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microsoft_docs_fetch

Fetch and convert a Microsoft Learn documentation webpage to markdown format. This tool retrieves the latest complete content of Microsoft documentation webpages including Azure, .NET, Microsoft 365, and other Microsoft technologies.

When to Use This Tool

  • When search results provide incomplete information or truncated content

  • When you need complete step-by-step procedures or tutorials

  • When you need troubleshooting sections, prerequisites, or detailed explanations

  • When search results reference a specific page that seems highly relevant

  • For comprehensive guides that require full context

Usage Pattern

Use this tool AFTER microsoft_docs_search when you identify specific high-value pages that need complete content. The search tool gives you an overview; this tool gives you the complete picture.

URL Requirements

  • The URL must be a valid HTML documentation webpage from the microsoft.com domain

  • Binary files (PDF, DOCX, images, etc.) are not supported

Output Format

markdown with headings, code blocks, tables, and links preserved.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesURL of the Microsoft documentation page to read

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.6/5.0
Behavior4/5

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

No annotations exist, so the description carries the burden. It mentions retrieving 'latest complete content', preserving markdown elements, and URL constraints. However, it doesn't address error handling, rate limits, or dynamic page behavior, leaving some transparency gaps.

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?

Well-structured with headers and front-loaded purpose. Some redundancy among bullet points in 'When to Use' (e.g., related use cases), but each section serves a purpose. Slightly verbose for a one-parameter tool.

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 simple single-parameter schema and absence of output schema, the description provides comprehensive context: output format, URL requirements, usage ordering, and exclusion criteria. It fully equips an agent to select and invoke the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% for the single URL parameter, but the description adds meaningful constraints: valid HTML from microsoft.com domain and binary files unsupported. This enhances schema semantics.

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 fetches and converts Microsoft Learn documentation pages to markdown, naming specific technology areas (Azure, .NET, Microsoft 365). It distinguishes itself from sibling microsoft_docs_search by focusing on complete page retrieval rather than search.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

An explicit 'When to Use This Tool' section lists conditions, and 'Usage Pattern' directs to use after microsoft_docs_search. It also states unsupported binary URLs, providing clear exclusions.

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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TDQS

C2.4/5.0
Disambiguation2/5

Several tools have overlapping or ambiguous purposes that could confuse an agent. For example, analyze_code, analyze_patterns, and analyze_design_patterns all involve code analysis with unclear boundaries, while check_deceptive_patterns and check_placeholders seem like subsets of analyze_code. The NPM tools form a coherent group but are distinct from the rest, creating a fragmented toolset.

Naming Consistency2/5

Naming conventions are highly inconsistent across the toolset. Some tools use snake_case (e.g., analyze_code, execute_code), others use camelCase (e.g., npmAlternatives, npmChangelogAnalysis), and there are mixed styles like query-docs with hyphens. The NPM tools follow a consistent npmPrefix pattern internally, but this is not applied to other tools, leading to overall chaos.

Tool Count2/5

With 39 tools, this server is overloaded for a 'DevTools Collection' scope. The count feels excessive, as many tools could be consolidated (e.g., multiple analysis tools) or logically grouped. While the NPM tools are numerous but focused, the overall set lacks cohesion, making it cumbersome for an agent to navigate and select appropriate tools efficiently.

Completeness3/5

The toolset covers a broad range of development tasks, including code analysis, execution, documentation, and package management, but there are notable gaps. For example, there is no tool for code generation or refactoring, and the Microsoft and NPM tools are well-covered but isolated from other functionalities. The surface is extensive but not fully integrated, with some dead ends in workflow transitions.

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