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get_articles_by_tag

Fetch Dev.to articles filtered by specific tags to find relevant content quickly. Specify a tag to retrieve matching articles from the platform.

Instructions

Get articles by tag from Dev.to

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagYes

Implementation Reference

  • server.py:34-38 (handler)
    The main handler function for the 'get_articles_by_tag' tool. It fetches articles from the Dev.to API using the provided tag as a parameter and formats the first 10 results for output. Registered via @mcp.tool() decorator.
    @mcp.tool()
    async def get_articles_by_tag(tag: str) -> str:
        """Get articles by tag from Dev.to"""
        articles = await fetch_from_api("/articles", params={"tag": tag})
        return format_articles(articles[:10])  # Limiting to 10 for readability
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 the action ('Get articles') but does not describe traits such as whether it's read-only, requires authentication, has rate limits, returns paginated results, or what format the output takes. This leaves significant gaps for a tool that likely interacts with an external API.

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 with zero waste. It is appropriately sized and front-loaded, clearly stating the tool's purpose without unnecessary elaboration, making it easy for an agent to parse quickly.

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 tool's complexity (interacting with Dev.to API), lack of annotations, no output schema, and low parameter coverage, the description is incomplete. It fails to address behavioral aspects, output format, error handling, or usage context, leaving the agent with insufficient information to invoke the tool effectively beyond basic purpose.

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 mentions 'by tag', which aligns with the single parameter 'tag' in the schema. However, with 0% schema description coverage, the schema provides no details about the parameter. The description adds minimal semantic value by indicating the parameter's role but lacks specifics like tag format, case sensitivity, or examples, resulting in a baseline score due to incomplete compensation.

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 ('Get') and resource ('articles by tag from Dev.to'), making the purpose understandable. It distinguishes from some siblings like 'get_article_by_id' or 'get_articles_by_username' by specifying the tag-based filtering, though it doesn't explicitly differentiate from 'search_articles' which might also support tag filtering.

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?

No guidance is provided on when to use this tool versus alternatives like 'search_articles' or 'get_latest_articles'. The description implies usage for tag-based retrieval but lacks explicit context, prerequisites, or exclusions, leaving the agent to infer usage scenarios.

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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