{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hn-47209377","slug":"mcp-file-tools-silently-eat-your-context-window-i-","name":"MCP file tools silently eat your context window.I built one that doesnt","type":"mcp","url":"https://github.com/ckanthony/Chisel","page_url":"https://unfragile.ai/mcp-file-tools-silently-eat-your-context-window-i-","categories":["mcp-servers"],"tags":["hackernews","show-hn"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hn-47209377__cap_0","uri":"capability://tool.use.integration.context.aware.file.reading.with.token.budgeting","name":"context-aware file reading with token budgeting","description":"Implements file reading operations that track and report token consumption before returning content, using a token counter (likely tiktoken-based) to estimate context window impact. Unlike standard MCP file tools that silently consume context, this capability exposes token costs upfront, allowing clients to make informed decisions about whether to read files or use alternative strategies like summarization or chunking.","intents":["I need to read files into my LLM context but want to know the token cost before committing","I want to avoid unexpectedly exhausting my context window when working with large codebases","I need to make trade-off decisions between reading full files vs summaries based on actual token counts"],"best_for":["LLM application developers building agents that interact with file systems","teams managing context-constrained workflows with Claude, GPT-4, or other token-limited models","developers building MCP servers who want transparent resource accounting"],"limitations":["Token estimation accuracy depends on tokenizer choice — may differ from actual model tokenization by 5-15%","No built-in caching of token counts across repeated reads — recalculates on each operation","Requires explicit token budget configuration per session; no automatic budget enforcement","Does not handle multi-byte character encoding edge cases that some tokenizers struggle with"],"requires":["MCP client implementation (Claude Desktop, custom MCP client, or compatible tool)","Python 3.8+ or Node.js 16+ depending on implementation","Token counter library (tiktoken for OpenAI models, or equivalent for other providers)"],"input_types":["file paths (string)","token budget parameters (integer)","optional encoding specification (utf-8, ascii, etc.)"],"output_types":["file content (string)","token count metadata (integer)","cost breakdown (structured data with line count, estimated tokens, budget remaining)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47209377__cap_1","uri":"capability://tool.use.integration.selective.file.chunking.with.token.aware.boundaries","name":"selective file chunking with token-aware boundaries","description":"Provides file reading strategies that split large files into token-bounded chunks rather than returning entire files, using token counts to determine chunk boundaries instead of arbitrary line counts. The implementation likely uses a sliding window approach that respects semantic boundaries (e.g., function/class definitions) while staying within token budgets, allowing clients to incrementally load only the portions of files they need.","intents":["I need to read only the relevant parts of a large file without loading the entire file into context","I want chunks sized to fit within my remaining token budget, not arbitrary line limits","I need to process large codebases incrementally, loading more context only when necessary"],"best_for":["developers building code analysis agents that work with large repositories","teams using context-window-constrained models (Claude 100K, GPT-4 8K) on large codebases","applications that need to balance comprehensiveness with token efficiency"],"limitations":["Semantic boundary detection (functions, classes) requires language-specific parsing — may not work well for all file types","Chunk boundaries may split logical units if token budget is very small relative to semantic units","No built-in overlap between chunks — context lost at chunk boundaries may require re-reading","Performance degrades on files with very long lines (e.g., minified code, JSON) where token boundaries don't align with line breaks"],"requires":["MCP server implementation with file system access","Token counter library calibrated to target model","Optional: AST parser for semantic boundary detection (tree-sitter, Babel, etc.)"],"input_types":["file path (string)","token budget per chunk (integer)","chunk index or offset (integer)","optional: semantic boundary preference (boolean)"],"output_types":["chunk content (string)","chunk metadata (start line, end line, token count, has_more_chunks boolean)","semantic context (function/class name if boundary-aware)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47209377__cap_2","uri":"capability://tool.use.integration.token.budget.tracking.and.enforcement.across.mcp.operations","name":"token budget tracking and enforcement across mcp operations","description":"Maintains a session-level token budget that tracks cumulative consumption across multiple file read operations, enforcing limits before operations exceed the budget. The implementation likely uses a state machine or middleware pattern to intercept file tool calls, check remaining budget, and either allow, deny, or suggest alternative operations (like