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nikydobrev

Azure DevOps Multi-Organization MCP Server

by nikydobrev

pipelines_get_run

Retrieve detailed information about a specific Azure DevOps pipeline run, including status, logs, and execution data for monitoring and analysis.

Instructions

Gets details of a specific pipeline run

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
organizationYesThe name of the Azure DevOps organization
projectYesProject ID or name to run the build in
pipelineIdYesID of the pipeline to run
runIdYesID of the run to get

Implementation Reference

  • The handler function for the pipelines_get_run tool. It retrieves a connection, gets the Pipelines API client, fetches the specific pipeline run by project, pipelineId, and runId, and returns the details as a JSON string in the tool response format.
    async ({ organization, project, pipelineId, runId }) => {
        const connection = await connectionManager.getConnection(organization);
        const pipelinesApi = await connection.getPipelinesApi();
        const pipelineRun = await pipelinesApi.getRun(project, pipelineId, runId);
        return {
            content: [{ type: "text", text: JSON.stringify(pipelineRun, null, 2) }],
        };
    }
  • Zod input schema for the pipelines_get_run tool, validating organization (string), project (string), pipelineId (number), and runId (number).
    {
        organization: z.string().describe("The name of the Azure DevOps organization"),
        project: z.string().describe("Project ID or name to run the build in"),
        pipelineId: z.number().describe("ID of the pipeline to run"),
        runId: z.number().describe("ID of the run to get"),
    },
  • Registration of the pipelines_get_run tool within the registerPipelineTools function using McpServer's server.tool method, including the tool name, description, input schema, and handler implementation.
    server.tool(
      "pipelines_get_run",
      "Gets details of a specific pipeline run",
      {
          organization: z.string().describe("The name of the Azure DevOps organization"),
          project: z.string().describe("Project ID or name to run the build in"),
          pipelineId: z.number().describe("ID of the pipeline to run"),
          runId: z.number().describe("ID of the run to get"),
      },
      async ({ organization, project, pipelineId, runId }) => {
          const connection = await connectionManager.getConnection(organization);
          const pipelinesApi = await connection.getPipelinesApi();
          const pipelineRun = await pipelinesApi.getRun(project, pipelineId, runId);
          return {
              content: [{ type: "text", text: JSON.stringify(pipelineRun, null, 2) }],
          };
      }
    );
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 states this is a read operation ('Gets'), but doesn't mention any behavioral traits like authentication requirements, rate limits, error conditions, or what details are returned. For a tool with no annotation coverage, this leaves significant gaps in understanding how it behaves.

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 states the core purpose without any wasted words. It's appropriately sized for a simple retrieval tool and front-loads the essential information. Every word earns its place.

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 details are returned, potential error conditions, or authentication requirements. For a tool with 4 required parameters and no structured output documentation, the description should provide more context about the operation's behavior and results.

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 description adds no parameter semantics beyond what's already in the schema, which has 100% coverage with clear descriptions for all 4 parameters. The baseline score of 3 is appropriate since the schema adequately documents the parameters, and the description doesn't need to compensate for any gaps.

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 verb ('Gets') and resource ('details of a specific pipeline run'), making the purpose immediately understandable. It distinguishes from sibling tools like 'pipelines_list_runs' by specifying retrieval of a single run rather than listing multiple runs. However, it doesn't explicitly differentiate from other get tools like 'pipelines_get_builds' beyond the resource name.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites like needing a specific run ID, nor does it contrast with similar tools like 'pipelines_get_builds' or 'pipelines_list_runs'. The agent must infer usage solely from the tool name and parameters.

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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