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gwbischof

Outsource MCP

by gwbischof
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

67%
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: one handles image generation while the other handles text generation. Their descriptions explicitly differentiate when to use each tool, with no overlap in functionality or ambiguity about which tool to select for a given task.

    Naming Consistency5/5

    Both tools follow a consistent 'outsource_<resource>' naming pattern, using snake_case throughout. The naming convention is predictable and clearly indicates the type of content being outsourced (image vs text).

    Tool Count2/5

    With only 2 tools, this server feels thin for its apparent scope of 'outsourcing' AI tasks. While the two tools cover image and text generation, the server name suggests broader outsourcing capabilities that aren't represented in the tool surface, such as audio generation, video processing, or other AI services.

    Completeness2/5

    For a server named 'Outsource MCP', the tool surface is severely incomplete. It only covers image and text generation, missing obvious outsourcing capabilities like audio generation, video processing, code execution, data analysis, or other AI services that would logically fall under an outsourcing umbrella. The domain implied by the server name is much broader than what's actually covered.

  • Average 4.6/5 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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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

  • Behavior4/5

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

    With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: provider limitations ('currently only "openai" is supported'), model-specific guidance, error conditions ('Other providers will return an error'), and return format ('URL of the generated image'). It doesn't mention rate limits, costs, or authentication requirements.

    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?

    Well-structured with clear sections (Args, Returns, Example usage, Note), front-loaded purpose statement, and every sentence adds value. No redundant information or wasted words.

    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 3-parameter tool with no annotations and no output schema, the description provides substantial context: purpose, parameters, return format, examples, and limitations. It could potentially mention authentication requirements or rate limits, but covers the essential operational aspects well.

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

    Parameters5/5

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

    With 0% schema description coverage, the description fully compensates by explaining all three parameters: provider (with current limitation), model (with quality/speed tradeoffs), and prompt (with guidance on detail). The example usage provides concrete parameter value guidance beyond what the bare schema offers.

    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 verb 'delegate image generation' and resource 'to an external AI model', with specific purpose 'create visual content'. It distinguishes from sibling 'outsource_text' by focusing on images rather than text.

    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 'when you need to create visual content' and distinguishes from text generation via sibling tool name. However, it doesn't explicitly state when NOT to use this tool or mention alternative approaches beyond the sibling.

    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 full burden. It discloses key behavioral traits: it delegates to external models, returns text responses or error messages, and implies it's a read-only operation (no destructive effects mentioned). However, it doesn't cover rate limits, authentication needs, or detailed error handling beyond 'if the request fails,' 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.

    Conciseness5/5

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

    The description is appropriately sized and front-loaded, starting with the core purpose and usage guidelines. Each sentence adds value, such as parameter explanations and examples, with no wasted words. The structure is logical and efficient.

    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 the tool's complexity (delegation to external AI models) and lack of annotations and output schema, the description is mostly complete. It covers purpose, usage, parameters, and returns, but could benefit from more details on error types or operational constraints. However, it's sufficient for basic understanding.

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

    Parameters5/5

    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 adds significant meaning beyond the input schema by explaining each parameter's purpose with examples: 'provider' specifies AI providers like 'openai', 'model' is the specific identifier, and 'prompt' is the instruction to send. This fully 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's purpose with specific verbs and resources: 'Delegate text generation to another AI model.' It distinguishes from the sibling tool 'outsource_image' by specifying 'text generation' versus image-related tasks. The purpose is specific and unambiguous.

    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 states when to use this tool: 'Use this when you need capabilities or perspectives from a different model than yourself.' It provides clear context for usage, including example scenarios like getting different perspectives or leveraging specialized models, without misleading 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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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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