summarization) based on available tokens.","intents":["I want to set a total token budget for a session and have the MCP server prevent operations that would exceed it","I need visibility into cumulative token consumption across multiple file reads in a single agent run","I want the server to suggest alternatives (summarize instead of read full file) when budget is low"],"best_for":["developers building cost-conscious LLM agents that need predictable token usage","teams running agents on token-metered APIs (OpenAI, Anthropic) with strict budgets","applications where context window exhaustion would cause failures rather than graceful degradation"],"limitations":["Budget enforcement is server-side only — does not prevent client-side token consumption from other sources (e.g., system prompts, conversation history)","No built-in recovery mechanism if budget is exceeded — requires explicit client handling of rejection responses","Budget tracking adds ~5-10ms latency per operation for token counting and state updates","Does not account for token overhead from MCP protocol framing itself"],"requires":["MCP server with stateful session management","Token counter library","Client capable of handling budget-exceeded error responses"],"input_types":["initial token budget (integer)","model identifier for tokenizer selection (string)","optional: budget enforcement mode (strict, warn, suggest)"],"output_types":["operation result or rejection (structured data)","remaining budget (integer)","alternative suggestions if budget insufficient (array of strings)"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47209377__cap_3","uri":"capability://tool.use.integration.token.cost.estimation.and.reporting.for.file.operations","name":"token cost estimation and reporting for file operations","description":"Calculates and returns token cost estimates for file operations before execution, using a tokenizer matched to the target LLM model. The implementation likely pre-tokenizes file content or uses heuristic estimation (characters × 1.3 for English text) to provide instant cost feedback without actually reading the file, enabling cost-benefit analysis before committing to expensive operations.","intents":["I want to know how many tokens a file will consume before I read it","I need to compare token costs across multiple files to decide which ones to load","I want to estimate the total token impact of reading a directory of files"],"best_for":["developers building interactive LLM tools where users need to make informed file selection decisions","teams analyzing codebase token costs before running large-scale code analysis agents","applications that need to present token cost information in UI/CLI for user decision-making"],"limitations":["Estimates may be inaccurate for files with special characters, code, or non-English text — tokenizer-specific variance of 5-20%","Requires file system access to read file sizes; cannot estimate costs for remote files without downloading","No caching of estimates — recalculates on each query unless explicitly cached by client","Estimates assume standard tokenizer; custom tokenizers or fine-tuned models may have different actual costs"],"requires":["File system access","Tokenizer library matching target model (tiktoken for OpenAI, sentencepiece for others)","File size metadata (available from stat() calls)"],"input_types":["file path (string)","model identifier (string, e.g., 'gpt-4', 'claude-3-opus')","optional: encoding hint (utf-8, ascii)"],"output_types":["estimated token count (integer)","confidence level (low/medium/high)","cost breakdown (file size in bytes, estimated tokens, tokens per KB)","comparison data (tokens vs similar files)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47209377__cap_4","uri":"capability://tool.use.integration.directory.traversal.with.cumulative.token.budgeting","name":"directory traversal with cumulative token budgeting","description":"Implements directory listing and recursive file discovery operations that calculate and report cumulative token costs for all files in a directory tree. The implementation likely walks the file system, collects file metadata, estimates tokens for each file, and aggregates costs, allowing clients to understand the full token impact of loading an entire directory before committing to the operation.","intents":["I need to understand the total token cost of loading an entire directory or project into context","I want to find the largest files (by token count) in a codebase to prioritize what to load","I need to filter a directory listing to only include files that fit within my remaining token budget"],"best_for":["developers building codebase analysis tools that need to understand scope before loading","teams working with large monorepos who need to selectively load relevant portions","applications that present file/directory selection UIs with token cost information"],"limitations":["Recursive traversal can be slow on large directory trees (100K+ files) — may require pagination or depth limits","Token estimation for entire directories is approximate and may not account for deduplication or compression","No built-in filtering by file type — requires client-side filtering or explicit include/exclude patterns","Symlinks and circular references require careful handling to avoid infinite loops"],"requires":["File system access with recursive