Skip to main content
Glama
omniwaifu

Pydantic AI Documentation Server

by omniwaifu

get_changelog_content

Retrieve parsed changelog content from Pydantic-AI documentation to access version history and release details for specific files.

Instructions

Retrieves the parsed content of a specific changelog file. The path should be relative to the Pydantic-AI documentation root's 'docs' directory (e.g., 'history/0.1.0.md').

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYes

Implementation Reference

  • The handler implementation for the get_changelog_content tool, which reads a changelog file from the documentation repository.
    @app.tool()
    async def get_changelog_content(path: str) -> Optional[ChangelogContent]:
        """
        Retrieves the parsed content of a specific changelog file.
        The path should be relative to the Pydantic-AI documentation root's 'docs' directory (e.g., 'history/0.1.0.md').
        """
        logger.info(f"get_changelog_content called for path: {path}")
        try:
            repo_root = get_repo_path()
            docs_dir = repo_root / "docs"
            changelog_file_path = (docs_dir / path).resolve()
    
            if not str(changelog_file_path).startswith(str(docs_dir.resolve())):
                logger.warning(
                    f"Path traversal attempt or invalid path for get_changelog_content: {path}"
                )
                return None
    
            if not changelog_file_path.is_file():
                logger.info(
                    f"Changelog file not found or not a file at path: {path} (resolved: {changelog_file_path})"
                )
                return None
    
            content = changelog_file_path.read_text(encoding="utf-8")
            version = Path(path).stem
    
            return ChangelogContent(
                path=path,
                version=version,
                content=content,
                release_date=None,
            )
    
        except Exception as e:
            logger.error(
                f"Error in get_changelog_content for path '{path}': {e}", exc_info=True
            )
            return None
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 mentions 'retrieves' and 'parsed content', implying a read-only operation, but doesn't specify details like whether it requires authentication, handles errors (e.g., invalid paths), returns structured data, or has rate limits. This leaves significant gaps for a tool with no annotation coverage.

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 appropriately sized with two sentences that are front-loaded and waste-free. The first sentence states the core purpose, and the second provides essential parameter context, making it efficient and well-structured.

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 complexity (a read operation with one parameter), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what 'parsed content' entails (e.g., text, structured data), error handling, or how it differs from sibling tools, leaving the agent with insufficient context for reliable 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 description adds meaning to the single parameter 'path' by specifying it should be 'relative to the Pydantic-AI documentation root's 'docs' directory' and gives an example ('history/0.1.0.md'), which is helpful since schema description coverage is 0%. However, it doesn't fully compensate by detailing constraints like allowed file formats or path validation rules, keeping it at a baseline level.

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 'retrieves' and the resource 'parsed content of a specific changelog file', making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_document_by_path' or 'list_available_changelogs', which could handle similar content or listing functions.

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 some context about the path format ('relative to the Pydantic-AI documentation root's 'docs' directory'), but offers no guidance on when to use this tool versus alternatives like 'get_document_by_path' for general documents or 'list_available_changelogs' for listing changelogs. There's no explicit when/when-not or alternative tool recommendations.

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/omniwaifu/pydantic-ai-docs-server'

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