Harness
MCP ServerFree** - Access and interact with Harness platform data, including pipelines, repositories, logs, and artifact registries.
Capabilities14 decomposed
mcp-standardized harness api bridging via json-rpc stdio protocol
Medium confidenceExposes Harness platform APIs through a Model Context Protocol (MCP) server that communicates with clients (Claude Desktop, VS Code, Cursor, Windsurf) using JSON-RPC 2.0 over stdio. The server acts as a protocol adapter, translating MCP tool calls into authenticated HTTP requests to Harness backend services and marshaling responses back through the MCP interface. This enables AI assistants and development tools to invoke Harness operations without direct API knowledge.
Implements dual-mode authentication (API key for external clients via stdio, JWT for internal services) with mode-specific toolset registration, allowing the same MCP server binary to serve both external developers and internal Harness microservices with appropriate access controls and base URLs.
Provides standardized MCP protocol support across multiple IDEs and AI tools simultaneously, whereas direct REST API clients require tool-specific integration code for each platform.
dual-mode authentication with api key and jwt token providers
Medium confidenceThe server implements two distinct authentication mechanisms selected via config.Internal flag: external stdio mode uses APIKeyProvider to authenticate requests with Harness API keys passed by clients, while internal mode uses JWTProvider to authenticate with JWT tokens signed using service-specific secrets. Each provider wraps HTTP client operations, injecting credentials into request headers before forwarding to Harness backend services. This architecture enables the same MCP server to serve both external developers and internal microservices with appropriate security boundaries.
Implements pluggable authentication providers (APIKeyProvider and JWTProvider) that wrap HTTP client creation at initialization time, allowing the same service client code to work with either authentication mechanism without conditional logic throughout the codebase. The InitToolsets orchestrator selects the appropriate provider based on config.Internal flag.
Supports both external API key and internal JWT authentication in a single binary, whereas most MCP servers require separate deployments or hardcoded authentication mechanisms.
internal ai services access with genai and chatbot integration (internal mode only)
Medium confidenceExposes internal Harness AI services through AIServices toolset available only in internal mode (JWT authentication). This includes genai service for AI-powered code generation and analysis, and chatbot service for conversational AI interactions. The implementation provides internal Harness microservices with direct access to AI capabilities through MCP tools, enabling AI-driven features within the Harness platform itself. These toolsets are not exposed in external stdio mode for security and licensing reasons.
Implements internal AI services (genai, chatbot) as toolsets that are conditionally registered only in internal mode (config.Internal = true), providing Harness microservices with direct MCP access to AI capabilities while maintaining security boundaries that prevent external client access.
Provides internal Harness services with standardized MCP access to AI capabilities, whereas direct service-to-service calls require custom integration code and lack the standardized tool interface.
connector management and configuration querying
Medium confidenceExposes connector operations through a Connectors toolset that enables listing configured connectors, retrieving connector details, validating connector connectivity, and managing connector configurations. The implementation provides access to all Harness connector types (Git, artifact registry, cloud, infrastructure) through unified APIs. This enables AI agents to discover available integrations, validate connector health, and manage connector configurations programmatically.
Implements connector operations through Harness Connector Service, providing unified access to all connector types (Git, artifact, cloud, infrastructure) with consistent APIs for listing, validating, and managing connectors. The Connectors service client abstracts connector-specific details, enabling AI agents to work with any connector type using identical tool signatures.
Provides unified connector management across all Harness connector types through a single toolset, whereas direct connector APIs require separate implementations for each connector type.
dashboard and metric visualization querying with custom dashboard support
Medium confidenceExposes dashboard operations through a Dashboards toolset that enables listing dashboards, retrieving dashboard definitions, querying dashboard metrics, and analyzing dashboard data. The implementation provides access to Harness dashboards and custom dashboards, enabling AI agents to retrieve metrics and visualizations for analysis. This enables AI agents to understand system state through dashboard data, generate reports, and provide insights based on dashboard metrics.
Implements dashboard operations through Harness Dashboard Service, providing unified access to both built-in and custom dashboards with metric querying and analysis capabilities. The Dashboards service client abstracts dashboard-specific details, enabling AI agents to retrieve and analyze dashboard data without understanding dashboard definition formats.
Provides unified dashboard data retrieval and analysis through Harness, whereas direct dashboard tools (Grafana, Datadog) require separate APIs and metric aggregation logic.
read-only mode enforcement with configurable write operation restrictions
Medium confidenceImplements a read-only mode that can be enabled via --read-only flag in stdio mode, preventing write operations (pipeline execution, PR comments, connector modifications) while allowing read operations (querying status, retrieving logs, listing resources). The implementation enforces read-only restrictions at the toolset level by conditionally registering write-capable tools. This enables safe deployment of MCP servers in restricted environments where only query operations are permitted.
