Enhanced Architecture MCP

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation2/5
Multiple tools have overlapping purposes that could cause confusion. 'query_local_ai' and 'reasoning_assist' both involve reasoning assistance with local AI, while 'hybrid_analysis' and 'token_efficient_reasoning' both address complex problem-solving with efficiency considerations. The descriptions don't clearly differentiate when to use one over the other.
Naming Consistency3/5
The naming conventions are mixed but still readable. 'model_list' and 'query_local_ai' follow a verb_noun pattern, while 'hybrid_analysis', 'reasoning_assist', and 'token_efficient_reasoning' use descriptive phrases without clear verbs. This inconsistency makes the set less predictable but not chaotic.
Tool Count4/5
With 5 tools, the count is reasonable for a server focused on AI and analysis tasks. It's slightly lean but covers core functionalities like listing models, querying AI, and analysis. A few more tools might enhance coverage, but this is within an appropriate range.
Completeness3/5
The toolset covers basic AI interaction and analysis but has notable gaps. There's no tool for managing or configuring models (e.g., load/unload), updating analysis parameters, or handling errors. The domain suggests more lifecycle operations, but agents can work around this with the provided tools.
Average 2.9/5 across 5 of 5 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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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 provided, the description carries the full burden of behavioral disclosure. It mentions 'hybrid local+cloud analysis' which hints at a distributed process, but fails to describe key traits like performance characteristics, privacy implications, error handling, or what 'analysis' entails in terms of output or side effects. This leaves significant gaps for a tool with potential complexity.
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 a single, efficient phrase that conveys the core idea without unnecessary words. However, it's front-loaded but overly terse, potentially sacrificing clarity for brevity, as it could benefit from slightly more elaboration to better guide usage.
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?
Given the tool's potential complexity (implied by 'hybrid' and 'complex data'), lack of annotations, and no output schema, the description is insufficient. It doesn't explain what the analysis produces, how results are returned, or any behavioral nuances, making it incomplete for effective agent use despite the good schema coverage.
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 documents all parameters (data, approach, model) with their types, defaults, and brief descriptions. The tool description adds no additional meaning about parameters beyond what's in the schema, meeting the baseline score when schema coverage is high.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Hybrid local+cloud analysis for complex data' states a general purpose (analysis) and mentions the hybrid approach, but it's vague about what specific analysis is performed and doesn't distinguish from siblings like 'query_local_ai' or 'reasoning_assist'. It lacks a specific verb-resource combination that clearly differentiates this tool's function.
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 'query_local_ai' or 'reasoning_assist'. The description implies it's for 'complex data' but doesn't specify what makes data complex enough for this tool or when other tools might be more appropriate, leaving the agent without clear usage criteria.
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?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'structured reasoning assistance' but doesn't explain what that means operationally: what format the output takes, whether it's deterministic, what happens with different step counts, or any limitations. For a reasoning tool with zero annotation coverage, this leaves significant behavioral 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 extremely concise at just 5 words. Every word earns its place by conveying the core function. There's no wasted language or unnecessary elaboration. The structure is front-loaded with the essential information.
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?
Given this is a reasoning tool with 3 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what kind of output to expect, how the reasoning is structured, or what makes it different from sibling tools. For a tool that presumably produces complex reasoning outputs, more context is needed.
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 all three parameters (problem, steps, model). The description adds no additional meaning about parameters beyond what's in the schema. It doesn't explain how 'steps' affects reasoning quality, what models are available, or provide examples of appropriate 'problem' statements. Baseline 3 is appropriate when schema does all the work.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Structured reasoning assistance for complex problems' states a general purpose but lacks specificity about what the tool actually does. It mentions 'reasoning assistance' but doesn't specify the verb (generate? analyze? evaluate?) or the resource (what kind of reasoning output?). It distinguishes from siblings only by being about 'reasoning' rather than analysis or querying, but remains vague about the actual operation.
