crash-report-retrieval-and-filtering, real-user-monitoring-metrics-aggregation, error-group-management-and-annotation, deployment-tracking-and-error-correlation, user-impact-and-affected-user-analysis, custom-grouping-and-error-pattern-detection

Raygun

MCP ServerFree

** - Interact with your crash reporting and real using monitoring data on your Raygun account

Open Source

/ 100

6 capabilities

Capabilities6 decomposed

crash-report-retrieval-and-filtering

Medium confidence

Fetches crash reports from Raygun's API with support for filtering by application, time range, status, and severity level. Implements pagination and structured JSON response parsing to handle large datasets of error events. Integrates directly with Raygun's REST API endpoints to query the full crash reporting database without local caching, enabling real-time access to the latest incident data.

Solves for

I need to pull all crashes from the last 24 hours for a specific application to analyze trendsI want to filter crashes by severity level to prioritize critical issuesI need to retrieve crash metadata including stack traces, affected users, and timestamps

Best for

DevOps engineers integrating crash data into incident response workflows

Development teams building custom dashboards on top of Raygun data

Automated monitoring systems that need programmatic access to error events

Requires

Raygun API key with read permissions

Valid Raygun account with active applications

Network access to Raygun's API endpoints (api.raygun.io)

Limitations

API rate limiting applies — Raygun enforces request throttling that may impact high-frequency polling

Pagination required for large result sets — no built-in streaming, requires manual iteration through pages

Filtering capabilities limited to Raygun's API schema — cannot perform complex multi-field queries beyond API support

What makes it unique

Direct MCP server integration with Raygun's proprietary crash reporting API, enabling Claude and other MCP clients to query real-time error data without custom API wrapper code. Implements Raygun-specific filtering semantics (severity, status, application context) natively rather than generic search.

vs alternatives

Tighter integration than generic HTTP clients because it understands Raygun's domain model (crash groups, user impact, version tracking) and exposes them as first-class MCP tools rather than raw API calls.

real-user-monitoring-metrics-aggregation

Medium confidence

Aggregates Real User Monitoring (RUM) data from Raygun including page load times, JavaScript errors, network performance, and user session metrics. Queries Raygun's analytics endpoints to compute time-series metrics and percentile distributions (p50, p95, p99) for performance analysis. Structures raw telemetry into actionable performance KPIs without requiring manual data transformation.

Solves for

I want to check current page load performance metrics across all usersI need to identify which pages have the highest error rates in productionI want to compare performance metrics between different application versions or environments

Best for

Performance engineers monitoring production application health

Product teams tracking user experience metrics for SLA compliance

Site reliability engineers building automated performance alerting

Requires

Raygun RUM SDK installed and configured in target application

Raygun API key with analytics read permissions

Active RUM data collection (requires end-user traffic)

Limitations

RUM data collection requires Raygun JavaScript SDK deployed in production — cannot retroactively collect historical data

Aggregation granularity limited by Raygun's retention policies — older data may be sampled or unavailable

Custom metric definitions not supported — limited to Raygun's pre-defined RUM metrics

What makes it unique

Exposes Raygun's RUM aggregation engine as MCP tools, allowing Claude to directly query performance percentiles and user impact metrics without manual API pagination or statistical computation. Handles Raygun's specific metric schemas (page load breakdown, network timing, error categorization).

vs alternatives

More domain-aware than generic analytics APIs because it understands Raygun's RUM data model and automatically computes performance percentiles and user impact scoring rather than returning raw event streams.

error-group-management-and-annotation

Medium confidence

Manages error group lifecycle in Raygun including status transitions (new → assigned → resolved), bulk operations on grouped crashes, and annotation/comment addition for collaboration. Implements state machine logic for error group workflows and supports batch updates across multiple related crashes. Enables team coordination on error resolution without requiring manual Raygun UI interaction.

Solves for

I want to mark a group of related crashes as resolved after deploying a fixI need to assign an error group to a team member and add context about the root causeI want to bulk-update the status of multiple error groups based on deployment events

Best for

Development teams automating error triage workflows

DevOps engineers triggering error status updates from CI/CD pipelines

Incident response teams coordinating crash investigation across multiple team members

Requires

Raygun API key with write permissions (not read-only)

Valid Raygun user account for assignment operations

Error group IDs from prior crash report queries

Limitations

Batch operations limited to Raygun API constraints — typically 100-1000 items per request

Annotations are append-only — no edit or delete capability for existing comments

Assignment requires valid Raygun user accounts — cannot assign to external stakeholders