Implements read-only mode as a startup configuration flag that conditionally registers write-capable toolsets, providing a simple but effective mechanism to prevent write operations in restricted environments. The implementation enforces read-only restrictions at the toolset registration level rather than per-operation, reducing complexity.
Provides simple read-only mode enforcement through startup flags, whereas fine-grained access control systems require complex permission management and per-operation authorization checks.
layered toolset registration with service client abstraction
Medium confidenceThe server uses a layered architecture where InitToolsets function orchestrates the registration of multiple domain-specific toolsets (Pipeline, PullRequest, Repository, ArtifactRegistry, CloudCost, ChaosEngineering, Logs, AIServices, Connectors, Dashboards). Each toolset follows a consistent registration pattern: create an HTTP client with appropriate authentication, instantiate a service client that wraps Harness API operations, create a toolset with individual tools, and add it to a toolset group. Service clients abstract HTTP details and provide business logic, while toolsets expose individual operations as MCP tools with standardized parameter schemas.
Implements a consistent registration pattern across 10+ toolsets where each follows: HTTP client creation → service client instantiation → tool definition → toolset group addition. This pattern is enforced in pkg/harness/tools.go registration functions (lines 125-221), enabling predictable extension points and reducing boilerplate for new toolsets.
Provides organized, domain-specific toolset grouping with consistent registration patterns, whereas generic MCP servers require flat tool lists or custom registration logic for each new capability.
pipeline execution and status monitoring with real-time log streaming
Medium confidenceExposes Harness pipeline operations through a Pipeline toolset that enables triggering pipeline executions, querying execution status, retrieving execution logs, and monitoring execution stages. The implementation wraps Harness Pipeline Service APIs, allowing clients to start pipelines with input variables, poll execution status with stage-level granularity, and stream execution logs in real-time. This enables AI agents to orchestrate CI/CD workflows and provide developers with execution feedback without manual dashboard navigation.
Implements pipeline execution as a toolset that combines execution triggering, status polling, and log retrieval into a cohesive workflow abstraction. The Pipeline service client wraps Harness Pipeline Service APIs with business logic for variable injection and stage-level status tracking, enabling AI agents to reason about pipeline state without understanding Harness API details.
Provides integrated pipeline execution and monitoring through MCP tools, whereas direct Harness API clients require separate calls to trigger, poll, and retrieve logs with manual state management.
pull request and code review integration with repository context
Medium confidenceExposes pull request operations through a PullRequest toolset that enables querying pull requests, retrieving PR details with diff context, posting comments, and managing PR status. The implementation integrates with Harness repository connectors (GitHub, GitLab, Bitbucket) to fetch PR metadata and code changes. This enables AI agents to analyze code changes, provide automated code review feedback, and participate in pull request discussions without leaving the MCP client.
Implements PR operations as a toolset that abstracts multiple Git platform connectors (GitHub, GitLab, Bitbucket) through a unified Harness Repository Service interface. The PullRequest service client translates MCP tool calls into connector-specific API calls, enabling AI agents to work with PRs across different Git platforms using identical tool signatures.
Provides unified PR operations across multiple Git platforms through Harness connectors, whereas platform-specific MCP servers require separate implementations for GitHub, GitLab, and Bitbucket.
repository browsing and file retrieval with connector abstraction
Medium confidenceExposes repository operations through a Repository toolset that enables listing repositories, browsing directory structures, retrieving file contents, and querying commit history. The implementation uses Harness repository connectors to abstract Git platform differences, allowing clients to browse code without direct Git API knowledge. This enables AI agents to analyze codebases, retrieve configuration files, and understand repository structure for context-aware operations.
Implements repository operations through Harness repository connectors, which abstract Git platform differences and provide unified file retrieval, directory browsing, and commit history APIs. The Repository service client translates MCP tool calls into connector-specific operations, enabling platform-agnostic codebase access.
Provides unified repository access across GitHub, GitLab, and Bitbucket through Harness connectors, whereas direct Git API clients require platform-specific implementations and credential management.
artifact registry querying and artifact retrieval with multi-registry support
Medium confidenceExposes artifact operations through an ArtifactRegistry toolset that enables querying artifact registries (Docker, Artifactory, ECR, GCR, Nexus), listing artifacts, retrieving artifact metadata, and downloading artifacts. The implementation uses Harness artifact connector abstraction to support multiple registry types with unified APIs. This enables AI agents to discover available artifacts, retrieve artifact information for deployment decisions, and automate artifact-driven workflows.
