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MCP server that provides tools for searching, retrieving, and asking questions about BootstrapBlazor component documentation. Integrates with Git to sync repository documentation and uses AI to provide expert answers about components.
The BootstrapBlazor MCP server provides 4 tools for component documentation and AI-assisted querying. Tool definitions are minimal. While descriptions exist, they are generic and lack the specificity needed for LLM tool selection. Input schemas are present but sparse, most parameters lack granular constraints or validation guidance. Output schemas are not documented. The server is HTTP-based and runs in Docker, making it accessible for deployment, but the tool definitions themselves fall short of production-grade quality. Most parameters are simple strings without enums or format constraints, forcing LLMs to reason about valid inputs without guidance.
Asks an AI expert (if enabled) a question about a specific BootstrapBlazor component based on its API documentation and code samples.
Returns the raw documentation for a component, including API parameters and code samples, without AI processing.
Returns a list of all available BootstrapBlazor components from the documentation.
Searches for BootstrapBlazor components matching a keyword in their documentation.
No documented output schemas. Tools return results but there is no specification of the response structure, field types, or what an LLM should expect. This forces LLMs to guess the shape of returned data, leading to failed field lookups and incorrect data extraction.
Parameter descriptions are minimal and lack constraint guidance. The 'Keyword' parameter in SearchComponentKeyword has only 'The keyword to search for in component documentation', no guidance on minimum length, maximum length, supported characters, or examples of valid keywords. This invites LLM hallucinations.
AskComponentExpert tool description does not clearly explain what 'AI expert' means, whether it requires external API configuration (AiBaseUrl, AiApiKey), or what happens if AI is disabled. The phrase 'if enabled' is vague and does not guide the LLM on when this tool is usable.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 48 | <=2025-11-25 | v2 |
| 2026-03-09 | F | 48 | - | v1 |
No pagination or result limits documented. GetComponentList and SearchComponentKeyword may return large result sets, but there is no mention of pagination parameters, result limits, or how LLMs should handle thousands of components. Returning all results risks context window explosion.
Error handling is not documented. No guidance on what happens when a component is not found, the keyword matches nothing, or the AI service fails. LLMs need recovery paths, 'Try search_component_keyword() first' or 'Component not found; available: ComponentA, ComponentB'.
Tool names use PascalCase (GetComponentList, SearchComponentKeyword) instead of snake_case (get_component_list, search_component_keyword). MCP convention and LLM parsing benefits from snake_case naming.
ComponentName parameter in GetComponentDocs and AskComponentExpert tools lacks guidance on valid formats. Is it case-sensitive? Does it accept partial names? Must it exactly match documentation? LLMs will guess and fail.