What makes it unique

Implements Raygun's error group state machine as MCP tools, allowing Claude to orchestrate multi-step error triage workflows (query → analyze → assign → annotate → resolve) without context switching to the Raygun UI. Supports batch operations and integrates with deployment pipelines.

vs alternatives

More workflow-aware than raw API clients because it understands error group lifecycle semantics and can chain operations (e.g., auto-resolve groups after deployment, bulk-assign based on error patterns) rather than requiring manual step-by-step API calls.

deployment-tracking-and-error-correlation

Medium confidence

Tracks application deployments in Raygun and correlates crash spikes with deployment events to identify regression-causing changes. Queries deployment history and cross-references with error group timelines to detect when new crashes appeared relative to code releases. Implements time-series correlation logic to surface deployment-error relationships without manual timeline analysis.

Solves for

I want to see if a crash spike correlates with a recent deploymentI need to identify which deployment introduced a new error patternI want to compare error rates before and after a specific release

Best for

Release engineers investigating deployment-related regressions

Development teams performing post-deployment validation

Automated systems detecting and alerting on deployment-induced errors

Requires

Raygun deployment tracking enabled (requires API integration or SDK configuration)

Deployment events logged to Raygun (via API or CI/CD integration)

Error data with precise timestamps for correlation

Limitations

Correlation detection requires manual interpretation — no built-in ML-based causality detection

Deployment tracking depends on Raygun's deployment API integration — requires explicit deployment event logging

Time-series granularity limited by Raygun's data retention and sampling policies

What makes it unique

Correlates Raygun's deployment events with crash timelines to automatically surface regression candidates, enabling Claude to identify deployment-error relationships without manual timeline inspection. Implements Raygun-specific deployment metadata (version, timestamp, user) in correlation logic.

vs alternatives

More actionable than generic error analytics because it explicitly models deployment events as a causal dimension and surfaces deployment-error correlations as structured insights rather than requiring manual cross-referencing of separate data sources.

user-impact-and-affected-user-analysis

Medium confidence

Analyzes user impact metrics for crashes including affected user counts, unique user segments, and user session context. Queries Raygun's user tracking data to identify which users experienced specific errors and their session context (browser, device, location, custom user attributes). Enables impact-driven prioritization by surfacing how many users were affected and their characteristics.

Solves for

I want to know how many users were affected by a critical crashI need to identify if a crash is affecting a specific user segment (e.g., mobile users, specific region)I want to prioritize fixes based on user impact rather than error frequency

Best for

Product managers prioritizing bug fixes based on user impact

Support teams identifying affected customers for targeted communication

Engineering teams making triage decisions based on user reach

Requires

Raygun SDK with user tracking enabled

User identification configured in application

Raygun API key with user data read permissions

Limitations

User tracking depends on Raygun SDK configuration — requires explicit user identification setup

Privacy constraints may limit user data availability — PII redaction policies apply

User segmentation limited to Raygun's pre-defined attributes — custom segments require additional configuration

What makes it unique

Exposes Raygun's user impact metrics as MCP tools, allowing Claude to directly query affected user counts and segment breakdowns without manual aggregation. Implements Raygun's user tracking schema (unique identifiers, session context, custom attributes) natively.

vs alternatives

More user-centric than error-frequency-based prioritization because it directly queries Raygun's user impact data and enables impact-driven triage decisions rather than treating all errors equally regardless of user reach.

custom-grouping-and-error-pattern-detection

Medium confidence

Applies custom grouping rules to crashes based on stack trace patterns, error messages, and custom attributes to surface related errors that Raygun's default grouping may miss. Implements pattern matching logic to identify error families and create synthetic error groups for analysis. Enables detection of systemic issues that manifest as multiple distinct error signatures.

Solves for

I want to group errors by root cause even though they have different stack tracesI need to identify all errors related to a specific feature or componentI want to detect error patterns that indicate a systemic issue rather than isolated bugs

Best for

Engineering teams analyzing error patterns across multiple error signatures

Platform teams identifying systemic issues affecting multiple services

Data analysts building custom error taxonomies for reporting

Requires

Access to error group data with stack traces and error messages

Pattern definition capability (regex, string matching, or custom logic)

Sufficient error volume to validate pattern accuracy

Limitations

Pattern matching limited to available error metadata — cannot infer patterns from error behavior alone

Custom grouping rules are stateless — no persistent rule storage or versioning

Performance impact scales with error volume — pattern matching on large datasets may be slow

What makes it unique

Implements custom error grouping logic on top of Raygun's native grouping, allowing Claude to detect error patterns and create synthetic error families based on stack trace analysis, error messages, and custom attributes. Enables multi-dimensional error correlation beyond Raygun's default grouping.

vs alternatives

More flexible than Raygun's built-in grouping because it allows arbitrary pattern matching rules and can surface error relationships that Raygun's heuristics miss, enabling custom root-cause analysis workflows.