traversal permissions","Token counter library","Optional: gitignore parser for respecting version control exclusions"],"input_types":["directory path (string)","max depth (integer, optional)","file pattern filter (glob or regex, optional)","model identifier (string)"],"output_types":["file listing with metadata (array of objects with path, size, estimated tokens)","cumulative token count (integer)","summary statistics (total files, total size, average tokens per file)","sorted results (by token count, file size, or path)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47209377__cap_5","uri":"capability://tool.use.integration.model.specific.tokenizer.selection.and.switching","name":"model-specific tokenizer selection and switching","description":"Automatically selects and switches between tokenizers based on the target LLM model identifier, ensuring token estimates and counts match the actual model's tokenization scheme. The implementation likely maintains a registry of model-to-tokenizer mappings (e.g., gpt-4 → tiktoken, claude-3 → sentencepiece) and dynamically loads the appropriate tokenizer, with fallback heuristics for unknown models.","intents":["I want token counts to be accurate for my specific model without manually configuring tokenizers","I need to switch between different models and have token estimates automatically adjust","I want to compare token costs across different models (e.g., GPT-4 vs Claude) for the same files"],"best_for":["developers building multi-model LLM applications that need consistent token accounting","teams evaluating different models and needing accurate cost comparisons","applications that let users choose their LLM provider and need automatic tokenizer adaptation"],"limitations":["Tokenizer registry must be manually maintained as new models are released — may lag behind latest models","Some models (especially fine-tuned or custom models) don't have public tokenizers — falls back to heuristics with 10-20% error","Switching tokenizers mid-session may cause budget misalignment if previous estimates used different tokenizer","No support for custom tokenizers — requires client-side implementation for proprietary models"],"requires":["Model identifier parameter in all token-related operations","Tokenizer libraries for supported models (tiktoken, sentencepiece, etc.)","Fallback heuristic for unknown models (e.g., character count × 1.3)"],"input_types":["model identifier (string, e.g., 'gpt-4-turbo', 'claude-3-opus', 'llama-2-70b')","content to tokenize (string)"],"output_types":["token count (integer)","tokenizer used (string identifier)","confidence level (high for known models, low for unknown)","alternative counts for other models (optional)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":32,"verified":false,"data_access_risk":"high","permissions":["MCP client implementation (Claude Desktop, custom MCP client, or compatible tool)","Python 3.8+ or Node.js 16+ depending on implementation","Token counter library (tiktoken for OpenAI models, or equivalent for other providers)","MCP server implementation with file system access","Token counter library calibrated to target model","Optional: AST parser for semantic boundary detection (tree-sitter, Babel, etc.)","MCP server with stateful session management","Token counter library","Client capable of handling budget-exceeded error responses","File system access"],"failure_modes":["Token estimation accuracy depends on tokenizer choice — may differ from actual model tokenization by 5-15%","No built-in caching of token counts across repeated reads — recalculates on each operation","Requires explicit token budget configuration per session; no automatic budget enforcement","Does not handle multi-byte character encoding edge cases that some tokenizers struggle with","Semantic boundary detection (functions, classes) requires language-specific parsing — may not work well for all file types","Chunk boundaries may split logical units if token budget is very small relative to semantic units","No built-in overlap between chunks — context lost at chunk boundaries may require re-reading","Performance degrades on files with very long lines (e.g., minified code, JSON) where token boundaries don't align with line breaks","Budget enforcement is server-side only — does not prevent client-side token consumption from other sources (e.g., system prompts, conversation history)","No built-in recovery mechanism if budget is exceeded — requires explicit client handling of rejection responses","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.28,"quality":0.22,"ecosystem":0.46,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.692Z","last_scraped_at":"2026-05-04T08:10:01.171Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-file-tools-silently-eat-your-context-window-i-","compare_url":"https://unfragile.ai/compare?artifact=mcp-file-tools-silently-eat-your-context-window-i-"}},"signature":"oMl7WqzXG2ZloJgGl1IbsmiO5ualYO3zKFmjVKCzw4XWJJ3Gy5S7zdqJYsTvSX5TPZ+4+8ceK4C0oPwVQ3/yDg==","signedAt":"2026-06-21T05:55:04.929Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-file-tools-silently-eat-your-context-window-i-","artifact":"https://unfragile.ai/mcp-file-tools-silently-eat-your-context-window-i-","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-file-tools-silently-eat-your-context-window-i-","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}