Skip to main content
Glama

Export Work Items

sdd_export_work_items
Read-onlyIdempotent

Transforms TASKS.md into structured work items for GitHub, Jira, or Azure Boards. Preserves task traceability and handles subtasks.

Instructions

Transforms TASKS.md into platform-specific work item payloads: GitHub Issues {title, body, labels}, Jira {fields: {project.key, summary, description, issuetype}} (project_key required), or Azure Boards {work_item_type, fields: System.Title/System.Description/System.AreaPath/System.IterationPath}. Honors include_subtasks and preserves REQ/task traceability in every shape. Returns routing_instructions for the AI client to create items via the appropriate MCP server.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
platformYesTarget platform for work item export
spec_dirNoSpec directory path (relative to workspace root).specs
area_pathNoAzure DevOps area path (optional for Azure Boards)
project_keyNoJira project key (required for Jira platform)
feature_numberNoFeature number (zero-padded, e.g. '001')001
iteration_pathNoAzure DevOps iteration path (optional for Azure Boards)
include_subtasksNoInclude subtasks for each top-level work item
Behavior5/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds valuable behavioral context: it honors include_subtasks, preserves traceability, and importantly returns routing_instructions rather than creating items directly. This goes beyond annotations without contradiction.

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 concise with three sentences. The first sentence immediately states the primary functionality and output shapes, front-loading critical information. No redundant or unnecessary words.

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

Completeness5/5

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

Given the complexity (three platforms with different structures), the description provides complete context: input source, output shapes for each platform, key parameter behavior, and return value. Since no output schema exists, the description adequately explains what the tool returns (routing_instructions).

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3. The description adds value by explicitly noting that 'project_key' is required for Jira (even though not marked required in schema) and that 'include_subtasks' is honored. This provides necessary linkage between parameters and platform-specific behavior.

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

Purpose5/5

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

The description clearly states the tool's action (transforms TASKS.md), the specific outputs (platform-specific payloads for GitHub, Jira, Azure Boards), and distinguishes it from sibling tools (no other export tool). The verb 'transforms' and resource 'TASKS.md' are precise.

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

Usage Guidelines4/5

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

The description implicitly indicates when to use the tool (when needing to export work items to one of the listed platforms). It provides detailed output shapes, but does not explicitly exclude scenarios or mention alternatives. However, given no sibling tools perform a similar function, the guidance is clear.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/paulasilvatech/specky'

If you have feedback or need assistance with the MCP directory API, please join our Discord server