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rasvan-ghiliciu

local-executor-mcp

by rasvan-ghiliciu
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
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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 completely distinct purposes: delegate_code handles code generation subtasks, while list_local_models checks available models. There is zero overlap or ambiguity.

    Naming Consistency5/5

    Both tool names follow a clean verb_noun pattern (delegate_code, list_local_models), using snake_case consistently. The naming is predictable and easy to understand.

    Tool Count3/5

    With only 2 tools, the server feels minimal. However, the narrow scope of 'local execution' justifies a small surface; it is borderline but not excessive.

    Completeness4/5

    For its stated purpose, the server covers the core delegation flow and model discovery. A status or cancel tool would be a minor enhancement, but no critical gaps exist.

  • Average 4.3/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 1 commit 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

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  • This repository includes a README.md file.

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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 transparency burden. It mentions the local endpoint and that results reflect availability, but it does not explain error behavior, return format, or what happens if the backend is unreachable. Still, it adds useful context about confirming reachability.

    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 two sentences, front-loaded with the core action, and every word adds value. There is no redundancy or filler.

    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 zero-parameter, no-output-schema tool, the description is reasonably complete. It states what the tool returns ('model IDs'), the context ('local llama-swap endpoint'), and practical use cases. It could mention edge cases like empty results or connection errors, but these are minor gaps.

    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 tool has zero parameters, so the baseline is 4. The description adds no param details, but none are needed since the schema is empty. It correctly communicates that the tool requires no inputs.

    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 explicitly identifies the resource ('model IDs available on the local llama-swap endpoint'). It clearly distinguishes itself from the sibling tool 'delegate_code' by focusing on model enumeration rather than code execution.

    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 gives explicit use cases: 'pick a model' and 'confirm the backend is reachable.' While it does not mention when not to use it or alternatives, for a simple list operation this is adequate guidance.

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

  • Behavior4/5

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

    No annotations are provided, so the description carries the burden of behavioral disclosure. It states that the tool 'returns ONLY the generated artifact' and that the planner must 'verify and integrate it,' which are key behavioral traits. However, it does not disclose potential failure modes, error handling, or any side effects, leaving some gaps.

    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 purpose statement, a 'GOOD for' list, a 'NOT for' list, and an instruction. It is somewhat longer than the ideal two-sentence example, but every section earns its place by adding practical guidance on usage. It is not verbose or redundant.

    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 four parameters, no output schema, and no annotations. The description covers the essential context: what it does, when to use it, what inputs to provide, and what output to expect (only the artifact). Minor gaps like error handling are not critical for a delegation tool, making this fairly complete.

    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 input schema already provides descriptions for all four parameters (100% coverage), so the baseline is 3. The description adds general guidance ('Provide a precise spec plus any context the worker needs'), but this is only a slight reinforcement of the task and context parameters without introducing new semantics beyond the schema.

    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: 'Delegate a self-contained, mechanical code-generation subtask to a local LLM.' It also provides specific examples of good use cases (boilerplate, scaffolding, CRUD) and exclusions, distinguishing it from the sibling tool 'list_local_models' which is about listing models, not delegating code generation.

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

    Usage Guidelines5/5

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

    The description explicitly lists 'GOOD for' and 'NOT for' scenarios, giving clear when-to-use and when-not-to-use guidance. It even instructs the agent to 'do those yourself' for unsuited tasks, which is a direct exclusion. This is exemplary usage guidance.

    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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