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mimeCam

DevBrain

by mimeCam

read_full_article

Extract the full content of any article using its URL. Designed for developers, this tool helps retrieve complete articles from high-quality, developer-curated blogs and newsletters.

Instructions

Returns the full content of an article identified by its URL.

Args: url: The URL of the article to read.

Returns: str: The full content of the article or an error message.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • Registers the 'read_full_article' tool using the FastMCP @tool decorator on the function definition.
    @mcp_server.tool
  • Implements the core logic of the read_full_article tool: enforces API token, constructs POST request to the DevBrain API endpoint for reading the full article by URL, returns the content or an error message.
    def read_full_article(url: str) -> str:
        """Returns the full content of an article identified by its URL.
    
        Args:
            url: The URL of the article to read.
    
        Returns:
            str: The full content of the article or an error message.
        """
        token_error = _enforce_token()
        if token_error:
            return token_error
    
        api_url = f"{api_host_base}/newsletter/article/read"
        headers = {
            "authorization": f"Bearer {_token}",
            "content-type": "application/json",
        }
        data = {"url": url}
        try:
            response = requests.post(api_url, headers=headers, json=data)
            response.raise_for_status()  # Raise an HTTPError for bad responses (4xx or 5xx)
            return response.text
        except requests.exceptions.RequestException:
            return "Full article for the given URL is not available at this time. API error occurred - DevBrain knowledge base service is temporarily unavailable."
  • Input/output schema defined by function signature (url: str -> str) and docstring describing parameters and return value for MCP tool validation.
    def read_full_article(url: str) -> str:
        """Returns the full content of an article identified by its URL.
    
        Args:
            url: The URL of the article to read.
    
        Returns:
            str: The full content of the article or an error message.
        """
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions that the tool returns content or an error message, but does not cover critical aspects like rate limits, authentication needs, network behavior, or error handling specifics. This is a significant gap for a tool that likely involves external requests.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose, followed by structured sections for args and returns. It is efficient with minimal waste, though the 'Returns' section could be more concise by integrating with the main description, as the output schema exists.

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

Completeness3/5

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

Given the tool's complexity (external URL fetching), lack of annotations, and an output schema that only specifies a string type, the description is moderately complete. It covers the basic operation but omits important contextual details like error conditions, performance, or dependencies, which are crucial for effective tool use.

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 schema description coverage is 0%, but the description adds basic semantics by explaining that 'url' is 'The URL of the article to read.' This clarifies the parameter's purpose beyond the schema's type information. However, it does not provide details on URL format, validation, or examples, leaving room for improvement.

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 tool's purpose: 'Returns the full content of an article identified by its URL.' It specifies the verb ('Returns') and resource ('full content of an article'), making it easy to understand. However, it does not explicitly differentiate from the sibling tool 'retrieve_knowledge', which could have similar functions, so it misses the top score.

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, such as the sibling 'retrieve_knowledge'. It lacks context on prerequisites, constraints, or scenarios where this tool is preferred, leaving the agent to infer usage based on the purpose 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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