consult7
CLI ToolFree** - Analyze large codebases and document collections using high-context models via OpenRouter, OpenAI, or Google AI -- very useful, e.g., with Claude Code
- Best for
- multi-provider llm abstraction with unified interface, intelligent file collection with pattern expansion and safety limits, context-aware token management with model-specific limits
- Type
- CLI Tool · Free
- Score
- 30/100
- Best alternative
- Mintlify
Capabilities10 decomposed
multi-provider llm abstraction with unified interface
Medium confidenceImplements a BaseProvider interface abstraction layer that unifies three distinct LLM providers (OpenRouter, Google AI, OpenAI) into a single consultation API. Each provider implements provider-specific features (OpenRouter's reasoning mode with |thinking suffix, Google's native ThinkingConfig, OpenAI's O-series effort-based thinking) while exposing a common interface through the PROVIDERS registry in providers/__init__.py. This enables seamless switching between providers and models without changing client code.
Uses a modular BaseProvider interface with provider-specific reasoning mode support (OpenRouter |thinking suffix, Google ThinkingConfig, OpenAI effort-based thinking) rather than lowest-common-denominator abstraction, allowing clients to opt-in to advanced features while maintaining code portability across providers.
Provides tighter integration with reasoning model features than generic LLM SDKs (like LangChain), enabling direct access to provider-specific thinking modes without wrapper overhead.
intelligent file collection with pattern expansion and safety limits
Medium confidenceProcesses file collections through file_processor.py with automatic wildcard pattern expansion (e.g., *.py, *_test.js), enforces safety limits (1MB per file, 4MB total collection), and auto-ignores sensitive paths (__pycache__, .env, secrets.py, .DS_Store, .git, node_modules). Requires absolute file paths starting with / for security. This enables safe, scalable analysis of large codebases without manual file enumeration or accidental inclusion of secrets.
Combines automatic pattern expansion with hardcoded safety patterns (auto-ignoring __pycache__, .env, secrets.py, .git, node_modules) and enforces absolute paths for security, rather than requiring users to manually exclude sensitive files or trust relative path resolution.
Prevents accidental secret exposure more reliably than generic file collection tools by auto-ignoring common sensitive paths and requiring absolute paths, reducing the risk of misconfiguration in automated analysis pipelines.
context-aware token management with model-specific limits
Medium confidenceImplements token_utils.py for pre-request token counting via estimate_tokens(), validates content fits within model context limits, manages thinking token budgets for reasoning modes, and dynamically retrieves context length from model metadata. This enables safe analysis of large files without exceeding model limits or wasting thinking tokens on models that don't support reasoning.
Combines pre-request token estimation with thinking-mode-aware budget allocation and dynamic context length retrieval, rather than treating token counting as a post-hoc concern. This enables proactive validation before expensive API calls and intelligent reasoning token allocation for O-series and Gemini models.
Provides tighter integration with reasoning model token budgets than generic LLM clients, enabling explicit control over thinking token allocation rather than relying on provider defaults.
mcp server protocol implementation with tool-based consultation interface
Medium confidenceImplements a Model Context Protocol (MCP) server (src/consult7/server.py) that exposes consultation capabilities as MCP tools, enabling AI agents (like Claude) to invoke file analysis through standardized tool calls. The server handles MCP protocol marshalling, tool registration, and request routing to the consultation engine. This allows Claude and other MCP-compatible agents to analyze codebases as a native capability without custom integrations.
Implements a full MCP server rather than a simple HTTP API, enabling native integration with Claude and other MCP-compatible agents. This allows agents to invoke analysis as a first-class capability without custom HTTP handling or context switching.
Provides deeper integration with Claude than REST API wrappers, enabling agents to invoke analysis natively through MCP tools without additional HTTP client code or context management overhead.
reasoning-mode consultation with provider-specific thinking support
Medium confidenceEnables analysis queries to leverage provider-specific reasoning modes: OpenRouter's |thinking suffix for extended reasoning, Google's native ThinkingConfig for Gemini models, and OpenAI's effort-based thinking for O-series models. The consultation engine routes reasoning requests to the appropriate provider and manages thinking token allocation. This allows complex codebase analysis to benefit from extended reasoning without manual prompt engineering.
Abstracts provider-specific reasoning modes (OpenRouter |thinking suffix, Google ThinkingConfig, OpenAI effort-based thinking) into a unified reasoning interface, allowing clients to request reasoning without knowing provider details. Manages thinking token budgets explicitly rather than relying on provider defaults.
