MMI Architecture Analyzer

by lady-logic

Overview Schema Related Servers Score Discussions

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Server Quality Checklist

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Latest release: v0.2.0
Disambiguation4/5
Most tools have distinct purposes, such as analyze_abstraction for abstraction levels and analyze_cycles for circular dependencies, but analyze_mmi overlaps by running multiple analyses that other tools handle individually, which could cause confusion in tool selection. The monitoring tools (get_monitoring_status, start_monitoring, stop_monitoring) are clearly separate from analysis tools, reducing overall ambiguity.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern with snake_case, such as analyze_abstraction, get_monitoring_status, and visualize_architecture. This uniformity makes the tool set predictable and easy to navigate, with no deviations in naming conventions across the nine tools.
Tool Count5/5
With 9 tools, the count is well-scoped for an architecture analyzer server, covering analysis, monitoring, and visualization aspects without being overwhelming. Each tool serves a clear role, such as specific analyses or monitoring controls, making the set appropriately sized for the domain.
Completeness4/5
The tool set provides comprehensive coverage for architecture analysis, including specific checks (e.g., layering, encapsulation), overall scoring (analyze_mmi), monitoring lifecycle (start/stop/get status), and visualization. A minor gap is the lack of tools for fixing or suggesting corrections for identified violations, but agents can work around this by using analysis results to guide manual improvements.
Average 3.1/5 across 9 of 9 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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- No stable releases found
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- No high-severity vulnerability alerts
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Add a glama.json file to provide metadata about your server.
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If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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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 describes what the tool does but lacks critical behavioral details: it doesn't specify whether this is a read-only analysis or has side effects, what permissions are required, what the output format looks like, or any performance characteristics. For a tool with 2 parameters and no annotations, this leaves significant gaps in understanding how the tool behaves.
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 appropriately concise with three sentences that each serve a purpose: stating the overall function, specifying what it detects, and clarifying the goal. It's front-loaded with the main purpose and avoids unnecessary elaboration. However, the second sentence could be slightly more streamlined.
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 (analyzing code architecture), lack of annotations, and absence of an output schema, the description is incomplete. It doesn't explain what the analysis output contains, how violations are reported, or what follow-up actions might be needed. For a tool that presumably returns analysis results, this creates uncertainty about how to interpret and use the tool's findings.
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 both parameters thoroughly. The description adds no parameter-specific information beyond what's in the schema. It doesn't explain how 'projectPath' relates to the abstraction analysis or provide context for 'mode' selection. The baseline score of 3 reflects adequate but minimal value added by the description.
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: analyzing separation of abstraction levels by detecting mixing of business logic with technical details and identifying violations. It uses specific verbs ('analyzes', 'detects', 'identifies') and specifies the resource (abstraction levels in code). However, it doesn't explicitly differentiate this from sibling tools like analyze_layering or analyze_encapsulation, which may have related but distinct 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. It doesn't mention sibling tools, specify contexts where this analysis is appropriate, or indicate prerequisites. The agent must infer usage from the purpose alone, which is insufficient for optimal tool selection.
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 states what the tool does but doesn't describe output format, whether it's read-only or has side effects, performance characteristics, or error handling. For a tool with 2 parameters and no output schema, 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 - just two sentences that directly state the tool's purpose and function. Every word earns its place with zero wasted text, 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 has 2 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what the analysis returns, how results are formatted, or what 'over-exposed types' means in practice. For a code analysis tool, users need to understand what kind of output to expect.
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 both parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema. The baseline of 3 is appropriate when the schema does the heavy lifting, though the description could have explained how parameters relate to the analysis.
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: analyzing encapsulation quality by checking visibility of classes/interfaces/records and identifying over-exposed types. It specifies the verb ('analyzes'), resource ('encapsulation quality'), and scope ('public vs internal visibility'), though it doesn't explicitly differentiate from sibling tools like analyze_abstraction or analyze_layering.
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. It doesn't mention sibling tools, prerequisites, or specific contexts where encapsulation analysis is appropriate versus other architectural analyses available on the server.
