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VCnoC

VC_replan-mcp

by VCnoC
OverviewSchemaRelated ServersScoreDiscussions
Python
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Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.9

  • Disambiguation5/5

    The two tools have entirely distinct purposes: one audits architecture for vulnerabilities, the other manages knowledge base links and content. There is no overlap in functionality.

    Naming Consistency5/5

    Both tool names follow the consistent pattern 'mcp_verb_noun' (audit_architecture, kb_update), with consistent use of underscores and clear verb-noun structure.

    Tool Count2/5

    With only 2 tools, the server feels under-scoped for a tool named 'VC_replan-mcp' which suggests a replanning capability. The tools cover only auditing and KB maintenance, lacking core replanning operations.

    Completeness2/5

    The tool surface is severely incomplete for the implied domain of VC replanning. There are no tools to create, modify, or execute plans; only an audit tool and a KB management tool exist.

  • Average 4.1/5 across 2 of 2 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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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

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

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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?

    With no annotations, the description carries the full burden. It lists possible actions (link, unlink, refresh_links, update_content, cleanup_stale) and returns, but does not disclose behavioral traits such as whether actions are destructive, authorization requirements, or side effects (e.g., unlink or cleanup_stale deleting data). Some transparency, but incomplete.

    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 extremely concise, using a docstring format with a one-line summary followed by clear Args and Returns sections. Every sentence adds value, and the most critical information is front-loaded.

    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?

    Given 6 parameters and no output schema or annotations, the description provides a solid overview of actions, parameters, and return type. However, it lacks details on how 'cleanup_stale' works, error handling, or prerequisites, which would make it fully comprehensive.

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

    Parameters4/5

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

    Schema coverage is 0%, so the description must compensate. It adds meaning for most parameters by specifying usage contexts (e.g., 'global_vuln_id: Target vulnerability ID (for link/unlink/update_content)'). However, 'content_patch' is only described as 'Content update patch', lacking detail on format or structure, which prevents a perfect score.

    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's purpose as managing global↔project links and content for knowledge base maintenance. It specifies a verb ('manage') and resource ('global ↔ project links and content'), and the sibling tool 'mcp_audit_architecture' is distinct, so differentiation is clear.

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

    Usage Guidelines3/5

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

    The description implies usage for knowledge base operations but provides no explicit guidance on when to use this tool versus alternatives, nor does it mention when not to use it. The sibling tool 'mcp_audit_architecture' is not contrasted.

    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?

    No annotations are provided, so the description carries the full burden. It explains the tool performs a scan and returns a vulnerability list, implying a read-only audit. However, it does not disclose potential side effects, permissions needed, or whether the tool modifies any state.

    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 concise with a brief summary and a well-organized Args list. It is front-loaded with the core purpose. Minor waste: the 'Args:' line could be integrated, but overall structure is clean.

    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?

    Given 4 parameters, no output schema, and no annotations, the description adequately explains the inputs and outputs. It mentions the tool uses web intelligence and KB history, but lacks examples or detailed methodology. Still, it covers the essential information for an agent to understand and invoke the tool.

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

    Parameters4/5

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

    The schema has 0% description coverage, but the description's Args section explains each parameter (proposed_solution, tech_stack_keywords, etc.) in plain language, adding meaning beyond the schema titles. This effectively compensates for the lack of schema 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 tool performs an architecture review using a 7-dimension matrix scan with web intelligence and KB history. It distinguishes itself from the sibling tool mcp_kb_update, which suggests a different purpose.

    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 provides clear context for when to use the tool (architecture review) and lists required parameters. However, it does not explicitly exclude scenarios or mention alternatives, though the sibling tool is a different function.

    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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