{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-reexpress","slug":"reexpress","name":"Reexpress","type":"mcp","url":"https://github.com/ReexpressAI/reexpress_mcp_server","page_url":"https://unfragile.ai/reexpress","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-reexpress__cap_0","uri":"capability://safety.moderation.similarity.distance.magnitude.sdm.statistical.verification.with.calibrated.confidence.estimation","name":"similarity-distance-magnitude (sdm) statistical verification with calibrated confidence estimation","description":"Implements a trained SDM estimator that compares LLM responses against a database of 120,159+ verified examples from the OpenVerification dataset to produce statistically calibrated confidence scores. The estimator extracts similarity, distance, and magnitude features from response pairs and maps them to high-reliability regions (≥90%, ≤89%, <60%, or Out-of-Distribution) using offline calibration at α=0.9, enabling principled confidence estimation without ground-truth labels.","intents":["Determine whether an LLM response is correct with statistical confidence rather than prompt-based self-rating","Distinguish high-quality responses from hallucinations in automated pipelines","Identify when an LLM should seek additional resources or clarification based on confidence thresholds","Filter tool-calling LLM outputs by reliability before downstream processing"],"best_for":["Teams deploying tool-calling LLMs (Claude Opus/Sonnet, GPT-4.5) in production workflows","Data science teams needing reliable confidence estimates for model outputs","Software development teams automating code generation with verification gates"],"limitations":["Requires pre-trained SDM model; out-of-distribution responses may have lower calibration accuracy","Confidence estimates are calibrated to the OpenVerification1 dataset distribution; domain shift reduces reliability","Ensemble verification adds latency (calls to GPT-5.2, Gemini-3-Pro, and Granite-3.3-8B sequentially)","High-reliability regions are discrete buckets (≥90%, ≤89%, <60%, OOD); no continuous confidence scores"],"requires":["MCP-compatible LLM client (Claude Opus 4.5 or Sonnet 4.5 recommended)","API keys for OpenAI/Azure (GPT-5.2), Google (Gemini-3-Pro), and local Granite-3.3-8B deployment","Python 3.9+ runtime for SDM estimator","Pre-trained SDM model weights (included in distribution)"],"input_types":["LLM response text","Original query or context","Verification outputs from ensemble models"],"output_types":["Calibrated confidence level (categorical: high/medium/low/out-of-distribution)","SDM feature vector (similarity, distance, magnitude scores)","Visualization of response reliability in high-reliability region space"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-reexpress__cap_1","uri":"capability://tool.use.integration.multi.model.ensemble.verification.with.independent.response.aggregation","name":"multi-model ensemble verification with independent response aggregation","description":"Automatically routes each LLM response to three independent verification models (GPT-5.2 via Azure/OpenAI, Gemini-3-Pro via Google, and local Granite-3.3-8B) in parallel or sequential mode, aggregates their outputs, and feeds the ensemble results to the SDM estimator. This architecture isolates verification from the primary LLM, reducing bias and enabling cross-model consistency checks.","intents":["Verify LLM responses using independent models to reduce single-model bias","Detect hallucinations by comparing primary response against ensemble consensus","Aggregate verification signals across multiple model families (proprietary and open-source)","Enable fallback verification if one model API is unavailable"],"best_for":["Teams requiring high-confidence verification in safety-critical workflows (medical, legal, financial)","Organizations with multi-cloud or hybrid deployments (Azure, Google Cloud, on-premise)","Workflows where model diversity improves detection of systematic errors"],"limitations":["Ensemble verification adds cumulative latency; sequential calls can exceed 5-10 seconds per response","Requires active API subscriptions and quota management for three separate model providers","Ensemble aggregation logic is fixed (no custom weighting per model); all models treated equally","Local Granite-3.3-8B requires GPU resources (~8GB VRAM minimum); CPU inference is prohibitively slow"],"requires":["Azure OpenAI API key with GPT-5.2 access","Google Cloud API key with Gemini-3-Pro access","Local deployment of Granite-3.3-8B (8GB+ GPU VRAM or quantized CPU variant)","Network connectivity to Azure and Google Cloud endpoints","MCP