Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@codex-sentry-mcp fetch the latest error log and analyze root cause"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
An MCP (Model Context Protocol) Server that bridges OpenAI Codex directly with the Sentry error tracking system.
This tool empowers AI development assistants (like Codex) to automatically fetch, read, and analyze the latest error logs from Sentry, pinpoint the root causes, and propose or apply bug fixes directly within the code editor.
Automated Log Retrieval: Instantly fetches the latest error events, stack traces, and issue contexts from Sentry.
Smart Bug Analysis: Provides structured error context directly to OpenAI Codex for deep diagnostic analysis.
Contextual Code Fixes: Enhances the AI's situational awareness to suggest accurate, automated bug resolutions in your workspace.
MCP Compliant: Fully compatible with any Model Context Protocol host or client environment.
Related MCP server: Codemend MCP Server
Node.js (v18 or higher recommended)
A Sentry Account & Auth Token
An MCP-compatible client environment
Clone the repository:
git clone [https://github.com/tongleminhanh3-dotcom/codex.git](https://github.com/tongleminhanh3-dotcom/codex.git)
cd codex-sentry-mcp
Install dependencies:Bash npm install Configure your environment variables in a .env file:
SENTRY_AUTH_TOKEN=your_sentry_token_here SENTRY_ORG=your_organization_slug SENTRY_PROJECT=your_project_slug Run the server: Bash npm start
Fetch the latest unresolved error logs and issues from Sentry
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of errors to retrieve (default is 5) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It only states 'fetch' and 'unresolved', but does not disclose if it modifies state, rate limits, pagination, or other behavioral traits beyond the obvious.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, front-loaded with core action and resource. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Tool is simple with one optional parameter and no output schema. Description covers the basics but lacks details like sorting order, time range for 'latest', or clarification that it returns only unresolved errors.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%; the single parameter 'limit' has a description in the schema. Description does not add extra meaning beyond what schema provides. Baseline 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states action ('fetch') and resource ('latest unresolved error logs and issues'). Uses specific verb+resource structure, no sibling tools to differentiate.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs alternatives or when not to use it. No sibling tools listed, but description lacks context like prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v1.0.0get_latest_sentry_errorsOnly one tool exists, so there is no risk of confusing it with other tools.
With a single tool, naming consistency is moot, but the name follows a clear verb_noun pattern and is descriptive.
A single tool is borderline for a server aimed at Sentry error monitoring; it feels thin but is focused on a specific task.
The server only retrieves errors, lacking common operations like resolving, acknowledging, or filtering errors, leaving significant gaps for typical Sentry workflows.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Give AI coding agents access to your Vynix visual feedback, bug reports, and AI diagnosis.
Monitoring + status pages set up by talking to Claude. Auto-detects 30+ SDKs and your URLs.
AI-powered intelligence for your development workflow via Indicate.
Query your team's drift, vulnerability, and upgrade data from any AI assistant. OAuth 2.1, 51 tools.
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curl -X GET 'https://glama.ai/api/mcp/v1/servers/tongleminhanh3-dotcom/codex-sentry-mcp'
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