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Yandex Tracker MCP

issue_get_worklogs

Retrieve worklogs for specific issues in Yandex Tracker using their IDs. Input issue IDs in the format '-' to fetch detailed worklog data.

Instructions

Get worklogs of a Yandex Tracker issue by its id

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
issue_idsYesMultiple Issue IDs. Each issue id is in the format '<project>-<id>', like 'SOMEPROJECT-1'

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • MCP tool handler and registration for 'issue_get_worklogs'. Supports multiple issue IDs, performs authorization checks, and delegates to the issues service to fetch worklogs.
    @mcp.tool(description="Get worklogs of a Yandex Tracker issue by its id")
    async def issue_get_worklogs(
        ctx: Context[Any, AppContext],
        issue_ids: IssueIDs,
    ) -> dict[str, list[Worklog]]:
        for issue_id in issue_ids:
            check_issue_id(settings, issue_id)
    
        result: dict[str, Any] = {}
        for issue_id in issue_ids:
            worklogs = (
                await ctx.request_context.lifespan_context.issues.issue_get_worklogs(
                    issue_id,
                    auth=get_yandex_auth(ctx),
                )
            )
            result[issue_id] = worklogs or []
    
        return result
  • Core implementation of issue_get_worklogs in the TrackerClient class, making HTTP GET request to Yandex Tracker API endpoint /v3/issues/{issue_id}/worklog and parsing the response.
    async def issue_get_worklogs(
        self, issue_id: str, *, auth: YandexAuth | None = None
    ) -> list[Worklog]:
        async with self._session.get(
            f"v3/issues/{issue_id}/worklog", headers=await self._build_headers(auth)
        ) as response:
            if response.status == 404:
                raise IssueNotFound(issue_id)
            response.raise_for_status()
            return WorklogList.model_validate_json(await response.read()).root
  • Caching wrapper for issue_get_worklogs that caches the result using aiocache.
    async def issue_get_worklogs(
        self, issue_id: str, *, auth: YandexAuth | None = None
    ) -> list[Worklog]:
        return await self._original.issue_get_worklogs(issue_id, auth=auth)
  • Protocol (interface) definition for the issue_get_worklogs method used across clients.
    async def issue_get_worklogs(
        self, issue_id: str, *, auth: YandexAuth | None = None
    ) -> list[Worklog]: ...
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 'gets' worklogs, implying a read-only operation, but doesn't cover aspects like authentication needs, rate limits, error handling, or what the output contains (though an output schema exists). For a tool with zero annotation coverage, this is insufficient to fully inform the agent.

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 with the core action and resource, making it easy to parse. Every word earns its place, achieving optimal conciseness.

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

Completeness4/5

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

Given the tool's moderate complexity (single parameter, read operation), 100% schema coverage, and the presence of an output schema, the description is reasonably complete. It clearly defines the tool's scope (worklogs for issues by ID). However, it lacks usage guidelines and behavioral details, which slightly reduces completeness for an agent's decision-making.

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?

Schema description coverage is 100%, so the input schema fully documents the single parameter 'issue_ids' with its format and array type. The description adds no additional parameter semantics beyond implying the tool uses issue IDs. This meets the baseline of 3, as the schema handles 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 ('Get worklogs') and resource ('of a Yandex Tracker issue'), specifying the target by 'its id'. It distinguishes from siblings like issue_get_comments or issue_get_attachments by focusing on worklogs. However, it doesn't explicitly differentiate from all siblings (e.g., issue_get might include worklogs), so it's not a perfect 5.

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 if worklogs are available through other tools like issue_get or issues_find, nor does it specify prerequisites or contexts for usage. This lack of comparative guidance limits its utility for an AI agent.

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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