server runtime with concurrent request handling"],"input_types":["LLM response text","Query context or task description","Optional: response metadata (model, temperature, tokens used)"],"output_types":["Ensemble verification results (per-model outputs)","Aggregated consensus signal","Disagreement metrics (if models diverge)"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-reexpress__cap_10","uri":"capability://tool.use.integration.llm.integration.layer.with.multi.provider.api.abstraction","name":"llm integration layer with multi-provider api abstraction","description":"Implements a unified API abstraction for calling three LLM providers (OpenAI/Azure GPT-5.2, Google Gemini-3-Pro, local Granite-3.3-8B) with consistent request/response handling, error recovery, and rate limiting. The layer handles provider-specific authentication, request formatting, and response parsing, allowing the SDM estimator to treat all three models as interchangeable verification backends.","intents":["Call multiple LLM providers for ensemble verification without provider-specific code","Handle API errors and rate limits gracefully with retry logic","Switch between providers or add new providers without changing verification logic","Monitor API usage and costs across multiple providers"],"best_for":["Teams using multiple LLM providers and wanting unified integration","Workflows requiring fallback verification if one provider is unavailable","Organizations managing costs across multiple LLM APIs"],"limitations":["Abstraction adds ~50-100ms latency per API call (request serialization, response parsing)","Provider-specific features (e.g., vision, function calling) are not exposed through abstraction","Rate limiting is per-provider; no global rate limiting across all three providers","Error handling is generic; provider-specific errors may be lost"],"requires":["API keys for OpenAI/Azure, Google Cloud, and local Granite deployment","Network connectivity to Azure and Google Cloud endpoints","Python 3.9+ with requests library"],"input_types":["Verification request (response text, query context)","Provider selection (GPT-5.2, Gemini-3-Pro, or Granite-3.3-8B)"],"output_types":["Verification response text","Provider metadata (model name, tokens used, latency)","Error information if API call fails"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-reexpress__cap_11","uri":"capability://automation.workflow.configuration.and.constants.system.with.environment.based.customization","name":"configuration and constants system with environment-based customization","description":"Implements a centralized configuration system that manages SDM estimator hyperparameters, file access control rules, LLM provider credentials, and calibration thresholds. Configuration is loaded from environment variables, YAML files, or Python constants, enabling deployment-specific customization without code changes. Includes validation and default values for all configuration options.","intents":["Customize SDM estimator behavior (confidence thresholds, feature weights) per deployment","Manage LLM provider credentials securely using environment variables","Define file access control rules for sandboxing","Override default calibration thresholds for domain-specific applications"],"best_for":["Teams deploying Reexpress across multiple environments (dev, staging, production)","Organizations requiring environment-specific configuration without code changes","Workflows with strict security requirements (credential management, sandboxing)"],"limitations":["Configuration validation is basic; no schema validation or type checking","No hot-reload of configuration; changes require server restart","Environment variables can be verbose for complex configurations","No audit trail of configuration changes"],"requires":["Environment variables or YAML configuration file","Knowledge of configuration option names and valid values","Ability to restart MCP server to apply configuration changes"],"input_types":["Configuration file (YAML) or environment variables","Configuration option names and values"],"output_types":["Validated configuration object","Configuration validation errors (if any)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-reexpress__cap_12","uri":"capability://automation.workflow.data.persistence.and.model.artifact.management.with.versioning","name":"data persistence and model artifact management with versioning","description":"Implements storage and retrieval of trained SDM models, calibration curves, training datasets, and feedback buffers using a file-based or database backend. Includes versioning of model artifacts, checkpointing during training, and