraction without file management"],"limitations":["Base64 encoding increases payload size by ~33% compared to binary; large image-heavy PDFs may exceed context window limits","Image extraction only works for embedded images; scanned PDFs (image-only) require OCR (not provided)","PNG conversion may lose quality for JPEG or other compressed formats in source PDF","No image filtering or deduplication; duplicate images across pages are extracted multiple times"],"requires":["Node.js >= 22.0.0","pdf-parse library with image extraction support","sufficient memory for base64 encoding (scales with image count and resolution)"],"input_types":["file path (string)","page range specification (optional)","image format preference (currently PNG only)"],"output_types":["base64-encoded data URIs (data:image/png;base64,...)","image metadata (page number, dimensions, format)","per-image extraction errors (non-blocking)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-sylphxai-pdf-reader-mcp__cap_10","uri":"capability://safety.moderation.comprehensive.test.coverage.with.94.percent.coverage","name":"comprehensive-test-coverage-with-94-percent-coverage","description":"Includes extensive test suite with 94%+ code coverage using Jest or similar testing framework, covering PDF extraction, error handling, edge cases (empty PDFs, corrupted pages, large files), and MCP protocol compliance. Tests are organized by module (extractor, loader, parser, handlers) and include both unit tests and integration tests. The test suite validates correctness of parallel extraction, Y-coordinate ordering, error isolation, and response schema compliance.","intents":["I want to verify that pdf-reader-mcp handles edge cases (corrupted pages, large PDFs, empty documents) correctly","I need confidence that the server will not crash or produce incorrect results in production","I want to understand the expected behavior of the server through test examples"],"best_for":["teams deploying pdf-reader-mcp in production and needing reliability assurance","developers contributing to pdf-reader-mcp and needing test coverage metrics","organizations with strict quality requirements (94%+ coverage is production-grade)"],"limitations":["94% coverage does not guarantee all edge cases are tested; some complex interactions may not be covered","Tests are snapshot-based or assertion-based; changes to PDF parsing behavior may require test updates","Test suite requires test PDFs and fixtures; adding new test cases requires creating sample PDFs","Test execution time scales with PDF count and size; full test suite may take 30-60 seconds to run"],"requires":["Node.js >= 22.0.0","Jest or similar testing framework (included in devDependencies)","test fixtures (sample PDFs) included in repository"],"input_types":["test PDFs (various sizes, formats, edge cases)","test parameters (page ranges, options)"],"output_types":["test results (pass/fail)","coverage report (94%+ coverage)","performance benchmarks (extraction time, memory usage)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-sylphxai-pdf-reader-mcp__cap_11","uri":"capability://automation.workflow.docker.deployment.with.containerized.mcp.server","name":"docker-deployment-with-containerized-mcp-server","description":"Provides Docker configuration (Dockerfile, docker-compose.yml) for containerized deployment of the MCP server, enabling easy integration into orchestrated environments (Kubernetes, Docker Compose). The Docker image includes Node.js runtime, pdf-reader-mcp dependencies, and startup scripts. Deployment documentation covers image building, container configuration, and integration with MCP clients via stdio transport within containers.","intents":["I want to deploy pdf-reader-mcp as a containerized service in Kubernetes or Docker Compose","I need to isolate PDF processing in a separate container for security and resource management","I want to scale PDF processing by running multiple container instances"],"best_for":["teams deploying AI agents in containerized environments (Kubernetes, Docker Compose)","organizations with container-based infrastructure and deployment pipelines","multi-tenant systems where PDF processing should be isolated per tenant"],"limitations":["Docker image adds ~500MB overhead compared to native Node.js installation","stdio transport within containers requires careful process management; container must not daemonize","No built-in health checks or liveness probes; orchestration system must implement monitoring","Scaling to multiple containers requires external coordination (load balancing, request routing) not provided by the server"],"requires":["Docker >= 20.10 or Docker Compose >= 2.0","container registry for image storage (Docker Hub, ECR, etc.)","MCP client configured to connect to container via stdio or network transport"],"input_types":["Dockerfile configuration","docker-compose.yml service definition","environment variables for runtime configuration"],"output_types":["Docker image (pdf-reader-mcp:latest)","running container with MCP server process","container logs (stdout/stderr)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-sylphxai-pdf-reader-mcp__cap_12","uri":"capability://automation.workflow.npm.package.distribution.with.automated.ci.cd","name":"npm-package-distribution-with-automated-ci-cd","description":"Distributes