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 describes the visualization output and interactive features, but doesn't cover critical aspects like performance implications, data sources (e.g., whether it analyzes code in real-time or uses cached results), error handling, or system requirements. For a visualization tool with zero annotation coverage, this leaves the agent guessing about operational behavior.
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 efficiently structured in two sentences: the first covers the core functionality, and the second adds interactive details. It's front-loaded with the main action ('Generate interactive architecture heatmap visualization') and avoids unnecessary fluff. However, it could be slightly more concise by integrating the color coding into the first sentence.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (visualization with interactive features), no annotations, no output schema, and a simple input schema, the description is minimally adequate. It explains what the tool produces but lacks details on output format (e.g., HTML file, URL), error cases, or integration with sibling analysis tools. It meets basic requirements but leaves gaps for effective agent use.
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%, with the single parameter 'projectPath' fully documented in the schema as 'Path to the C# project directory.' The description adds no additional parameter context beyond what the schema provides, such as format examples or constraints. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but doesn't need to.
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: 'Generate interactive architecture heatmap visualization with D3.js.' It specifies the visualization type (heatmap), technology (D3.js), and key elements (files as nodes, dependencies as links, violations highlighted). However, it doesn't explicitly differentiate from sibling tools like analyze_abstraction or analyze_cycles, which appear to be analysis tools rather than visualization tools.
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. It mentions interactive features like clicking nodes for details, but doesn't specify use cases, prerequisites, or exclusions. Given sibling tools like analyze_abstraction that might provide complementary data, the lack of comparative context is a significant gap.
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. While it states what the tool does, it doesn't describe important behavioral aspects like what format the analysis output takes, whether it's a read-only analysis or makes changes, error handling, performance characteristics, or what happens with invalid project paths.
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 efficiently structured in two clear sentences that directly state the tool's purpose. There's no wasted text, though it could potentially benefit from slightly more detail given the lack of annotations and output schema.
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?
For a tool with no annotations and no output schema that performs architectural analysis, the description is insufficient. It doesn't explain what the analysis output looks like, what constitutes a 'violation', how results are presented, or what happens with edge cases. The description needs to compensate for the lack of structured metadata.
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 parameters. The description doesn't add any parameter-specific information beyond what's in the schema, such as explaining what 'compact' versus 'detailed' mode means in practical terms or providing examples of valid project paths.
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 specific action ('analyzes Clean Architecture layering violations'), target resource ('C# project'), and scope ('Domain/Application/Infrastructure layers'). It distinguishes from siblings like analyze_abstraction or analyze_cycles by focusing specifically on layering violations and dependency rules.
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 analyze_encapsulation or visualize_architecture. It doesn't mention prerequisites, typical use cases, or when other tools might be more appropriate for different architectural analysis needs.
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. It mentions that cycles 'violate good architecture principles' and are 'critical' for Domain layer, adding some behavioral context. However, it doesn't disclose key traits: whether this is a read-only analysis, what the output format is, if it has side effects, performance implications, or error handling. 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with three sentences that are front-loaded: the first states the core purpose, the second elaborates on detection, and the third adds critical context. There's no wasted text, and each sentence adds value, though it could be slightly more structured for optimal clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and 2 parameters with full schema coverage, the description is moderately complete. It covers the purpose and some context but lacks details on output format, error cases, or integration with other tools. For a static analysis tool, this is adequate but leaves room for improvement in guiding the agent on what to expect from the tool's behavior.
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 parameters (projectPath and mode). The description doesn't add any parameter-specific information beyond what's in the schema, such as examples or usage tips. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but also doesn't detract.
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: 'Analyzes circular dependencies (cycles) in the codebase' with specific verb ('analyzes') and resource ('circular dependencies in the codebase'). It distinguishes from siblings by focusing on cycles rather than abstraction, encapsulation, layering, etc., though it doesn't explicitly contrast with them. The mention of Domain layer adds specificity but doesn't fully differentiate from all siblings.
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 context with 'Critical when Domain layer is involved,' suggesting when this tool is particularly important. However, it lacks explicit guidance on when to use this vs. alternatives like analyze_layering or other analysis tools, and no exclusions or prerequisites are stated. The guidance is helpful but incomplete.
