Microsoft Learn MCP

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the read-only nature is covered. The description adds that the tool retrieves the 'LATEST OFFICIAL' snippets, lending some context, but does not disclose other behavioral aspects like pagination, result limits, or error behavior. With annotations in place, the description provides minimal additional transparency.

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

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

The description is well-structured with clear sections ('When to Use', 'Usage Pattern') and front-loaded with a clear opening sentence. It is moderately sized, but contains some redundancy and promotional language ('empower coding capabilities') that could be trimmed. Every section earns its place overall.

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

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

For a search tool with an output schema (not shown) and annotations covering safety, the description is reasonably complete. It explains what to input (query, SDK/method name), how to use optional language, and provides eligible values. It does not mention pagination or result shape, but those may be covered by the output schema. The tool is simple enough that no further detail is needed.

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

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

Schema description coverage is 100%, so the baseline is 3. The description's usage pattern repeats what the schema already says, but it does emphasize that `language` can filter results and improve quality—slight added value. The eligible values list in the description duplicates the schema's list exactly, so no genuinely new meaning is introduced.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Search for code snippets and examples in official Microsoft Learn documentation.' It specifies the resource (Microsoft Learn docs), the action (search/retrieve), and the scope (code samples). The tool name and description distinguish it from sibling tools (microsoft_docs_search, microsoft_docs_fetch) by focusing specifically on code snippets.

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

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

The description provides an explicit 'When to Use This Tool' section with concrete scenarios: when providing Microsoft/Azure code snippets in answers or generating any Microsoft/Azure related code. It also gives a usage pattern via query and optional language. However, it does not explicitly mention alternatives or when not to use this tool vs. siblings, so it falls short of a full 5.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, and the description adds valuable behavioral context: it retrieves the latest complete content, converts to markdown, preserves formatting, and excludes binary files. No contradictions with annotations.

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

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

The description is well-structured with clearly labeled sections (When to Use, Usage Pattern, URL Requirements, Output Format). Each sentence adds value and the information is front-loaded with a concise opening sentence defining the tool's core behavior.

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

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, no output schema) and strong annotations, the description covers all necessary context: purpose, usage timing, executable constraints, and output format. It is fully sufficient for an agent to select and invoke the tool correctly.

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

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

Schema description coverage is 100% for the single url parameter, but the tool description adds meaningful constraints beyond the schema, such as the requirement that the URL be a valid HTML microsoft.com documentation page and that binary files are unsupported. This enriches the parameter semantics without redundancy.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb+resource+outcome: 'Fetch and convert a Microsoft Learn documentation webpage to markdown format.' It clearly distinguishes itself from sibling tools by focusing on fetching full page content versus searching or code sample retrieval.

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

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

The description provides explicit when-to-use criteria, a usage pattern that places the tool after microsoft_docs_search, and URL requirements that define valid inputs. This is strong guidance that tells the agent exactly when and how to use the tool versus alternatives.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is clear. The description adds valuable behavioral details: returns up to 10 chunks, each max 500 tokens, sourced from Microsoft Learn and official sources, and includes title, URL, and self-contained excerpt. This goes beyond annotations by specifying output limits and content format, making agent invocation expectations precise.

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

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

The description is well-structured, leading with the core purpose, then operational details, and ending with a follow-up pattern. It is appropriately sized and front-loaded, though a few phrases like 'high-quality' and 'optimized for fast retrieval' add mild redundancy. Still, every sentence earns its place.

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

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

The tool has a rich output schema (title, URL, excerpt), and while the description does not repeat the schema, it adds context on result count and token limits. The follow-up pattern with fetch tool makes the overall workflow complete. It lacks mention of edge cases (e.g., empty query) but overall covers the essentials for a search tool.

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

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

With 100% schema description coverage, the baseline is 3. The tool description does not add new semantic details about the query parameter; it only repeats the purpose. The parameter is already well-defined in the schema, so the description neither enhances nor obscures meaning.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with 'Search official Microsoft/Azure documentation to find the most relevant and trustworthy content for a user's query,' which names a specific verb (search) and resource (Microsoft/Azure docs). It clearly distinguishes this tool from its siblings by highlighting the search mechanism and the excerpt-based results, with explicit mention of the complimentary fetch tool in the follow-up pattern.

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

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

The description provides explicit guidance: 'Always use this tool to quickly ground your answers' and directs users to microsoft_docs_fetch for full detail, even calling it a 'required step for comprehensive results.' This gives a clear when-to-use and when-to-use-alternative strategy, which is stronger than simply implied usage.

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