recovery from incomplete training runs. Supports both local file storage and cloud storage backends (S3, GCS).","intents":["Save and load trained SDM models across server restarts","Version model artifacts to track improvements and enable rollback","Checkpoint training progress to enable resumable training","Persist feedback buffer to enable incremental model updates"],"best_for":["Teams deploying Reexpress in production and needing model persistence","Workflows with long training runs requiring checkpointing","Organizations tracking model versions for compliance or auditing"],"limitations":["File-based storage is not suitable for distributed deployments; requires shared filesystem","No built-in model compression; trained models can be large (>500MB)","Versioning is manual; no automatic version management or garbage collection","Feedback buffer persistence requires explicit flush; updates are lost on server crash"],"requires":["Local filesystem or cloud storage (S3, GCS) for model artifacts","Sufficient disk space for model checkpoints (~500MB per checkpoint)","Optional: database for metadata (model versions, training dates)"],"input_types":["Trained SDM model weights","Calibration curves and lookup tables","Training dataset and feedback buffer"],"output_types":["Saved model artifacts (pickle, HDF5, or similar)","Version metadata (training date, dataset, hyperparameters)","Checkpoint recovery information"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-reexpress__cap_13","uri":"capability://planning.reasoning.reasoning.with.sdm.verification.for.multi.step.task.decomposition","name":"reasoning with sdm verification for multi-step task decomposition","description":"Enables LLM clients to use SDM verification as a reasoning tool within multi-step task decomposition workflows. The LLM can call reexpress_verify to check intermediate results, adjust reasoning based on confidence levels, and request re-verification if confidence is low. This creates a feedback loop where verification guides task decomposition and error recovery.","intents":["Verify intermediate results in multi-step reasoning tasks (e.g., code generation, math problems)","Adjust reasoning strategy based on confidence levels (e.g., try different approach if confidence <60%)","Implement automatic error recovery by re-attempting tasks with low confidence","Build confidence-aware task decomposition strategies"],"best_for":["Complex reasoning tasks requiring verification of intermediate steps","Workflows where confidence-based error recovery improves success rates","Teams building agentic systems with self-correction capabilities"],"limitations":["Verification adds latency to reasoning loops; multi-step tasks may be slow","Confidence-based error recovery can create infinite loops if not bounded","No built-in strategy for choosing when to re-verify vs. accept low confidence","Verification is stateless; no memory of previous verification decisions across steps"],"requires":["MCP-compatible LLM client with tool-calling and reasoning capabilities","Task decomposition logic that can adjust based on confidence feedback","Mechanism to bound error recovery (max retries, timeout)"],"input_types":["Intermediate task result (text, code, structured data)","Task context and reasoning state"],"output_types":["Confidence level for intermediate result","Recommendation for next step (accept, re-verify, re-attempt)"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-reexpress__cap_2","uri":"capability://automation.workflow.dynamic.model.updates.with.feedback.incorporation.reexpress.add.true.reexpress.add.false.reexpress.add.ood","name":"dynamic model updates with feedback incorporation (reexpress_add_true, reexpress_add_false, reexpress_add_ood)","description":"Provides three MCP tools that allow users to incrementally update the SDM estimator with feedback without full retraining: reexpress_add_true marks a response as correct, reexpress_add_false marks it as incorrect, and reexpress_add_ood flags it as out-of-distribution. These tools update an in-memory feedback buffer that can be periodically flushed to the training dataset, enabling the estimator to adapt to domain-specific patterns over time.","intents":["Correct SDM estimator predictions when it misclassifies a response","Flag out-of-distribution responses to improve calibration in new domains","Build domain-specific training data incrementally without manual annotation","Adapt the estimator to new task types or response formats without retraining from scratch"],"best_for":["Teams deploying Reexpress in production and encountering domain-specific patterns","Workflows where feedback is