Implements artifact registry operations through Harness artifact connectors that abstract multiple registry types (Docker, Artifactory, ECR, GCR, Nexus) into unified APIs. The ArtifactRegistry service client translates MCP tool calls into registry-specific operations, enabling AI agents to query and retrieve artifacts across different registries using identical tool signatures.
Provides unified artifact registry access across Docker, Artifactory, ECR, GCR, and Nexus through Harness connectors, whereas direct registry API clients require separate implementations and credential management for each registry type.
cloud cost analysis and optimization recommendations with multi-cloud support
Medium confidenceExposes cloud cost operations through a CloudCost toolset that enables querying cloud spending, analyzing cost trends, retrieving cost anomalies, and generating optimization recommendations. The implementation integrates with Harness Cloud Cost Management service to aggregate costs across AWS, Azure, and GCP, providing unified cost visibility and AI-driven optimization insights. This enables AI agents to analyze spending patterns, identify cost anomalies, and recommend resource optimization strategies.
Implements cloud cost operations through Harness Cloud Cost Management service, which aggregates costs across AWS, Azure, and GCP and applies statistical anomaly detection and optimization algorithms. The CloudCost service client exposes cost analysis and recommendation capabilities as MCP tools, enabling AI agents to reason about cloud spending without understanding cloud provider APIs.
Provides unified cloud cost analysis and optimization across AWS, Azure, and GCP through Harness CCM, whereas direct cloud provider APIs require separate implementations and cross-cloud aggregation logic.
chaos engineering experiment execution and result analysis
Medium confidenceExposes chaos engineering operations through a ChaosEngineering toolset that enables creating chaos experiments, executing experiments, monitoring experiment progress, and analyzing experiment results. The implementation integrates with Harness Chaos Engineering service to define fault injection scenarios, execute them against target systems, and collect metrics on system resilience. This enables AI agents to orchestrate chaos testing workflows, analyze resilience patterns, and recommend system hardening strategies.
Implements chaos engineering operations through Harness Chaos Engineering service, which provides fault injection capabilities, experiment orchestration, and result analysis. The ChaosEngineering service client exposes experiment execution and monitoring as MCP tools, enabling AI agents to orchestrate resilience testing without understanding chaos engineering frameworks.
Provides integrated chaos experiment execution and result analysis through Harness, whereas direct chaos engineering tools (Gremlin, Chaos Mesh) require separate experiment definition and result aggregation logic.
log aggregation and analysis with multi-source querying
Medium confidenceExposes log operations through a Logs toolset that enables querying logs from multiple sources (application logs, infrastructure logs, deployment logs), filtering by time range and criteria, and analyzing log patterns. The implementation integrates with Harness Logs service to aggregate logs from various sources and provide unified querying and analysis capabilities. This enables AI agents to investigate issues, analyze error patterns, and correlate logs across multiple systems.
Implements log operations through Harness Logs service, which aggregates logs from multiple sources and provides unified querying and analysis. The Logs service client exposes log retrieval and analysis as MCP tools, enabling AI agents to investigate issues without understanding individual log source APIs.
Provides unified log querying and analysis across multiple sources through Harness, whereas direct log aggregation tools (ELK, Splunk) require separate query syntax and result aggregation logic.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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@z_ai/mcp-server
MCP Server for Z.AI - A Model Context Protocol server that provides AI capabilities
Best For
- ✓Teams using Claude Desktop, VS Code, Cursor, or Windsurf who want native Harness integration
- ✓Organizations building AI agents that need standardized access to CI/CD and deployment platforms
- ✓Developers migrating from REST API clients to MCP-compatible tooling
- ✓Organizations with both external developer tools and internal microservice architectures
- ✓Teams requiring audit trails and service-specific credential isolation
- ✓Harness platform operators managing multi-tenant or hybrid deployment scenarios
- ✓Harness internal development teams building AI-powered platform features
- ✓Internal microservices requiring AI capabilities for code generation or analysis
Known Limitations
- ⚠MCP protocol overhead adds ~50-100ms per request due to JSON-RPC serialization and stdio communication
- ⚠Client must support MCP protocol — older IDE versions or custom tools require MCP implementation
- ⚠No persistent connection pooling — each tool invocation establishes new HTTP connection to Harness backend
- ⚠Limited to tools exposed via MCP toolset registration — not all Harness APIs are automatically available
- ⚠API key mode requires clients to manage and pass credentials — no built-in credential rotation or expiration enforcement
- ⚠JWT mode requires pre-shared service secrets — compromised secrets affect all internal services using that secret
Requirements
Input / Output
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** - Access and interact with Harness platform data, including pipelines, repositories, logs, and artifact registries.
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