Provides unified access to reasoning modes across multiple providers, whereas most tools lock users into a single provider's reasoning implementation. Enables cost-aware reasoning token allocation rather than unlimited thinking.
large-context codebase analysis with 1m+ token models
Medium confidenceEnables analysis of large codebases by leveraging models with 1M+ token context windows (Google Gemini 2.5 Pro, OpenAI GPT-5 400K, OpenRouter Claude Sonnet 4). The consultation engine formats file collections into a single context window and routes to appropriate high-context models. This allows comprehensive codebase analysis in a single query without chunking or multiple round-trips.
Specifically targets 1M+ token models and enforces collection limits (4MB) that align with these models' capabilities, rather than treating large-context analysis as a generic use case. Provides explicit routing to high-context models and token budget management for expensive queries.
Enables single-query analysis of large codebases, whereas chunking-based approaches (like LangChain) require multiple queries and lose cross-file context. Provides explicit cost and latency management for high-context models rather than treating them as drop-in replacements.
documentation generation from code and document collections
Medium confidenceAnalyzes code and document collections to generate comprehensive documentation by leveraging large-context models. The consultation engine accepts file collections and documentation queries, formats them into a single context, and returns structured documentation output. This enables automated documentation generation that understands full codebase context rather than isolated files.
Leverages full-codebase context (up to 1M tokens) for documentation generation, enabling documentation that understands cross-file dependencies and architecture, rather than generating documentation from isolated files or limited context.
Produces more comprehensive and architecturally-aware documentation than file-by-file tools because it analyzes entire codebases in a single context, capturing cross-file relationships and system design.
secure file collection with secret prevention and path validation
Medium confidenceEnforces security constraints on file collection through absolute path requirements (/path/to/file), auto-ignores sensitive paths (.env, secrets.py, .git, node_modules), and validates file access permissions before inclusion. This prevents accidental exposure of API keys, credentials, or private configuration in analysis requests sent to external LLM providers.
Combines absolute path requirements with hardcoded auto-ignore patterns (.env, secrets.py, .git, node_modules) to prevent secret exposure, rather than relying on user configuration or manual exclusion lists. This shifts security burden from users to the tool.
Prevents accidental secret exposure more reliably than generic file collection tools by making secret prevention the default behavior rather than an opt-in feature. Absolute path requirements reduce misconfiguration risk compared to relative path tools.
provider cost and performance comparison for analysis queries
Medium confidenceEnables comparison of different providers and models for the same analysis query by routing requests to multiple providers and returning cost/performance metadata (token usage, latency, pricing). The provider abstraction layer tracks token counts and execution time per provider, allowing users to make informed decisions about provider selection based on cost-benefit trade-offs.
Provides explicit provider comparison capabilities through the multi-provider abstraction, enabling cost-benefit analysis across providers. Most tools lock users into a single provider; Consult7 makes provider switching and comparison a first-class feature.
Enables data-driven provider selection based on actual cost and performance metrics, whereas most tools require manual provider selection or don't expose cost/performance trade-offs.
adaptive analysis strategy based on model capabilities
Medium confidenceDynamically adjusts analysis approach based on model capabilities: uses reasoning modes for O-series and Gemini models, allocates thinking tokens intelligently, and selects appropriate context window sizes. The consultation engine inspects model metadata and provider features to determine optimal analysis strategy without user configuration.
Automatically adapts analysis strategy based on model capabilities (reasoning support, context window size) rather than requiring explicit user configuration. This enables users to benefit from advanced features without understanding provider-specific capabilities.
Reduces configuration burden compared to tools requiring explicit model selection and reasoning mode specification. Enables automatic leverage of advanced features as new models become available.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Best For
- ✓Teams building LLM-powered analysis tools that need provider flexibility
- ✓Developers wanting to leverage reasoning models without provider lock-in
- ✓Organizations comparing LLM provider costs and performance
- ✓Developers analyzing large codebases (100+ files) with a single query
- ✓Teams with strict security requirements around secret exposure
- ✓CI/CD pipelines that need deterministic file collection without manual configuration
- ✓Cost-conscious teams optimizing token usage across multiple models
- ✓Developers building adaptive analysis pipelines that adjust to model capabilities
Known Limitations
- ⚠Provider-specific features (thinking modes, reasoning) require explicit configuration per provider
- ⚠Token counting accuracy varies by provider; estimates may differ from actual usage
- ⚠No automatic fallback if primary provider fails — requires explicit error handling
- ⚠1MB per-file limit excludes large generated files, binaries, or data files
- ⚠4MB total collection limit constrains analysis to ~1M token context windows; larger codebases require multiple queries
- ⚠Absolute path requirement (/path/to/file) makes relative path usage impossible
Requirements
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** - Analyze large codebases and document collections using high-context models via OpenRouter, OpenAI, or Google AI -- very useful, e.g., with Claude Code
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