available post-deployment (e.g., user corrections, downstream validation)","Organizations wanting to improve calibration over time without ML engineering overhead"],"limitations":["Feedback updates are applied to in-memory buffer only; full retraining required to persist changes to the SDM model","No versioning of feedback; overwrites or conflicts are not tracked","Feedback buffer has no persistence layer; updates are lost if the MCP server restarts","No active learning strategy; feedback is applied uniformly without prioritization of high-impact examples"],"requires":["MCP client with tool-calling support","Access to reexpress_add_true, reexpress_add_false, reexpress_add_ood tools","Mechanism to collect feedback (user input, downstream validation, or automated checks)","Periodic retraining pipeline to flush feedback buffer to persistent storage"],"input_types":["Response text","Query context","User feedback label (true/false/ood)"],"output_types":["Confirmation of feedback acceptance","Updated SDM estimator state (in-memory)","Feedback buffer statistics (count of true/false/ood examples)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-reexpress__cap_3","uri":"capability://safety.moderation.high.reliability.region.calibration.with.discrete.confidence.buckets","name":"high-reliability region calibration with discrete confidence buckets","description":"Implements offline calibration of the SDM estimator using empirical calibration curves at α=0.9, mapping SDM feature vectors to discrete confidence regions: ≥90% (high confidence), ≤89% (medium confidence), <60% (low confidence), or Out-of-Distribution. Calibration is performed once during training and stored as lookup tables or decision boundaries, enabling fast inference without per-query calibration overhead.","intents":["Assign discrete confidence levels to responses for downstream filtering or routing","Identify responses that fall outside the training distribution (out-of-distribution detection)","Set confidence thresholds for automated decision-making (e.g., require human review if confidence <60%)","Understand the statistical reliability of confidence estimates via calibration metrics"],"best_for":["Teams needing discrete confidence buckets for rule-based filtering (e.g., 'approve if ≥90%, review if <60%')","Workflows with strict reliability requirements (medical, legal, compliance)","Systems where continuous confidence scores are harder to interpret than categorical labels"],"limitations":["Discrete buckets lose granularity; responses near bucket boundaries may be misclassified","Calibration is fixed at α=0.9; changing confidence thresholds requires recalibration","Out-of-distribution detection is binary; no gradient of 'how far out' a response is","Calibration curves are dataset-specific; domain shift reduces calibration accuracy"],"requires":["Pre-trained SDM estimator with calibration curves computed on OpenVerification1 dataset","Calibration lookup tables or decision boundary parameters (included in model distribution)","Knowledge of α=0.9 calibration target for interpreting confidence levels"],"input_types":["SDM feature vector (similarity, distance, magnitude scores)"],"output_types":["Discrete confidence level (categorical: ≥90%, ≤89%, <60%, OOD)","Calibration confidence interval (if available)","Out-of-distribution flag"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-reexpress__cap_4","uri":"capability://tool.use.integration.mcp.server.implementation.with.file.access.control.and.tool.registry","name":"mcp server implementation with file access control and tool registry","description":"Implements a Model Context Protocol (MCP) server that exposes SDM verification and feedback tools as callable functions for LLM clients. The server includes a file access control system that restricts which files or directories the LLM can access during verification, a dynamic tool registry for managing available tools, and request/response serialization compatible with Claude Opus/Sonnet and other MCP-compliant clients.","intents":["Integrate SDM verification into Claude or other MCP-compatible LLM clients as callable tools","Restrict LLM access to sensitive files or directories during verification","Dynamically enable/disable verification tools based on configuration or runtime state","Serialize verification requests and responses in MCP-compatible format"],"best_for":["Teams using Claude Opus/Sonnet with tool-calling capabilities","Workflows requiring sandboxed file access during LLM verification","Organizations integrating Reexpress into existing MCP-based LLM applications"],"limitations":["File access control is path-based; no