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nikydobrev

Azure DevOps Multi-Organization MCP Server

by nikydobrev

pipelines_list_runs

List all pipeline runs in Azure DevOps to track execution history, monitor build status, and analyze performance across multiple organizations.

Instructions

Lists all runs for a specific pipeline

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

Implementation Reference

  • Registration of the 'pipelines_list_runs' MCP tool, including input schema (organization, project, pipelineId) and handler function that retrieves the list of pipeline runs via the Azure DevOps Pipelines API.
    server.tool(
      "pipelines_list_runs",
      "Lists all runs for a specific pipeline",
      {
          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"),
      },
      async ({ organization, project, pipelineId }) => {
          const connection = await connectionManager.getConnection(organization);
          const pipelinesApi = await connection.getPipelinesApi();
          const pipelineRuns = await pipelinesApi.listRuns(project, pipelineId);
          return {
              content: [{ type: "text", text: JSON.stringify(pipelineRuns, null, 2) }],
          };
      }
    );
  • Handler function for 'pipelines_list_runs' tool: connects to Azure DevOps, gets PipelinesApi, calls listRuns(project, pipelineId), and returns JSON stringified list of runs.
    async ({ organization, project, pipelineId }) => {
        const connection = await connectionManager.getConnection(organization);
        const pipelinesApi = await connection.getPipelinesApi();
        const pipelineRuns = await pipelinesApi.listRuns(project, pipelineId);
        return {
            content: [{ type: "text", text: JSON.stringify(pipelineRuns, null, 2) }],
        };
    }
  • Zod input schema for 'pipelines_list_runs' tool defining required parameters: organization (string), project (string), pipelineId (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"),
    },
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 it's a list operation, implying it's read-only, but doesn't mention any behavioral traits like pagination, rate limits, authentication needs, or what the output format looks like (e.g., JSON array of runs). This leaves significant gaps for an agent to use it 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 that directly states the tool's purpose without any fluff. It's front-loaded and appropriately sized for a simple list operation, with every word contributing to clarity.

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 (a list operation with 3 required parameters), no annotations, and no output schema, the description is incomplete. It lacks details on behavioral aspects (e.g., output format, pagination) and usage context, which are crucial for an agent to invoke this tool correctly without trial and error.

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, clearly documenting each parameter (organization, project, pipelineId). The description adds no additional semantic context beyond what the schema provides, such as explaining relationships between parameters or usage examples. This meets the baseline for high schema coverage.

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 ('Lists') and resource ('all runs for a specific pipeline'), making the purpose immediately understandable. However, it doesn't distinguish this tool from sibling tools like 'pipelines_get_builds' or 'pipelines_get_run', which also deal with pipeline runs but with different scopes or details.

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. For example, it doesn't clarify if this is for listing all runs versus filtered runs, or how it differs from 'pipelines_get_builds' or 'pipelines_get_run' in the sibling list, leaving the agent to guess 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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