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tiovikram

Linear MCP Server

by tiovikram

search_issues

Search Linear issues using text queries to find and retrieve relevant tickets for project management and tracking.

Instructions

Search for issues using a text query

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesSearch query text
firstNoNumber of results to return (default: 50)

Implementation Reference

  • The switch case handler for the 'search_issues' tool. Validates input arguments, calls LinearClient.searchIssues with the query, formats the search results (including id, title, status, assignee, priority, url, metadata), and returns as JSON text content.
    case "search_issues": {
      const args = request.params.arguments as unknown as SearchIssuesArgs;
      if (!args?.query) {
        throw new Error("Search query is required");
      }
    
      const searchResults = await linearClient.searchIssues(args.query, {
        first: args?.first ?? 50,
      });
    
      const formattedResults = await Promise.all(
        searchResults.nodes.map(async (result) => {
          const state = await result.state;
          const assignee = await result.assignee;
          return {
            id: result.id,
            title: result.title,
            status: state ? await state.name : "Unknown",
            assignee: assignee ? assignee.name : "Unassigned",
            priority: result.priority,
            url: result.url,
            metadata: result.metadata,
          };
        })
      );
    
      return {
        content: [
          {
            type: "text",
            text: JSON.stringify(formattedResults, null, 2),
          },
        ],
      };
    }
  • The input schema definition for the 'search_issues' tool as provided in the tool registration, specifying the expected arguments (query required, first optional).
    inputSchema: {
      type: "object",
      properties: {
        query: {
          type: "string",
          description: "Search query text",
        },
        first: {
          type: "number",
          description: "Number of results to return (default: 50)",
        },
      },
      required: ["query"],
    },
  • src/index.ts:186-203 (registration)
    The full tool object registration for 'search_issues' in the ListTools response, including name, description, and input schema.
    {
      name: "search_issues",
      description: "Search for issues using a text query",
      inputSchema: {
        type: "object",
        properties: {
          query: {
            type: "string",
            description: "Search query text",
          },
          first: {
            type: "number",
            description: "Number of results to return (default: 50)",
          },
        },
        required: ["query"],
      },
    },
  • TypeScript type definition matching the input schema for SearchIssuesArgs used in the handler for type safety.
    type SearchIssuesArgs = {
      query: string;
      first?: number;
    };
  • src/index.ts:52-52 (registration)
    Capability flag in server capabilities declaring support for the 'search_issues' tool.
    search_issues: true,
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 searching with a text query but fails to describe critical traits like whether results are paginated, sorted, or filtered beyond the query, what permissions are required, or how the search operates (e.g., full-text, exact match). This leaves significant gaps for a search tool.

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 that directly states the tool's function without unnecessary words. It is appropriately sized and front-loaded, making it easy for an agent to parse quickly.

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 complexity of a search operation with no annotations and no output schema, the description is incomplete. It lacks details on behavioral aspects (e.g., search scope, result format) and doesn't compensate for the absence of structured fields, making it inadequate for effective tool selection and invocation.

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?

The input schema has 100% description coverage, documenting both parameters ('query' and 'first') adequately. The description adds no additional meaning beyond what the schema provides, such as query syntax or result formatting. With high schema coverage, the baseline score of 3 is appropriate as the schema does the heavy lifting.

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

Purpose4/5

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

The description clearly states the action ('search for issues') and resource ('issues'), making the purpose evident. However, it doesn't differentiate from sibling tools like 'list_issues' or 'get_issue', which might also retrieve issues but with different mechanisms or scopes.

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 'list_issues' or 'get_issue'. The description implies usage for text-based queries but lacks explicit context or exclusions, leaving the agent to infer based on tool names alone.

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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