fine-grained permission model (e.g., read-only vs. read-write)","MCP server is single-threaded by default; concurrent verification requests may queue","Tool registry is static; adding new tools requires server restart","No built-in authentication; assumes trusted LLM client environment"],"requires":["MCP-compatible LLM client (Claude Opus 4.5 or Sonnet 4.5 recommended)","Python 3.9+ runtime for MCP server","MCP server configuration file specifying file access rules","Network connectivity between LLM client and MCP server (local or remote)"],"input_types":["MCP tool call requests (JSON-RPC format)","Tool parameters (response text, query context, feedback labels)"],"output_types":["MCP tool call responses (JSON-RPC format)","Verification results (confidence level, SDM features)","Feedback acknowledgments"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-reexpress__cap_5","uri":"capability://data.processing.analysis.training.pipeline.with.iterative.shuffling.and.data.preparation","name":"training pipeline with iterative shuffling and data preparation","description":"Implements a multi-stage training pipeline that prepares data from the OpenVerification1 dataset, applies iterative shuffling to reduce overfitting, trains the SDM estimator on similarity/distance/magnitude features, and evaluates performance using calibration metrics. The pipeline includes data validation, feature engineering, and hyperparameter tuning stages, with checkpointing to enable resumable training.","intents":["Train a new SDM estimator on custom datasets or domain-specific data","Prepare and validate training data from raw response pairs","Tune SDM hyperparameters for optimal calibration on a specific domain","Evaluate estimator performance using calibration curves and reliability metrics"],"best_for":["Teams deploying Reexpress to new domains and needing to retrain the estimator","Organizations with proprietary datasets wanting to build domain-specific confidence models","ML engineers optimizing SDM performance for specific task types"],"limitations":["Training requires 120K+ labeled examples; smaller datasets may overfit","Iterative shuffling adds training time; full pipeline can take hours on CPU","Feature engineering is fixed (similarity, distance, magnitude); no custom feature support","Calibration is computed once; no online calibration during inference"],"requires":["Python 3.9+ with scikit-learn, numpy, pandas","Training dataset with response pairs and correctness labels","GPU recommended for faster training (CUDA 11.8+)","Disk space for checkpoints and model artifacts (~500MB per checkpoint)"],"input_types":["CSV or JSON files with response pairs (query, response, label)","Training hyperparameters (learning rate, batch size, epochs)","Validation split ratio"],"output_types":["Trained SDM model weights","Calibration curves and lookup tables","Evaluation metrics (accuracy, calibration error, AUC-ROC)","Training logs and checkpoints"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-reexpress__cap_6","uri":"capability://data.processing.analysis.calibration.process.with.empirical.curve.fitting.and.high.reliability.region.mapping","name":"calibration process with empirical curve fitting and high-reliability region mapping","description":"Implements offline calibration by fitting empirical curves to the relationship between SDM features and response correctness, then mapping feature space to discrete high-reliability regions (≥90%, ≤89%, <60%, OOD). Calibration uses the training dataset to compute confidence intervals and decision boundaries, stored as lookup tables or parametric models for fast inference.","intents":["Compute calibration curves that map SDM features to confidence levels","Identify decision boundaries between confidence regions","Detect out-of-distribution responses by analyzing feature distributions","Validate calibration quality using held-out test sets"],"best_for":["Teams validating SDM estimator calibration on new domains","Workflows requiring transparent, interpretable confidence estimation","Organizations needing to audit confidence level reliability"],"limitations":["Calibration curves are dataset-specific; domain shift reduces accuracy","Empirical curve fitting can be unstable with small datasets (<10K examples)","Out-of-distribution detection is based on feature distribution; may miss semantic OOD cases","Calibration is static; no online recalibration during deployment"],"requires":["Training dataset with 10K+ labeled response pairs","Computed SDM features (similarity, distance, magnitude) for all training examples","Scikit-learn or similar for curve fitting","Validation dataset for calibration