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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crash log capture and analysis

1 shared capability

MCP Server26

@google-cloud/observability-mcp

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1 shared capability

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Unified framework for building enterprise RAG pipelines with small, specialized models

evaluation and metrics tracking for rag quality

1 shared capability

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Observability and DevTool Platform for AI Agents

error tracking and failure analysis

1 shared capability

MCP Server26

ref-tools-mcp

ModelContextProtocol server for Ref

error handling and diagnostic reporting for ref tool failures

1 shared capability

Best For

✓DevOps engineers integrating crash data into incident response workflows
✓Development teams building custom dashboards on top of Raygun data
✓Automated monitoring systems that need programmatic access to error events
✓Performance engineers monitoring production application health
✓Product teams tracking user experience metrics for SLA compliance
✓Site reliability engineers building automated performance alerting
✓Development teams automating error triage workflows
✓DevOps engineers triggering error status updates from CI/CD pipelines

Known Limitations

⚠API rate limiting applies — Raygun enforces request throttling that may impact high-frequency polling
⚠Pagination required for large result sets — no built-in streaming, requires manual iteration through pages
⚠Filtering capabilities limited to Raygun's API schema — cannot perform complex multi-field queries beyond API support
⚠RUM data collection requires Raygun JavaScript SDK deployed in production — cannot retroactively collect historical data
⚠Aggregation granularity limited by Raygun's retention policies — older data may be sampled or unavailable
⚠Custom metric definitions not supported — limited to Raygun's pre-defined RUM metrics

Requirements

Raygun API key with read permissionsValid Raygun account with active applicationsNetwork access to Raygun's API endpoints (api.raygun.io)Raygun RUM SDK installed and configured in target applicationRaygun API key with analytics read permissionsActive RUM data collection (requires end-user traffic)Raygun API key with write permissions (not read-only)Valid Raygun user account for assignment operations

Input / Output

Accepts: application_id (string), time_range (ISO 8601 dates or relative), severity_filter (enum: critical, error, warning, info), status_filter (enum: new, assigned, resolved), time_window (ISO 8601 or relative duration), metric_type (enum: page_load, js_errors, network, session), grouping_dimension (optional: page_url, browser, device_type), error_group_id (string), new_status (enum: new, assigned, resolved, ignored), assigned_user_id (string, optional), annotation_text (string, optional), batch_group_ids (array of strings, optional), deployment_id (string, optional), time_window (ISO 8601 dates), error_group_id (string, optional), segment_filter (optional: browser, device_type, location, custom_attribute), error_groups (array of error group objects), pattern_rules (array of matching criteria: stack_trace_pattern, error_message_pattern, custom_attribute_match), grouping_strategy (enum: union, intersection, custom_logic)

Produces: JSON array of crash report objects, structured metadata (timestamps, user counts, affected versions), stack trace data (raw and parsed), JSON object with aggregated metrics, percentile distributions (p50, p95, p99), time-series data points, user session summaries, confirmation response with updated status, updated error group metadata, operation success/failure status, deployment timeline with associated error metrics, error rate before/after deployment comparison, correlation analysis (errors introduced, errors resolved), affected user count (total and unique), user segment breakdown (browser, device, location), user session context (timestamps, session duration), custom user attributes, synthetic error groups (grouped by pattern), pattern match confidence scores, error family summaries

UnfragileRank

Adoption15%(25% weight)

Quality14%(25% weight)

Ecosystem30%(15% weight)

Match Graph25%(30% weight)

Freshness75%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: MCP Server

6 capabilities

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** - Interact with your crash reporting and real using monitoring data on your Raygun account

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Capabilities6 decomposed

crash-report-retrieval-and-filtering

Medium confidence

Solves for

Best for

DevOps engineers integrating crash data into incident response workflows

Development teams building custom dashboards on top of Raygun data

Automated monitoring systems that need programmatic access to error events

Requires

Raygun API key with read permissions

Valid Raygun account with active applications

Network access to Raygun's API endpoints (api.raygun.io)

Limitations

API rate limiting applies — Raygun enforces request throttling that may impact high-frequency polling

Pagination required for large result sets — no built-in streaming, requires manual iteration through pages

