Skip to main content
Glama
tmtcomeup

PocketFlow MCP Server

by tmtcomeup
OverviewSchemaRelated ServersScoreDiscussions
TypeScript

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: analyze_github_repository generates a tutorial, while get_repository_structure retrieves file structure. There is no overlap or ambiguity between these functions, making it easy for an agent to select the correct tool.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern (analyze_github_repository, get_repository_structure) with clear, descriptive names. The naming is uniform and predictable, enhancing usability.

    Tool Count2/5

    With only 2 tools, the server feels thin for its apparent domain of GitHub repository analysis and tutorial generation. A more complete set might include tools for updating tutorials, managing analysis results, or handling other repository aspects, suggesting an under-scoped implementation.

    Completeness2/5

    The tool surface is severely incomplete for the PocketFlow methodology domain. It lacks essential operations such as creating, updating, or deleting tutorials, managing user interactions, or handling errors, which are likely needed for a comprehensive workflow, leading to potential agent failures.

  • Average 3/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

    If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.

    MCP servers without a LICENSE cannot be installed.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior2/5

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

    With no annotations provided, the description carries full burden for behavioral disclosure but offers minimal information. It doesn't mention authentication requirements (though github_token parameter suggests optional auth), rate limits, what format the structure is returned in, whether this is a read-only operation, or any side effects. 'Get' implies a read operation, but this isn't explicitly stated.

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

    Conciseness5/5

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

    The description is a single, efficient sentence that states the core purpose without unnecessary words. It's appropriately sized for a tool with good schema documentation and gets straight to the point. Every word earns its place.

    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 tool with 5 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what format the structure is returned in (tree, list, JSON?), doesn't mention authentication behavior despite the github_token parameter, and provides no context about performance, limitations, or error conditions. The agent would need to guess about important behavioral aspects.

    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?

    With 100% schema description coverage, all parameters are documented in the schema itself. The description adds no additional parameter semantics beyond what the schema provides - it doesn't explain how patterns work, what depth means practically, or provide examples. The baseline of 3 is appropriate when the schema does the heavy lifting.

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

    Purpose4/5

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

    The description clearly states the action ('Get') and resource ('file structure of a GitHub repository'), making the purpose immediately understandable. However, it doesn't differentiate from the sibling tool 'analyze_github_repository' - we can infer this tool focuses on structure while the sibling might analyze content or metrics, but this distinction isn't explicit in the description.

    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?

    The description provides no guidance about when to use this tool versus the sibling 'analyze_github_repository' or any other alternatives. It doesn't specify prerequisites (like authentication needs) or contextual constraints beyond what's implied by the tool name. The agent must infer usage from the tool name alone.

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

  • 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.

GitHub Badge

Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

pocketflow-mcp MCP server

Copy to your README.md:

Score Badge

pocketflow-mcp MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/tmtcomeup/pocketflow-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server