quality assessment"],"input_types":["SDM feature vectors and correctness labels from training data","Calibration target (α=0.9 by default)"],"output_types":["Calibration curves (parametric or lookup table)","Decision boundaries for confidence regions","Calibration metrics (expected calibration error, Brier score)","Out-of-distribution threshold parameters"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-reexpress__cap_7","uri":"capability://data.processing.analysis.sdm.feature.extraction.with.similarity.distance.and.magnitude.computation","name":"sdm feature extraction with similarity, distance, and magnitude computation","description":"Extracts three classes of features from response pairs: similarity features (semantic overlap between responses), distance features (divergence in key attributes), and magnitude features (response length, complexity, token count). Features are computed using embedding-based similarity (cosine distance in embedding space), string-based distance metrics (edit distance, token overlap), and response metadata, then normalized and fed to the SDM estimator.","intents":["Compute interpretable features that correlate with response correctness","Compare primary LLM response against ensemble verification outputs","Detect systematic differences between responses (e.g., hallucinations vs. correct answers)","Enable feature-space analysis of verification failures"],"best_for":["Teams wanting interpretable confidence estimates (vs. black-box neural networks)","Workflows requiring feature-level debugging of verification failures","Organizations analyzing why SDM estimator makes specific predictions"],"limitations":["Feature extraction adds latency (~100-200ms per response pair for embedding computation)","Similarity features require embedding model (e.g., sentence-transformers); adds dependency","Distance metrics are heuristic-based; may not capture semantic differences well","Magnitude features are simplistic (length, token count); no structural complexity metrics"],"requires":["Embedding model (sentence-transformers or similar) for similarity computation","Response text and metadata (length, token count)","Normalization parameters (mean, std) computed on training data"],"input_types":["Primary LLM response text","Ensemble verification response texts","Query context (optional)"],"output_types":["Similarity feature vector (cosine similarity, semantic overlap)","Distance feature vector (edit distance, token divergence)","Magnitude feature vector (response length, complexity)","Normalized feature vector for SDM estimator input"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-reexpress__cap_8","uri":"capability://data.processing.analysis.evaluation.methodology.with.calibration.metrics.and.reliability.assessment","name":"evaluation methodology with calibration metrics and reliability assessment","description":"Implements comprehensive evaluation of the SDM estimator using calibration-specific metrics: expected calibration error (ECE), Brier score, AUC-ROC, and reliability diagrams. Evaluation is performed on held-out test sets to assess generalization, and includes per-confidence-region metrics to validate that each region (≥90%, ≤89%, <60%, OOD) achieves its target reliability.","intents":["Validate that confidence estimates are statistically calibrated (e.g., 90% confidence = 90% accuracy)","Identify miscalibrated regions and adjust decision boundaries","Compare SDM estimator performance across domains or model versions","Audit confidence level reliability for compliance or safety requirements"],"best_for":["Teams deploying Reexpress in regulated industries (medical, legal, financial)","Organizations requiring transparent, auditable confidence estimates","ML engineers optimizing SDM calibration for specific domains"],"limitations":["Evaluation requires large held-out test sets (5K+ examples); smaller datasets have high variance","Calibration metrics assume balanced class distribution; imbalanced data may skew results","Per-region metrics may be unreliable if regions have few examples","Evaluation is offline; no online monitoring of calibration drift during deployment"],"requires":["Held-out test dataset with 5K+ labeled response pairs","Computed SDM features and confidence predictions","Scikit-learn or similar for metric computation"],"input_types":["Predicted confidence levels (categorical: ≥90%, ≤89%, <60%, OOD)","Ground-truth correctness labels","SDM feature vectors (optional, for feature importance analysis)"],"output_types":["Calibration metrics (ECE, Brier score, AUC-ROC)","Reliability diagrams (confidence vs. accuracy plots)","Per-region accuracy and count