Sentry
MCP ServerFree** - Retrieving and analyzing issues from Sentry.io.
Capabilities5 decomposed
sentry issue retrieval and filtering via mcp
Medium confidenceRetrieves error and performance issues from Sentry.io through the Model Context Protocol, implementing MCP's standardized tool-calling interface to expose Sentry's REST API as callable functions. The server translates MCP tool requests into authenticated Sentry API calls, handling pagination, filtering by project/organization, and returning structured issue data with stack traces, metadata, and resolution status. Uses MCP's resource-based architecture to expose Sentry organizations and projects as discoverable resources that LLMs can query.
Implements Sentry integration as an MCP server, exposing error monitoring as a first-class tool callable by LLMs through MCP's standardized protocol rather than requiring direct API integration. Follows MCP's resource discovery pattern to expose Sentry organizations and projects as queryable resources, enabling LLMs to dynamically discover available monitoring contexts.
Provides LLM-native access to Sentry data through MCP's standardized interface, eliminating the need for custom API wrappers or prompt engineering to interact with error data, compared to passing raw Sentry API documentation to LLMs.
mcp protocol transport and tool schema exposure
Medium confidenceImplements the Model Context Protocol server specification, exposing Sentry capabilities as discoverable MCP tools with JSON Schema definitions. The server handles MCP's JSON-RPC 2.0 transport layer (stdio or HTTP), manages tool registration with input/output schemas, and routes incoming tool calls from MCP clients to appropriate Sentry API handlers. Implements MCP's resource and tool discovery mechanisms so clients can enumerate available operations before invoking them.
Implements full MCP server specification including resource discovery, tool schema registration, and JSON-RPC transport handling. Exposes Sentry as a composable tool within MCP's multi-tool ecosystem rather than a standalone API wrapper.
Provides standardized MCP interface for Sentry integration, enabling seamless composition with other MCP servers (GitHub, Slack, databases) in unified agent workflows, versus custom API clients that require separate integration logic per service.
sentry api authentication and credential management
Medium confidenceManages Sentry API authentication by accepting and validating API tokens or DSN credentials, storing them securely for use in subsequent API requests. The server implements credential handling patterns that allow MCP clients to provide authentication once during initialization, then transparently includes credentials in all Sentry API calls without requiring the client to manage tokens. Supports both organization-level and project-level API tokens with appropriate scope validation.
Implements MCP-specific credential handling where tokens are provided once to the server during initialization, then transparently included in all downstream API calls, rather than requiring clients to manage and pass credentials with each tool invocation.
Separates credential management from tool invocation logic, reducing security surface compared to passing API tokens as parameters in each LLM-generated tool call.
issue data transformation and schema mapping
Medium confidenceTransforms raw Sentry API responses into structured, LLM-friendly formats by mapping Sentry's native issue schema to simplified JSON objects with relevant fields (error message, stack trace, affected users, timestamps, resolution status). Implements field selection and flattening logic to reduce noise and focus on actionable debugging information. Handles nested Sentry data structures (events, tags, breadcrumbs) and presents them in a format optimized for LLM comprehension and reasoning.
Implements LLM-specific data transformation that prioritizes readability and reasoning capability over completeness, selecting and flattening Sentry's nested structures to match how LLMs best process error information.
Provides pre-processed, LLM-optimized issue data compared to passing raw Sentry API responses, reducing the cognitive load on LLMs to parse complex nested structures and improving reasoning quality.
project and organization resource discovery
Medium confidenceExposes Sentry organizations and projects as discoverable MCP resources, allowing LLM clients to enumerate available monitoring contexts before querying issues. Implements MCP's resource listing pattern to return available projects with metadata (project slug, team, platform), enabling LLMs to dynamically discover which Sentry projects are accessible with the provided credentials. Supports filtering and pagination of resource lists for large Sentry instances.
Implements MCP's resource discovery pattern for Sentry, exposing projects as first-class discoverable resources rather than requiring clients to hardcode project identifiers or maintain separate project registries.
Enables dynamic, context-aware project selection in LLM workflows compared to static project configuration, allowing agents to adapt to changing monitoring contexts.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
Related Artifactssharing capabilities
Artifacts that share capabilities with Sentry, ranked by overlap. Discovered automatically through the match graph.
@sentry/mcp-server
Sentry MCP Server
@sentry/mcp-server
Sentry MCP Server
Sentry MCP Server
Query Sentry issues, errors, and stack traces via MCP.
cls-mcp-server
[](https://www.npmjs.com/package/cls-mcp-server) [](https://github.com/Tencent/cls-mcp-server/blob/v1.0.2/LICENSE)
Hippycampus
** - Turns any Swagger/OpenAPI REST endpoint with a yaml/json definition into an MCP Server with Langchain/Langflow integration automatically.
mcp-client
** MCP REST API and CLI client for interacting with MCP servers, supports OpenAI, Claude, Gemini, Ollama etc.
Best For
- ✓DevOps engineers and SREs building LLM-powered incident response agents
- ✓Development teams using Claude or other MCP-compatible LLMs for automated error triage
- ✓Teams migrating error monitoring workflows into AI-assisted debugging pipelines
- ✓Developers building MCP-compatible LLM applications that need error monitoring context
- ✓Teams standardizing on MCP for tool integration across multiple services
- ✓Teams deploying Sentry MCP servers in shared or multi-tenant environments
- ✓Developers building secure LLM agent workflows that access production monitoring data
- ✓LLM agents that need to reason about error data without understanding Sentry's internal schema
Known Limitations
- ⚠Archived and unmaintained — no security updates or bug fixes since archival
- ⚠No built-in caching of issue data — each query hits Sentry API, risking rate limits
- ⚠Limited to read-only operations; cannot create issues, update status, or post comments via MCP
- ⚠Requires valid Sentry API token with appropriate project/organization permissions
- ⚠Archived implementation — may not support latest MCP protocol versions
- ⚠No built-in error recovery or retry logic for failed Sentry API calls
Requirements
Input / Output
UnfragileRank
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About
** - Retrieving and analyzing issues from Sentry.io.
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