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 use this tool versus alternatives like 'hybrid_analysis', 'model_list', 'query_local_ai', or 'token_efficient_reasoning'. There's no mention of appropriate contexts, prerequisites, or exclusions. The agent must infer usage from the tool name and vague description alone.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool queries a local AI model via Ollama, implying it's a read-only operation that may involve network calls or local processing, but lacks details on permissions, rate limits, error handling, or response format. This is inadequate for a tool with potential complexity in AI interactions.
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, efficient sentence that front-loads the core purpose without unnecessary words. It directly communicates the tool's function and context, making it easy to parse and understand quickly, with no wasted information.
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?
Given the complexity of querying AI models and the lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., text response, structured data), error conditions, or behavioral traits like latency or resource usage, leaving significant gaps for an AI agent to use it effectively.
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 fully documents parameters like 'prompt', 'model', and 'temperature'. The description adds no additional meaning beyond what's in the schema, such as examples or constraints not covered. Baseline 3 is appropriate as the schema handles parameter semantics effectively.
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 ('Query') and target ('local AI model via Ollama'), with the purpose 'for reasoning assistance' providing specific context. It distinguishes from siblings like 'model_list' or 'hybrid_analysis' by focusing on querying rather than listing or analyzing, though it doesn't explicitly differentiate from 'reasoning_assist' or 'token_efficient_reasoning' which may have overlapping purposes.
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 use this tool versus alternatives like 'reasoning_assist' or 'token_efficient_reasoning'. It mentions 'reasoning assistance' but doesn't specify scenarios, prerequisites, or exclusions, leaving the agent with minimal context for tool selection among siblings.
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 carries full burden for behavioral disclosure. It states 'List available local AI models' which implies a read-only operation, but doesn't specify what 'available' means (e.g., installed, loaded, compatible), whether there are rate limits, authentication needs, or what the output format looks like. This leaves significant behavioral gaps for a tool with zero annotation coverage.
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, efficient sentence with zero wasted words. It's front-loaded with the core purpose and appropriately sized for a simple listing tool, making it easy to parse quickly.
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?
Given the tool's simplicity (0 parameters, no output schema), the description is minimal but adequate for basic understanding. However, with no annotations and no output schema, it fails to address behavioral aspects like what 'available' entails or the return format, leaving the agent with incomplete context for reliable use.
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 0 parameters, and schema description coverage is 100% (though trivial since there are no parameters). The description doesn't need to add parameter semantics, so it meets the baseline expectation for parameterless tools. No additional value is added, but none is required.
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 verb ('List') and resource ('available local AI models'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'query_local_ai' or 'reasoning_assist' that might also involve local AI models, so it doesn't reach the highest clarity level.
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 use this tool versus alternatives like 'query_local_ai' or 'hybrid_analysis'. It lacks explicit when/when-not instructions or references to sibling tools, leaving usage context implied at best.
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 carries the full burden of behavioral disclosure. It mentions the tool delegates to 'local AI' and conserves 'cloud tokens,' which hints at cost-saving and local processing, but lacks details on performance, error handling, or output format. For a tool with no annotations, this leaves significant gaps in understanding its behavior.
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, efficient sentence that front-loads the key purpose without any wasted words. It directly communicates the tool's value proposition and is appropriately sized for its complexity.
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?
Given the tool's complexity (delegating reasoning tasks) and the lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns, how errors are handled, or any limitations, which are crucial for effective use. The description alone isn't sufficient for a full understanding.
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 documents all three parameters thoroughly. The description doesn't add any meaning beyond what the schema provides—it doesn't explain parameter interactions or usage nuances. This meets the baseline score of 3 since the schema does the heavy lifting.
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 tool's purpose: 'Delegate heavy reasoning to local AI to conserve cloud tokens.' It specifies the verb ('delegate') and resource ('heavy reasoning'), and distinguishes it from potential siblings by emphasizing token conservation. However, it doesn't explicitly differentiate from tools like 'reasoning_assist' or 'query_local_ai' by name, which keeps it from a perfect score.
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 when to use this tool ('heavy reasoning' scenarios where conserving cloud tokens is important) but doesn't provide explicit guidance on when not to use it or name alternatives. Given sibling tools like 'hybrid_analysis' and 'reasoning_assist', more specific differentiation would be helpful, but the context is clear enough for basic usage.
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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