statistics","Calibration quality report"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-reexpress__cap_9","uri":"capability://data.processing.analysis.interactive.visualization.and.analysis.of.response.reliability.in.feature.space","name":"interactive visualization and analysis of response reliability in feature space","description":"Provides interactive visualizations of SDM features and confidence regions, including scatter plots of similarity/distance/magnitude features colored by confidence level, reliability diagrams showing confidence vs. accuracy, and out-of-distribution detection visualizations. Visualizations enable exploration of why specific responses are classified as high/medium/low confidence or out-of-distribution.","intents":["Understand why the SDM estimator assigned a specific confidence level to a response","Identify clusters of responses with similar features and confidence levels","Detect systematic patterns in verification failures (e.g., all low-confidence responses are long)","Audit confidence level assignments for bias or anomalies"],"best_for":["Data scientists and ML engineers debugging SDM estimator behavior","Teams auditing confidence estimates for bias or fairness issues","Workflows requiring transparent, interpretable verification decisions"],"limitations":["Visualizations are 2D/3D projections of high-dimensional feature space; may lose information","Interactive visualizations require web server or Jupyter notebook; not suitable for headless deployments","Large datasets (>100K examples) may be slow to render interactively","No built-in clustering or anomaly detection; requires manual exploration"],"requires":["Matplotlib, Plotly, or similar visualization library","Computed SDM features and confidence predictions","Optional: Jupyter notebook or web server for interactive exploration"],"input_types":["SDM feature vectors (similarity, distance, magnitude)","Predicted confidence levels","Ground-truth labels (optional)"],"output_types":["Scatter plots of feature space colored by confidence","Reliability diagrams (confidence vs. accuracy)","Out-of-distribution detection plots","Feature importance visualizations"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":32,"verified":false,"data_access_risk":"high","permissions":["MCP-compatible LLM client (Claude Opus 4.5 or Sonnet 4.5 recommended)","API keys for OpenAI/Azure (GPT-5.2), Google (Gemini-3-Pro), and local Granite-3.3-8B deployment","Python 3.9+ runtime for SDM estimator","Pre-trained SDM model weights (included in distribution)","Azure OpenAI API key with GPT-5.2 access","Google Cloud API key with Gemini-3-Pro access","Local deployment of Granite-3.3-8B (8GB+ GPU VRAM or quantized CPU variant)","Network connectivity to Azure and Google Cloud endpoints","MCP server runtime with concurrent request handling","API keys for OpenAI/Azure, Google Cloud, and local Granite deployment"],"failure_modes":["Requires pre-trained SDM model; out-of-distribution responses may have lower calibration accuracy","Confidence estimates are calibrated to the OpenVerification1 dataset distribution; domain shift reduces reliability","Ensemble verification adds latency (calls to GPT-5.2, Gemini-3-Pro, and Granite-3.3-8B sequentially)","High-reliability regions are discrete buckets (≥90%, ≤89%, <60%, OOD); no continuous confidence scores","Ensemble verification adds cumulative latency; sequential calls can exceed 5-10 seconds per response","Requires active API subscriptions and quota management for three separate model providers","Ensemble aggregation logic is fixed (no custom weighting per model); all models treated equally","Local Granite-3.3-8B requires GPU resources (~8GB VRAM minimum); CPU inference is prohibitively slow","Abstraction adds ~50-100ms latency per API call (request serialization, response parsing)","Provider-specific features (e.g., vision, function calling) are not exposed through abstraction","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.5,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"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":"2026-06-17T09:51:04.048Z","last_scraped_at":"2026-05-03T14:00:15.503Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=reexpress","compare_url":"https://unfragile.ai/compare?artifact=reexpress"}},"signature":"r2VXctFVdR2JfJAhjb+uMZ3o4ShmpYlYqb5fkNLpncxrMJk7Y27kdHBQJRQ+cw/wIDUgwswNe9mtmmAMAtfiBQ==","signedAt":"2026-06-22T04:11:30.446Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/reexpress","artifact":"https://unfragile.ai/reexpress","verify":"https://unfragile.ai/api/v1/verify?slug=reexpress","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"}}