pdf-reader-mcp as an npm package with automated CI/CD pipeline (GitHub Actions) that runs tests, builds the package, and publishes to npm registry on release. The package.json defines dependencies, build scripts, and entry points. CI/CD pipeline validates code quality, runs test suite, and publishes new versions automatically. This enables easy installation via 'npm install pdf-reader-mcp' and ensures consistent builds across environments.","intents":["I want to install pdf-reader-mcp via npm without cloning the repository","I need to ensure I'm using a stable, tested version of the server","I want to receive updates automatically when new versions are published"],"best_for":["Node.js developers integrating pdf-reader-mcp into projects via npm","teams using npm-based dependency management and automated updates","open-source projects that want to distribute pdf-reader-mcp as a dependency"],"limitations":["npm package includes compiled JavaScript only; source TypeScript is not included (reduces package size but limits debugging)","CI/CD pipeline is GitHub Actions-specific; not portable to other CI systems without modification","npm package version must be manually bumped; no automatic semantic versioning based on commits","npm registry requires authentication for private packages; public packages are accessible to all"],"requires":["npm >= 8.0 or yarn >= 3.0","Node.js >= 22.0.0 (as specified in package.json engines field)","npm account for publishing (if maintaining the package)"],"input_types":["package.json with dependencies and build scripts","GitHub Actions workflow files (.github/workflows/*.yml)"],"output_types":["npm package tarball (pdf-reader-mcp-X.Y.Z.tgz)","published package on npm registry","CI/CD build logs and test results"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-sylphxai-pdf-reader-mcp__cap_2","uri":"capability://data.processing.analysis.flexible.page.range.parsing.with.cross.platform.path.support","name":"flexible-page-range-parsing-with-cross-platform-path-support","description":"Parses complex page range specifications (e.g., '1-5,10,15-20') into discrete page numbers, and normalizes file paths across Windows/Unix/relative/absolute formats using path resolution logic in src/pdf/parser.ts. The implementation validates range syntax, expands ranges into individual pages, and resolves paths relative to the MCP server's working directory, handling edge cases like negative indices and out-of-bounds ranges gracefully.","intents":["I want to extract specific pages from a PDF without loading the entire document into memory","I need to specify page ranges using human-readable syntax (e.g., '1-10,20,25-30') in my MCP requests","I'm using Windows paths and Unix paths interchangeably and need the server to handle both transparently"],"best_for":["developers building MCP clients that need to support flexible page selection UIs","cross-platform teams using pdf-reader-mcp on Windows, macOS, and Linux","agents processing large PDFs where extracting specific sections is more efficient than full document extraction"],"limitations":["Range parsing does not support reverse ranges (e.g., '20-1') or negative indices; must be ascending","Path resolution is relative to MCP server's working directory; absolute paths are required for files outside that directory","No validation of page existence until extraction time; invalid ranges (e.g., '1-1000' on a 50-page PDF) fail during extraction, not parsing","Symlinks and relative path traversal (../) are resolved but not validated for security; requires careful deployment"],"requires":["Node.js >= 22.0.0","valid page range string or null (for all pages)","file path accessible from MCP server's working directory"],"input_types":["page range string (e.g., '1-5,10,15-20')","file path (absolute or relative)","optional page limit override"],"output_types":["array of discrete page numbers","resolved absolute file path","validation errors (range syntax, path resolution)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-sylphxai-pdf-reader-mcp__cap_3","uri":"capability://safety.moderation.per.page.error.isolation.with.graceful.degradation","name":"per-page-error-isolation-with-graceful-degradation","description":"Implements error handling that isolates failures to individual pages using Promise.allSettled() internally, allowing extraction to continue on remaining pages even if one page fails to parse. Failed pages generate warning objects in the response (not exceptions) that include error details, page number, and fallback content (if available). This pattern is implemented in src/handlers/readPdf.ts and prevents single malformed pages from blocking the entire PDF extraction.","intents":["I need to extract text from a PDF with some corrupted pages without the entire operation failing","I want detailed error information about which pages failed and why, without losing data from successful pages","I'm building an agent that processes PDFs from untrusted sources and needs robust error recovery"],"best_for":["production systems processing PDFs from diverse sources (user uploads, web scraping, legacy documents)","AI agents that need to continue processing despite partial failures","teams building document