Sourcerer MCP has 5 tools with clear naming (verb-first: semantic_search, find_similar_chunks, get_chunk_code, index_workspace, get_index_status). All tools have descriptions and input schemas are defined via the mark3labs/mcp-go framework. However, critical issues limit quality: (1) Parameter descriptions are minimal or missing, file_types in semantic_search has a 19-char description ('Filter by file type(s)'), below the 20-char floor for credit. (2) Output schemas are entirely undocumented, no LLM can infer what fields semantic_search or find_similar_chunks return, breaking the tool-chaining pattern. (3) Error handling is generic, CallToolResult returns plain text error strings with no recovery guidance (e.g., 'Search failed: %v'). (4) No input validation descriptions, semantic_search accepts free-form query strings with no constraints stated. (5) The instructions are well-written (detailed guidance on usage), but this is a prompt, not a schema remedy. Per-tool scores average 52 across 5 tools, with semantic_search and find_similar_chunks scoring lowest due to undocumented outputs.

• Define output schemas for all tools. semantic_search and find_similar_chunks must document return type as either structured JSON (array of objects with fields like chunk_id, file, lines, relevance_score) or formatted text with clear field delimiters. Include examples in code comments or test cases.
• Expand parameter descriptions to 50 - 150 characters with actionable detail. For semantic_search.file_types, replace 'Filter by file type(s)' with 'Restrict search scope: src (source code), docs (documentation), tests (test files). Defaults to ["src", "docs"]. Omit to search all.'
• Add enum constraint to file_types. The schema should include 'enum: ["src", "docs", "tests"]' rather than relying on instructions.
• Implement typed error responses. Return structured errors with fields like {error: string, type: 'not_found'|'invalid_query'|'timeout', suggestion?: string} instead of plain text. Example: {error: 'No results found for query', type: 'not_found', suggestion: 'Try a broader search term or add file_types parameter.'}
• Document output schemas in code comments or a separate schema file. For get_chunk_code, specify: returns string (concatenated code chunks) OR object {chunks: [{id: string, code: string, file: string, lines: string}], total: number}. Consistency matters for LLM reasoning.
• Add precondition documentation. Explain that index_workspace requires the workspace to exist and be readable, and that find_similar_chunks requires a prior semantic_search or index operation to populate embeddings.