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raptor7197

MCP LLM Integration Server

by raptor7197
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Python
Local

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    llm_predict and echo are completely distinct in purpose—one performs LLM inference while the other is a simple testing utility. There is no overlap or ambiguity between them.

    Naming Consistency3/5

    llm_predict uses a prefix-plus-verb compound while echo is a bare verb, showing mixed naming conventions. However, both names are short and readable, so the inconsistency is not chaotic.

    Tool Count3/5

    With only two tools, the server sits at the borderline of feeling thin. For a minimal LLM integration demo the pair is acceptable, but a broader integration server would likely need more endpoints.

    Completeness2/5

    The server provides only one functional inference operation plus an echo test tool. Obvious gaps exist for an 'LLM integration server,' such as model management, parameter options, or alternative interaction modes like embeddings or chat.

  • Average 3.4/5 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

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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

  • Behavior2/5

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

    With no annotations, the description carries the full burden of behavioral disclosure. It reveals only that the LLM is 'local', implying offline execution and privacy, but says nothing about response format, nondeterminism, latency, errors, or side effects. This is insufficient for a tool whose output will be consumed by the agent.

    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 one short sentence with no filler, tautology, or duplication. It front-loads the action and resource. This efficiency is good, though it comes at the expense of necessary detail, so it does not reach a perfect 5.

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

    Completeness2/5

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

    The tool has no output schema and no annotations, so the description must explain what the agent can expect from a call. It fails to mention that the tool returns a text completion, how 'max_tokens' affects behavior, or any example invocation. Even for a simple two-parameter tool, an agent lacks enough information to confidently interpret the response.

    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 description coverage is 100%, so the schema already fully documents both 'prompt' and 'max_tokens'. The description does not add semantic value beyond restating 'text input', which maps directly to 'prompt'. The baseline of 3 applies because the schema carries the parameter information.

    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 identifies a specific resource ('local LLM') and an action ('process text input'), which tells an agent the tool routes text to a language model. The verb 'process' is generic and does not explicitly say 'generate' or 'predict', but the tool name 'llm_predict' disambiguates the intent. It does not mention why this differs from sibling 'echo', so it is not fully differentiated.

    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?

    The description provides no guidance on when to choose this tool over its sibling 'echo' or any alternative. There are no stated conditions, exclusions, or example use cases. An agent would have to infer from the name alone that this is for LLM inference, but nothing in the text says 'use this when you need model-generated text'.

    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?

    With no annotations, the description carries the full burden. 'Echo back the input text' accurately conveys the primary behavior and implies no side effects, which is sufficient for such a trivial tool. It does not mention output formatting, but the behavior is otherwise transparent.

    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 a single concise sentence with no extraneous words. It is front-loaded with the action and purpose, earning every word it uses.

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

    Completeness5/5

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

    For a one-parameter, side-effect-free echo tool with full schema coverage, the description is complete. The return behavior is self-evident from 'Echo back the input text', so no output schema is strictly necessary.

    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%, and the description's 'input text' directly parallels the schema's 'Text to echo back'. The description adds no new semantics beyond the schema, so the baseline score of 3 is appropriate.

    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 clearly states the action ('Echo back'), the target ('input text'), and the intended purpose ('for testing'). It is specific enough that an agent can distinguish it from llm_predict, though it does not explicitly name the sibling.

    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 phrase 'for testing' implies when to use the tool, but there is no explicit guidance about when not to use it or how it compares to llm_predict. The intended context is clear but underdeveloped.

    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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