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tmtcomeup

PocketFlow MCP Server

by tmtcomeup

analyze_github_repository

Analyze a GitHub repository to generate a beginner-friendly tutorial using the PocketFlow methodology, focusing on key abstractions and file patterns for clarity and simplicity.

Instructions

Analyze a GitHub repository and generate a comprehensive tutorial following the PocketFlow methodology

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
api_keyYesAPI key for the LLM provider
exclude_patternsNoFile patterns to exclude (e.g., ["*test*", "*docs/*"])
github_tokenNoOptional GitHub token for private repos or rate limit avoidance
include_patternsNoFile patterns to include (e.g., ["*.py", "*.js"])
languageNoLanguage for tutorial generationenglish
llm_providerYesLLM provider to use for analysisgoogle
max_abstractionsNoMaximum number of abstractions to identify
max_file_sizeNoMaximum file size in bytes
modelNoSpecific model to use (e.g., "anthropic/claude-3.5-sonnet" for OpenRouter or "gemini-2.5-pro" for Google)gemini-2.5-pro
project_nameNoOptional project name (derived from repo if omitted)
repo_urlYesGitHub repository URL (e.g., https://github.com/user/repo)
use_cacheNoEnable LLM response caching
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden but lacks critical behavioral details. It doesn't disclose that this is a complex, multi-step operation involving LLM calls, file processing, and potential rate limits. No information about execution time, error handling, or what 'comprehensive tutorial' entails is included.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that clearly states the tool's purpose. It's appropriately sized and front-loaded with the core functionality, though it could benefit from additional context about the PocketFlow methodology.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex tool with 12 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what the tutorial output looks like, how the analysis works, performance characteristics, or error conditions. The agent lacks crucial context for proper tool selection and invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so parameters are well-documented in the schema itself. The description adds no additional parameter context beyond implying the tool analyzes repository content for tutorial generation. This meets the baseline for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('analyze a GitHub repository') and the outcome ('generate a comprehensive tutorial following the PocketFlow methodology'). It distinguishes from the sibling tool 'get_repository_structure' by focusing on analysis and tutorial generation rather than just structural retrieval.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. While it implicitly suggests use for tutorial generation, there's no mention of prerequisites (e.g., needing API keys), limitations, or comparison with the sibling tool 'get_repository_structure'.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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