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diegofornalha

MCP Sentry para Cursor

sentry_list_issues

Retrieve and filter error issues from Sentry projects to monitor application health and identify problems using search queries like 'is:unresolved' or 'level:error'.

Instructions

List issues for a project

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
projectSlugYesProject slug/identifier
queryNoSearch query (e.g., 'is:unresolved', 'level:error')

Implementation Reference

  • Executes the sentry_list_issues tool: checks apiClient, extracts projectSlug and query, calls apiClient.listIssues, formats and returns list of issues.
    case "sentry_list_issues": {
      if (!apiClient) {
        throw new Error("Sentry API client not initialized. Provide auth token.");
      }
      
      const { projectSlug, query } = args as any;
      const issues = await apiClient.listIssues(projectSlug, query);
      
      return {
        content: [
          {
            type: "text",
            text: `Found ${issues.length} issues in ${projectSlug}:\n${issues.slice(0, 10).map((i: any) => 
              `- [${i.level}] ${i.title} (${i.count} events)`
            ).join('\n')}${issues.length > 10 ? '\n... and more' : ''}`,
          },
        ],
      };
    }
  • Core implementation of listing issues: constructs API endpoint with projectSlug and optional query, calls private request method to fetch from Sentry API.
    async listIssues(projectSlug: string, query?: string) {
      const params = query ? `?query=${encodeURIComponent(query)}` : '';
      return this.request(`/projects/${this.org}/${projectSlug}/issues/${params}`);
    }
  • src/index.ts:397-413 (registration)
    Registers the sentry_list_issues tool in the ListTools response with name, description, and input schema.
    {
      name: "sentry_list_issues",
      description: "List issues for a project",
      inputSchema: {
        type: "object",
        properties: {
          projectSlug: {
            type: "string",
            description: "Project slug/identifier",
          },
          query: {
            type: "string",
            description: "Search query (e.g., 'is:unresolved', 'level:error')",
          },
        },
        required: ["projectSlug"],
      },
  • Private request method used by listIssues to make authenticated HTTP requests to Sentry API.
    private async request(endpoint: string, options: any = {}) {
      const url = `${this.baseUrl}${endpoint}`;
      const response = await fetch(url, {
        ...options,
        headers: {
          'Authorization': `Bearer ${this.authToken}`,
          'Content-Type': 'application/json',
          ...options.headers,
        },
      });
    
      if (!response.ok) {
        throw new Error(`Sentry API error: ${response.status} ${response.statusText}`);
      }
    
      return response.json();
    }
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions listing issues but doesn't cover critical aspects like pagination, rate limits, authentication requirements, or the format of returned data. This leaves significant gaps for an AI agent to understand how to interact with the tool effectively.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with zero waste. It's front-loaded with the core action and resource, making it easy to parse quickly. Every word contributes directly to the tool's purpose without unnecessary elaboration.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'issues' are in Sentry's context, how results are structured, or any behavioral traits like pagination. For a tool with two parameters and no structured output information, more context is needed to guide effective usage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the input schema already documents both parameters ('projectSlug' and 'query') adequately. The description adds no additional meaning beyond what the schema provides, such as examples of common queries or clarification on project slug format, meeting the baseline for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'List issues for a project' clearly states the verb ('List') and resource ('issues'), but it's vague about scope and doesn't differentiate from siblings like 'sentry_list_issue_events' or 'sentry_get_issue'. It specifies the target ('for a project'), which provides some context, but lacks detail on what 'issues' entail in this context.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives such as 'sentry_get_issue' (for a single issue) or 'sentry_list_issue_events' (for events related to issues). The description implies usage for listing issues, but offers no context on prerequisites, filtering capabilities, or comparisons to sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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