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Presh-AR

codescan-mcp-server

by Presh-AR
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

Server Quality Checklist

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

  • Latest release: v1.1.0

  • Disambiguation5/5

    Each tool has a distinct purpose: listing projects, retrieving issues for a project, and fetching metrics. There is no overlap or ambiguity.

    Naming Consistency4/5

    All tool names are single nouns, which is consistent. Adding verbs (e.g., list_projects) would make them more descriptive, but the pattern is uniform.

    Tool Count4/5

    3 tools is a reasonable number for a focused code scanning server. It covers the core actions without being overly sparse.

    Completeness3/5

    The server covers listing projects, retrieving issues, and getting metrics. Missing are details on individual projects, issue filtering, or update/delete operations, but for a read-only scanner it is fairly complete.

  • Average 2.4/5 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

  • This repository is licensed under MIT License.

  • 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.

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  • 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

  • Behavior1/5

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

    With no annotations, the description must disclose behavioral traits. It only says 'Get issues', which implies a read operation, but it doesn't confirm idempotency, rate limits, pagination behavior, or error handling. The description provides no meaningful behavioral context for a tool with 26 parameters.

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

    Conciseness2/5

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

    The description is a single sentence, which is concise, but it is under-specified and lacks structure. It fails to earn its place because it does not aid correct tool invocation.

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

    Completeness1/5

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

    Given the high parameter count, no output schema, and no annotations, the description is severely incomplete. It does not cover return values, filtering options, pagination, or any usage nuances.

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

    Parameters1/5

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

    Schema description coverage is 0%, so the description must compensate. It does not explain any of the 26 parameters. Even though some parameters have enums, their meanings are not clarified. The description adds no value beyond the parameter names.

    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 'Get issues for a Codescan project' clearly states the verb and resource, and implies the context of a Codescan project. It distinguishes from siblings 'metrics' and 'projects' by naming a different resource.

    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 like 'metrics' or 'projects'. There are no exclusions or context hints for usage.

    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 must convey behavioral traits. It only says 'Get available metrics', which implies a read operation but does not disclose side effects, authentication requirements, rate limits, pagination behavior, or response format. The presence of 'page' and 'page_size' parameters hints at pagination, but this is not explained.

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

    Conciseness2/5

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

    The description is extremely short (one sentence), but it lacks necessary detail. Conciseness is achieved at the cost of completeness, making it under-specified. The structure is flat and does not front-load key information beyond the basic purpose.

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

    Completeness1/5

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

    Given four parameters, no annotations, no output schema, and sibling tools, the description is severely incomplete. It fails to explain what the tool returns, how parameters work, or when to use it. The tool's complexity demands a richer description.

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

    Parameters1/5

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

    With 0% schema description coverage, the description must explain parameter semantics, but it does not. It mentions none of the four parameters (token, page, page_size, component), leaving the agent to guess their purpose and format. This is a critical gap.

    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 states 'Get available metrics from Codescan', clearly indicating the verb (get) and resource (metrics). It distinguishes this tool from sibling tools 'issues' and 'projects' by referring to a different resource type. However, it does not explicitly differentiate itself from potential other metric-related tools.

    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 usage guidelines are provided. The description does not specify when to use this tool over alternatives, nor does it mention prerequisites or typical use cases. The agent is left without guidance on tool selection.

    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, and the description fails to disclose any behavioral traits such as authentication requirements, pagination behavior, or what happens when no projects exist.

    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 very concise at one sentence with no wasted words, but it is overly brief and lacks necessary detail for a tool with four parameters.

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

    Completeness1/5

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

    Given the tool has four parameters with no schema descriptions, no output schema, and no annotations, the description is severely incomplete, providing only a basic purpose.

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

    Parameters1/5

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

    Schema description coverage is 0% and the description does not mention any of the four parameters (token, page, page_size, projects), leaving their meaning and usage completely unexplained.

    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 uses a specific verb 'List' and resource 'Codescan projects', clearly indicating the tool's function and distinguishing it from siblings 'issues' and 'metrics'.

    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 on when to use this tool versus alternatives; the description simply states what it does without any context on when it is appropriate.

    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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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.

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