Add DocGen tool with comprehensive documentation generation capabilities (#109)
* WIP: new workflow architecture * WIP: further improvements and cleanup * WIP: cleanup and docks, replace old tool with new * WIP: cleanup and docks, replace old tool with new * WIP: new planner implementation using workflow * WIP: precommit tool working as a workflow instead of a basic tool Support for passing False to use_assistant_model to skip external models completely and use Claude only * WIP: precommit workflow version swapped with old * WIP: codereview * WIP: replaced codereview * WIP: replaced codereview * WIP: replaced refactor * WIP: workflow for thinkdeep * WIP: ensure files get embedded correctly * WIP: thinkdeep replaced with workflow version * WIP: improved messaging when an external model's response is received * WIP: analyze tool swapped * WIP: updated tests * Extract only the content when building history * Use "relevant_files" for workflow tools only * WIP: updated tests * Extract only the content when building history * Use "relevant_files" for workflow tools only * WIP: fixed get_completion_next_steps_message missing param * Fixed tests Request for files consistently * Fixed tests Request for files consistently * Fixed tests * New testgen workflow tool Updated docs * Swap testgen workflow * Fix CI test failures by excluding API-dependent tests - Update GitHub Actions workflow to exclude simulation tests that require API keys - Fix collaboration tests to properly mock workflow tool expert analysis calls - Update test assertions to handle new workflow tool response format - Ensure unit tests run without external API dependencies in CI 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com> * WIP - Update tests to match new tools * WIP - Update tests to match new tools * WIP - Update tests to match new tools * Should help with https://github.com/BeehiveInnovations/zen-mcp-server/issues/97 Clear python cache when running script: https://github.com/BeehiveInnovations/zen-mcp-server/issues/96 Improved retry error logging Cleanup * WIP - chat tool using new architecture and improved code sharing * Removed todo * Removed todo * Cleanup old name * Tweak wordings * Tweak wordings Migrate old tests * Support for Flash 2.0 and Flash Lite 2.0 * Support for Flash 2.0 and Flash Lite 2.0 * Support for Flash 2.0 and Flash Lite 2.0 Fixed test * Improved consensus to use the workflow base class * Improved consensus to use the workflow base class * Allow images * Allow images * Replaced old consensus tool * Cleanup tests * Tests for prompt size * New tool: docgen Tests for prompt size Fixes: https://github.com/BeehiveInnovations/zen-mcp-server/issues/107 Use available token size limits: https://github.com/BeehiveInnovations/zen-mcp-server/issues/105 * Improved docgen prompt Exclude TestGen from pytest inclusion * Updated errors * Lint * DocGen instructed not to fix bugs, surface them and stick to d * WIP * Stop claude from being lazy and only documenting a small handful * More style rules --------- Co-authored-by: Claude <noreply@anthropic.com>
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44
config.py
44
config.py
@@ -14,9 +14,9 @@ import os
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# These values are used in server responses and for tracking releases
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# IMPORTANT: This is the single source of truth for version and author info
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# Semantic versioning: MAJOR.MINOR.PATCH
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__version__ = "5.5.3"
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__version__ = "5.5.5"
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# Last update date in ISO format
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__updated__ = "2025-06-21"
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__updated__ = "2025-06-22"
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# Primary maintainer
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__author__ = "Fahad Gilani"
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@@ -82,13 +82,16 @@ DEFAULT_CONSENSUS_MAX_INSTANCES_PER_COMBINATION = 2
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# ↑ ↑
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# │ │
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# MCP transport Internal processing
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# (25K token limit) (No MCP limit - can be 1M+ tokens)
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# (token limit from MAX_MCP_OUTPUT_TOKENS) (No MCP limit - can be 1M+ tokens)
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#
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# MCP_PROMPT_SIZE_LIMIT: Maximum character size for USER INPUT crossing MCP transport
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# The MCP protocol has a combined request+response limit of ~25K tokens total.
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# The MCP protocol has a combined request+response limit controlled by MAX_MCP_OUTPUT_TOKENS.
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# To ensure adequate space for MCP Server → Claude CLI responses, we limit user input
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# to 50K characters (roughly ~10-12K tokens). Larger user prompts must be sent
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# as prompt.txt files to bypass MCP's transport constraints.
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# to roughly 60% of the total token budget converted to characters. Larger user prompts
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# must be sent as prompt.txt files to bypass MCP's transport constraints.
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#
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# Token to character conversion ratio: ~4 characters per token (average for code/text)
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# Default allocation: 60% of tokens for input, 40% for response
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#
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# What IS limited by this constant:
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# - request.prompt field content (user input from Claude CLI)
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@@ -104,7 +107,34 @@ DEFAULT_CONSENSUS_MAX_INSTANCES_PER_COMBINATION = 2
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#
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# This ensures MCP transport stays within protocol limits while allowing internal
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# processing to use full model context windows (200K-1M+ tokens).
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MCP_PROMPT_SIZE_LIMIT = 50_000 # 50K characters (user input only)
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def _calculate_mcp_prompt_limit() -> int:
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"""
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Calculate MCP prompt size limit based on MAX_MCP_OUTPUT_TOKENS environment variable.
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Returns:
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Maximum character count for user input prompts
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"""
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# Check for Claude's MAX_MCP_OUTPUT_TOKENS environment variable
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max_tokens_str = os.getenv("MAX_MCP_OUTPUT_TOKENS")
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if max_tokens_str:
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try:
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max_tokens = int(max_tokens_str)
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# Allocate 60% of tokens for input, convert to characters (~4 chars per token)
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input_token_budget = int(max_tokens * 0.6)
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character_limit = input_token_budget * 4
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return character_limit
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except (ValueError, TypeError):
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# Fall back to default if MAX_MCP_OUTPUT_TOKENS is not a valid integer
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pass
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# Default fallback: 60,000 characters (equivalent to ~15k tokens input of 25k total)
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return 60_000
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MCP_PROMPT_SIZE_LIMIT = _calculate_mcp_prompt_limit()
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# Threading configuration
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# Simple in-memory conversation threading for stateless MCP environment
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