pipelines where visibility into per-page failures is critical for debugging"],"limitations":["Error isolation adds ~50-100ms overhead per page due to Promise.allSettled() wrapping","Failed pages return null or empty content; no automatic fallback to OCR or alternative extraction methods","Error messages are generic (e.g., 'failed to extract text') without detailed stack traces; debugging requires server logs","No retry logic; failed pages are not automatically re-attempted with different parsing strategies"],"requires":["Node.js >= 22.0.0","Promise.allSettled() support (Node.js 12.9+)","error handling middleware in MCP client to interpret warning objects"],"input_types":["file path (string)","page range specification"],"output_types":["successful page extractions (text, images, metadata)","warning objects for failed pages (error code, message, page number)","partial results (some pages succeeded, some failed)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-sylphxai-pdf-reader-mcp__cap_4","uri":"capability://tool.use.integration.stdio.based.mcp.server.with.json.rpc.protocol","name":"stdio-based-mcp-server-with-json-rpc-protocol","description":"Implements a Model Context Protocol (MCP) server using Node.js stdio transport, communicating with MCP clients via JSON-RPC 2.0 messages over standard input/output. The server exposes a single 'read_pdf' tool with structured input schema and response format, handling client requests asynchronously and returning results as JSON. Implemented in src/index.ts with MCP SDK integration for protocol compliance and automatic schema validation.","intents":["I want to integrate PDF reading into Claude Desktop, Cursor, or Cline without building a custom HTTP server","I need to expose PDF capabilities to any MCP-compatible client using standard protocol","I want to avoid managing API keys, authentication, and network infrastructure for PDF processing"],"best_for":["Claude Desktop users and Cursor/Cline developers extending AI assistants with PDF capabilities","teams building MCP servers and needing a reference implementation for tool exposure","organizations deploying AI agents that need local, offline PDF processing without cloud dependencies"],"limitations":["stdio transport is synchronous at the OS level; large responses (>10MB) may cause buffering delays","No built-in authentication or authorization; relies on process-level isolation (MCP client runs as same user)","Single tool ('read_pdf') limits extensibility; adding new capabilities requires server restart","Error responses are JSON-RPC error objects; clients must parse error codes to distinguish between invalid input, file not found, and extraction failures"],"requires":["Node.js >= 22.0.0","MCP SDK (included in package.json dependencies)","MCP client configured to launch this server as a subprocess"],"input_types":["JSON-RPC 2.0 request objects with 'read_pdf' tool invocation","structured tool parameters (file path, page range, options)"],"output_types":["JSON-RPC 2.0 response objects with extracted content","JSON-RPC 2.0 error objects for failures","streaming responses (if MCP client supports it)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-sylphxai-pdf-reader-mcp__cap_5","uri":"capability://data.processing.analysis.pdf.metadata.extraction.with.document.properties","name":"pdf-metadata-extraction-with-document-properties","description":"Extracts PDF metadata including author, title, creation date, modification date, and other document properties from PDF headers without parsing page content. This is implemented in src/pdf/extractor.ts using pdf-parse's metadata API, returning structured metadata objects that provide document-level context to AI agents. Metadata extraction is fast (no page parsing required) and can be used to filter or prioritize PDFs before full content extraction.","intents":["I want to quickly check PDF metadata (title, author, creation date) before deciding whether to extract full content","I need to organize or filter PDFs by metadata properties in an agent workflow","I want to include document context (author, title) in the LLM prompt without extracting all page content"],"best_for":["document management systems that need fast metadata indexing","AI agents filtering PDFs by metadata before processing","teams building document discovery features that rely on title, author, and date information"],"limitations":["Metadata extraction depends on PDF creator properly populating metadata fields; many PDFs have missing or incomplete metadata","Dates are returned in PDF date format (D:YYYYMMDDHHmmSS); clients must parse and normalize for consistent handling","No support for custom metadata fields or XMP metadata; only standard PDF properties are extracted","Metadata extraction still requires opening the PDF file; no performance benefit over full extraction for single-PDF operations"],"requires":["Node.js >= 22.0.0","pdf-parse library with metadata support","PDF file accessible via filesystem path"],"input_types":["file path (string)"],"output_types":["JSON object with metadata fields (title, author, creator, creationDate, modDate, etc.)","null or empty object if metadata is not present in