Skip to main content
Glama
skainguyen1412

MCP Codebase Mentor

by skainguyen1412
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Local

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a distinct responsibility: init_codebase prepares the index, search_codebase queries it, and generate_tutorial creates learning content from the indexed data. There is no overlap in purpose or behavior.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern: init_codebase, generate_tutorial, search_codebase. The naming is uniform, predictable, and clearly indicates each tool's action.

    Tool Count5/5

    With only 3 tools, the server is tightly scoped to its purpose of codebase mentoring. Each tool is essential to the workflow, and the small count is appropriate for a focused toolset.

    Completeness5/5

    The toolset covers the full lifecycle: initialization, semantic search, and tutorial generation. For the stated domain, there are no obvious missing operations that would leave an agent stuck.

  • Average 3.9/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • 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

  • Behavior3/5

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

    Without annotations, the description carries the burden of behavioral disclosure. It discloses the prerequisite and outlines the tutorial's content structure (e.g., Mermaid diagrams, learning path). However, it does not clarify whether the tool writes files, modifies the codebase, or what the exact return format is, leaving some behavioral ambiguity.

    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 well-structured with a clear opening sentence followed by a bulleted list of outputs, and ends with a prerequisite. It is concise and front-loaded, though the bullet list is slightly verbose. There is no redundant information.

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

    Completeness3/5

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

    Given the tool's complexity (prerequisite, multiple output types) and no output schema or annotations, the description covers the key purpose and prerequisite but lacks details on return values, error handling, and how focusTopic modifies the output. It is minimally adequate but incomplete.

    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?

    Since schema description coverage is 100%, the baseline for this dimension is 3. The description adds no additional parameter detail beyond the schema; rootPath and focusTopic are not discussed in the description, but the schema already provides adequate descriptions.

    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 (generate a comprehensive tutorial/learning guide) and the resource (codebase). It distinguishes itself from sibling tools init_codebase and search_codebase by focusing on producing a tutorial artifact rather than initializing or searching.

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

    Usage Guidelines4/5

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

    The description explicitly states a prerequisite ('Requires init_codebase to be run first'), indicating when it is appropriate to use this tool. It does not explicitly mention alternatives or exclusions, but the prerequisite provides clear usage context.

    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?

    No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the prerequisite and return values, but does not explicitly state that the operation is read-only, nor does it describe error conditions, permissions, or rate limits.

    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 concise, front-loaded with the main purpose, and uses a bulleted list for return values. Each sentence is informative with no unnecessary fluff.

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

    Completeness4/5

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

    The tool has only two parameters and no output schema, so the description's explanation of the return format (snippets, scores, file context) is helpful. It covers the essential workflow but omits error behavior and potential resource implications.

    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 coverage is 100%, so both query and rootPath are already well-described in the input schema. The description adds little beyond the schema, paraphrasing that query is natural language and rootPath must be initialized.

    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 it performs semantic search across a codebase using vector embeddings. It is easily distinguished from siblings init_codebase and generate_tutorial.

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

    Usage Guidelines4/5

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

    The description explicitly states that init_codebase must be run first, providing a clear prerequisite. It does not explicitly mention when not to use the tool or alternative options, but the workflow implication is strong.

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

  • Behavior3/5

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

    With no annotations provided, the description carries the full burden. It discloses the main steps (crawling respecting .gitignore, analyzing files, building a manifest, creating a vector index), which is useful. However, it does not explicitly mention whether the tool modifies the codebase, writes files, or is safe to re-run, leaving some behavioral ambiguity.

    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 concise and well-structured: a one-line summary, a bulleted list of actions, and a usage directive. Every sentence adds value without redundancy.

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

    Completeness4/5

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

    For a tool with one parameter, no output schema, and no annotations, the description covers purpose, process, and workflow integration well. It could mention what happens with the generated manifest/index, but that is not critical for an initialization step.

    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?

    The schema describes rootPath as an absolute path to the codebase root, covering 100% of the parameter's meaning. The description adds no extra parameter details, so the baseline score of 3 applies.

    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 tool initializes and indexes a codebase for AI mentoring, then lists specific actions like crawling, analyzing, building a manifest, and creating a vector index. It distinguishes itself from sibling tools by being a prerequisite for generate_tutorial and search_codebase.

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

    Usage Guidelines4/5

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

    The description explicitly says 'Run this before using generate_tutorial or search_codebase', providing clear timing context. While it does not state when not to use the tool, the directive is strong and alternatives are implied by their sequencing.

    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

mcp-codebase MCP server

Copy to your README.md:

Score Badge

mcp-codebase 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/skainguyen1412/mcp-codebase'

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