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Rudra-ravi

Wikipedia MCP Server

by Rudra-ravi

get_sections

Read-onlyIdempotent

Retrieve the list of sections for a Wikipedia article by providing its title.

Instructions

Get the sections of a Wikipedia article.

Returns a dictionary with the article title and list of sections.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleYes
sectionsYes

Implementation Reference

  • The 'get_sections' tool handler function registered via @register_tool decorator. It takes a 'title' string parameter, calls wikipedia_client.get_sections(title), and returns the result.
    @register_tool("get_sections", model_output_schema(SectionsResponse))
    def get_sections(title: str):
        """
        Get the sections of a Wikipedia article.
    
        Returns a dictionary with the article title and list of sections.
        """
        logger.info("Tool: Getting sections for: %s", title)
        sections = wikipedia_client.get_sections(title)
        return {"title": title, "sections": sections}
  • The WikipediaClient.get_sections() method that fetches the Wikipedia page and extracts its sections using _extract_sections().
    def get_sections(self, title: str) -> List[Dict[str, Any]]:
        """
        Get the sections of a Wikipedia article.
    
        Args:
            title: The title of the Wikipedia article.
    
        Returns:
            A list of sections.
        """
        try:
            page = self.wiki.page(title)
    
            if not page.exists():
                return []
    
            return self._extract_sections(page.sections)
        except Exception as e:
            logger.error(f"Error getting Wikipedia sections: {e}")
            return []
  • The _extract_sections() helper method that recursively extracts section data (title, level, text, subsections) from Wikipedia section objects.
    def _extract_sections(self, sections, level=0) -> List[Dict[str, Any]]:
        """
        Extract sections recursively.
    
        Args:
            sections: The sections to extract.
            level: The current section level.
    
        Returns:
            A list of sections.
        """
        result = []
        for section in sections:
            section_data = {
                "title": section.title,
                "level": level,
                "text": section.text,
                "sections": self._extract_sections(section.sections, level + 1),
            }
            result.append(section_data)
        return result
  • The SectionsResponse model class defining the output schema (title: str, sections: list[dict]) for the get_sections tool.
    class SectionsResponse(MCPBaseModel):
        title: str
        sections: list[dict[str, Any]]
  • The register_tool() decorator that registers the function with FastMCP server under both the original name and 'wikipedia_' prefix, using the output schema.
    def register_tool(name: str, output_schema: dict[str, Any]):
        def decorator(func):
            server.tool(
                func,
                name=name,
                annotations=_READ_ONLY_TOOL_ANNOTATIONS,
                output_schema=output_schema,
            )
            server.tool(
                func,
                name=f"wikipedia_{name}",
                annotations=_READ_ONLY_TOOL_ANNOTATIONS,
                output_schema=output_schema,
            )
            return func

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv2.0.1
  2. Removedv1.5.8
  3. Changed1 schema field changedv1.0.0
    • removedInput schema / properties / title / title
      Removed value: -"Title"
  4. First observed

TDQS

A4.1/5.0
Behavior3/5

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

Annotations already indicate a safe read-only operation. The description adds the return structure (a dictionary with title and sections), which is helpful but largely covered by the output schema. No additional behaviors like error handling are disclosed, so a mid-range score is appropriate.

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 two short sentences, front-loaded with the action, and contains zero fluff. It is appropriately sized.

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 tool's simplicity, one parameter, annotations covering safety, and an output schema, the description is complete enough for an agent to use it. It clearly defines the operation and result shape.

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 provides no description for the 'title' parameter (0% coverage), but the description clarifies it's the Wikipedia article title. This adds essential meaning, though no further constraints or format details are given.

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 retrieves sections of a Wikipedia article. It uses a specific verb and resource, distinguishing it from siblings like get_article and get_summary.

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 provides a clear use case (get sections of a Wikipedia article) and implicitly indicates when to use it. It does not explicitly mention alternatives or when not to use, but the context is unambiguous.

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