PDF"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-sylphxai-pdf-reader-mcp__cap_6","uri":"capability://automation.workflow.batch.pdf.processing.with.concurrency.limits","name":"batch-pdf-processing-with-concurrency-limits","description":"Processes multiple PDF files concurrently with a configurable concurrency limit (default: 3 concurrent operations) to prevent resource exhaustion while maintaining parallelism. The implementation uses a queue-based approach in src/handlers/readPdf.ts that limits the number of in-flight Promise operations, allowing agents to submit multiple PDF extraction requests without overwhelming the server. Concurrency is managed via Promise.all() with a sliding window of active operations.","intents":["I want to extract content from 10 PDFs in parallel without the server running out of memory","I need to process multiple documents for an agent workflow while maintaining predictable resource usage","I'm building a batch document processing pipeline that should not exceed a specific number of concurrent operations"],"best_for":["agents processing document batches (e.g., analyzing multiple research papers, processing invoice batches)","teams building document pipelines that need predictable resource consumption","production deployments where uncontrolled concurrency could cause memory or CPU spikes"],"limitations":["Concurrency limit is global across all clients; no per-client rate limiting or quota management","Queue-based approach adds latency for requests beyond the concurrency limit; requests wait in queue before processing","No priority queue; requests are processed in FIFO order regardless of file size or complexity","Concurrency limit is hardcoded to 3; changing it requires server restart (not dynamically configurable)"],"requires":["Node.js >= 22.0.0","sufficient memory for 3 concurrent PDF extractions (typically 50-200MB depending on PDF size)","MCP client capable of sending multiple requests"],"input_types":["multiple file paths (array or sequential requests)","page range specifications per file"],"output_types":["array of extraction results (one per PDF)","per-PDF error objects if extraction fails","timing information (queue wait time, extraction time)"],"categories":["automation-workflow","performance-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-sylphxai-pdf-reader-mcp__cap_7","uri":"capability://data.processing.analysis.fast.page.counting.without.content.loading","name":"fast-page-counting-without-content-loading","description":"Counts total pages in a PDF by reading only the document structure (PDF catalog and page tree) without loading or parsing page content. Implemented in src/pdf/loader.ts using pdf-parse's page enumeration API, this operation completes in milliseconds even for large PDFs. Page count is returned as metadata and can be used by agents to validate page range requests or estimate extraction time before processing.","intents":["I want to quickly check how many pages a PDF has before deciding on a page range to extract","I need to validate that a requested page range (e.g., '1-100') is valid for a specific PDF","I want to estimate extraction time based on page count before submitting a full extraction request"],"best_for":["agents that need to validate page ranges before extraction","document management systems that need fast page counting for indexing","interactive tools where users specify page ranges and need immediate feedback on validity"],"limitations":["Page count is returned as metadata; no information about page sizes, orientation, or content density","Encrypted PDFs may not expose page count without decryption; behavior depends on PDF security settings","Page count is approximate for some PDFs with complex page trees; edge cases may return incorrect counts"],"requires":["Node.js >= 22.0.0","pdf-parse library with page enumeration support","PDF file accessible via filesystem path"],"input_types":["file path (string)"],"output_types":["integer page count","error object if page count cannot be determined"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-sylphxai-pdf-reader-mcp__cap_8","uri":"capability://tool.use.integration.structured.response.formatting.with.schema.validation","name":"structured-response-formatting-with-schema-validation","description":"Formats all PDF extraction results into a standardized JSON structure with schema validation, ensuring consistent response format across all tool invocations. The response schema includes sections for extracted text, images, metadata, errors, and extraction statistics. Implemented in src/handlers/readPdf.ts with TypeScript type definitions and JSON Schema validation, this ensures MCP clients can reliably parse responses and handle errors consistently.","intents":["I want to parse PDF extraction responses with a predictable, well-defined structure","I need to distinguish between successful extractions, partial failures, and complete failures in a structured way","I want to include extraction metadata (time, page count, error count) in the response for monitoring and debugging"],"best_for":["MCP client developers building UI or parsing logic around PDF extraction","teams building document processing pipelines that need consistent response handling","monitoring and observability systems that track extraction success rates and performance"],"limitations":["Schema is