Filtering capabilities limited to Raygun's API schema — cannot perform complex multi-field queries beyond API support

What makes it unique

vs alternatives

real-user-monitoring-metrics-aggregation

Medium confidence

Solves for

Best for

Performance engineers monitoring production application health

Product teams tracking user experience metrics for SLA compliance

Site reliability engineers building automated performance alerting

Requires

Raygun RUM SDK installed and configured in target application

Raygun API key with analytics read permissions

Active RUM data collection (requires end-user traffic)

Limitations

RUM data collection requires Raygun JavaScript SDK deployed in production — cannot retroactively collect historical data

Aggregation granularity limited by Raygun's retention policies — older data may be sampled or unavailable

Custom metric definitions not supported — limited to Raygun's pre-defined RUM metrics

What makes it unique

vs alternatives

error-group-management-and-annotation

Medium confidence

Solves for

Best for

Development teams automating error triage workflows

DevOps engineers triggering error status updates from CI/CD pipelines

Incident response teams coordinating crash investigation across multiple team members

Requires

Raygun API key with write permissions (not read-only)

Valid Raygun user account for assignment operations

Error group IDs from prior crash report queries

Limitations

Batch operations limited to Raygun API constraints — typically 100-1000 items per request

Annotations are append-only — no edit or delete capability for existing comments

Assignment requires valid Raygun user accounts — cannot assign to external stakeholders

What makes it unique

vs alternatives

deployment-tracking-and-error-correlation

Medium confidence

Solves for

I want to see if a crash spike correlates with a recent deploymentI need to identify which deployment introduced a new error patternI want to compare error rates before and after a specific release

Best for

Release engineers investigating deployment-related regressions

Development teams performing post-deployment validation

Automated systems detecting and alerting on deployment-induced errors

Requires

Raygun deployment tracking enabled (requires API integration or SDK configuration)

Deployment events logged to Raygun (via API or CI/CD integration)

Error data with precise timestamps for correlation

Limitations

Correlation detection requires manual interpretation — no built-in ML-based causality detection

Deployment tracking depends on Raygun's deployment API integration — requires explicit deployment event logging

Time-series granularity limited by Raygun's data retention and sampling policies

What makes it unique

vs alternatives

user-impact-and-affected-user-analysis

Medium confidence

Solves for

Best for

Product managers prioritizing bug fixes based on user impact

Support teams identifying affected customers for targeted communication

Engineering teams making triage decisions based on user reach

Requires

Raygun SDK with user tracking enabled

User identification configured in application

Raygun API key with user data read permissions

Limitations

User tracking depends on Raygun SDK configuration — requires explicit user identification setup

Privacy constraints may limit user data availability — PII redaction policies apply

User segmentation limited to Raygun's pre-defined attributes — custom segments require additional configuration

What makes it unique

vs alternatives

custom-grouping-and-error-pattern-detection

Medium confidence

Solves for

Best for

Engineering teams analyzing error patterns across multiple error signatures

Platform teams identifying systemic issues affecting multiple services

Data analysts building custom error taxonomies for reporting

Requires

Access to error group data with stack traces and error messages

Pattern definition capability (regex, string matching, or custom logic)

Sufficient error volume to validate pattern accuracy

Limitations

Pattern matching limited to available error metadata — cannot infer patterns from error behavior alone

Custom grouping rules are stateless — no persistent rule storage or versioning

Performance impact scales with error volume — pattern matching on large datasets may be slow

What makes it unique

vs alternatives

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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Raygun

Capabilities6 decomposed

crash-report-retrieval-and-filtering

real-user-monitoring-metrics-aggregation

error-group-management-and-annotation

deployment-tracking-and-error-correlation

user-impact-and-affected-user-analysis

custom-grouping-and-error-pattern-detection

Related Artifactssharing capabilities

Galileo Observe

XcodeBuildMCP

@google-cloud/observability-mcp

llmware

agentops

ref-tools-mcp

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to Raygun

Are you the builder of Raygun?

Get the weekly brief

Data Sources

Raygun

Capabilities6 decomposed

crash-report-retrieval-and-filtering

real-user-monitoring-metrics-aggregation

error-group-management-and-annotation

deployment-tracking-and-error-correlation

user-impact-and-affected-user-analysis

custom-grouping-and-error-pattern-detection

Related Artifactssharing capabilities

Galileo Observe

XcodeBuildMCP

@google-cloud/observability-mcp

llmware

agentops

ref-tools-mcp

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to Raygun

Are you the builder of Raygun?

Get the weekly brief

Data Sources