fixed; no support for custom response fields or extensions without modifying the server","Large responses (>10MB) may exceed MCP client buffer limits; no streaming or chunked response support","Schema validation adds ~10-20ms overhead per response; not optimized for latency-critical applications","Error details are limited to error code and message; no stack traces or detailed diagnostic information"],"requires":["Node.js >= 22.0.0","MCP client capable of parsing JSON responses","TypeScript type definitions (optional, for client-side type safety)"],"input_types":["PDF extraction results (text, images, metadata, errors)"],"output_types":["JSON object with standardized structure","includes: extracted_text, images, metadata, errors, statistics","all fields present even if empty (for consistent parsing)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-sylphxai-pdf-reader-mcp__cap_9","uri":"capability://code.generation.editing.typescript.based.implementation.with.type.safety","name":"typescript-based-implementation-with-type-safety","description":"Entire codebase is implemented in TypeScript with strict type checking enabled, providing compile-time type safety for all PDF operations, handler functions, and MCP protocol interactions. Type definitions are exported for client-side use, enabling MCP clients to import and use the same types for request/response validation. The build system (src/build.ts or similar) compiles TypeScript to JavaScript for runtime execution.","intents":["I want to build an MCP client with type-safe PDF extraction requests and responses","I need compile-time validation of PDF tool parameters before sending requests","I want to avoid runtime type errors in my document processing pipeline"],"best_for":["TypeScript-based MCP clients and agents","teams building type-safe document processing pipelines","developers who value compile-time error detection over runtime debugging"],"limitations":["TypeScript compilation adds ~2-5 seconds to build time; not suitable for rapid prototyping","Type definitions are only available to TypeScript clients; JavaScript clients must rely on runtime validation","Strict type checking can be verbose for complex PDF operations; may require explicit type assertions","Type definitions are not auto-generated from MCP schema; manual synchronization required when schema changes"],"requires":["Node.js >= 22.0.0","TypeScript compiler (tsc) for building from source","TypeScript 4.5+ for client-side type checking"],"input_types":["TypeScript source files (.ts)","JSON schema definitions"],"output_types":["compiled JavaScript (.js)","TypeScript type definitions (.d.ts)","source maps for debugging"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":49,"verified":false,"data_access_risk":"high","permissions":["Node.js >= 22.0.0","pdf-parse npm package (included in dependencies)","PDF file accessible via absolute or relative filesystem path","pdf-parse library with image extraction support","sufficient memory for base64 encoding (scales with image count and resolution)","Jest or similar testing framework (included in devDependencies)","test fixtures (sample PDFs) included in repository","Docker >= 20.10 or Docker Compose >= 2.0","container registry for image storage (Docker Hub, ECR, etc.)","MCP client configured to connect to container via stdio or network transport"],"failure_modes":["Y-coordinate ordering assumes standard left-to-right, top-to-bottom layouts; may not preserve reading order in complex multi-column or rotated PDFs","Parallel processing is bounded by Node.js event loop; gains diminish beyond ~10 concurrent pages","Memory usage scales linearly with PDF size; large PDFs (>500MB) may cause heap pressure","Base64 encoding increases payload size by ~33% compared to binary; large image-heavy PDFs may exceed context window limits","Image extraction only works for embedded images; scanned PDFs (image-only) require OCR (not provided)","PNG conversion may lose quality for JPEG or other compressed formats in source PDF","No image filtering or deduplication; duplicate images across pages are extracted multiple times","94% coverage does not guarantee all edge cases are tested; some complex interactions may not be covered","Tests are snapshot-based or assertion-based; changes to PDF parsing behavior may require test updates","Test suite requires test PDFs and fixtures; adding new test cases requires creating sample PDFs","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.3817897162843499,"quality":0.5,"ecosystem":0.8,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.065Z","last_scraped_at":"2026-05-03T14:23:34.856Z","last_commit":"2026-04-20T07:21:59Z"},"community":{"stars":680,"forks":65,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-sylphxai-pdf-reader-mcp","compare_url":"https://unfragile.ai/compare?artifact=mcp-sylphxai-pdf-reader-mcp"}},"signature":"ygfQ6hiNdmolppDo3kkYfyvwr26KN5n+1A+BBW71BpsiGLKsOhkmAyNDmDOmSYpRpE8XbldXQHhrxfOat7ePBA==","signedAt":"2026-06-21T01:49:11.452Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-sylphxai-pdf-reader-mcp","artifact":"https://unfragile.ai/mcp-sylphxai-pdf-reader-mcp","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-sylphxai-pdf-reader-mcp","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}