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. It implies a read-only operation ('shows'), but doesn't disclose behavioral traits like whether it requires authentication, has rate limits, returns real-time vs. cached data, or handles errors. The description is minimal and lacks operational context.
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. Every word contributes meaning without waste, making it appropriately sized for a zero-parameter tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
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 adequate but minimal. It covers the basic purpose but lacks context on usage, behavior, or output format, which could be helpful for an agent to understand how to interpret the results (e.g., what 'scores and trends' entail).
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% (since there are no parameters to describe). The description doesn't need to add parameter semantics, so a baseline of 4 is appropriate as it doesn't introduce confusion or gaps.
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 'shows' and the resource 'status of all monitored projects', specifying what data is included ('current scores and trends'). However, it doesn't explicitly differentiate from sibling tools like 'visualize_architecture' or 'analyze_*' tools, which might also show project-related information.
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. It doesn't mention prerequisites like needing monitoring to be active (implied by 'monitored projects'), nor does it compare to siblings such as 'start_monitoring' or 'stop_monitoring' for context on setup/teardown.
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 describes the monitoring behavior ('Watches .cs files and automatically analyzes on changes') and persistence ('Stores score history over time'), which is useful. However, it misses critical details: whether this requires specific permissions, if it runs in background/blocking mode, potential resource impacts, error handling, or how to access stored history. For a tool that initiates continuous processes, this is a significant gap.
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 efficiently structured in three concise sentences that cover purpose, behavior, and outcome. Each sentence adds value: starting monitoring, watching/analyzing files, and storing history. It's front-loaded with the core action. Minor improvement could be made by combining ideas, but it's highly efficient with zero waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description provides adequate basics for a monitoring initiation tool. It covers what the tool does and key behaviors, but lacks details on operational aspects (e.g., how monitoring runs, accessing results, stopping). With 1 parameter at 100% schema coverage, it's minimally viable but leaves gaps in understanding the full tool behavior.
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%, with the single parameter 'projectPath' fully documented in the schema. The description doesn't add any parameter-specific information beyond what the schema provides (e.g., no examples of valid paths or format requirements). Baseline is 3 since the schema does the heavy lifting, but the description doesn't compensate with additional context.
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: 'Starts continuous MMI monitoring for a C# project' with specific actions ('Watches .cs files and automatically analyzes on changes') and outcome ('Stores score history over time'). It distinguishes from siblings like 'analyze_mmi' (one-time analysis) and 'get_monitoring_status' (status check), but doesn't explicitly contrast them. The verb 'starts' is specific, and the resource 'C# project' is well-defined.
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 context: it's for initiating ongoing monitoring rather than one-time analysis (contrasting with 'analyze_mmi'). However, it doesn't explicitly state when to use this tool versus alternatives like 'stop_monitoring' or prerequisites (e.g., project must be valid). The context is clear but lacks explicit guidance on exclusions or comparisons.
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. It mentions running analysis and providing a score, but doesn't disclose behavioral traits like execution time, resource requirements, error handling, or output format details. The description doesn't contradict annotations (none exist), but provides minimal behavioral context beyond the basic operation.
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 (two sentences) and front-loaded with the core purpose. Every word earns its place: first sentence defines the analysis scope, second sentence specifies the output. No redundant information or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 2 parameters with full schema coverage but no annotations and no output schema, the description is minimally adequate. It explains what the tool does but lacks details about the analysis process, output format, or integration with sibling tools. For a tool performing complex architectural analysis, more context about results and limitations would be helpful.
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 both parameters (projectPath and mode). The description adds no parameter-specific information beyond what's in the schema, maintaining the baseline score of 3. It doesn't explain how parameters affect the analysis process or results.
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: 'Complete MMI (Modularity Maturity Index) analysis' with specific dimensions (Layering, Encapsulation, Abstraction Levels) and mentions providing an overall architecture quality score. It distinguishes from siblings like analyze_abstraction, analyze_encapsulation, and analyze_layering by indicating it runs all three dimensions together, but doesn't explicitly contrast with other analysis tools like analyze_cycles.
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 comprehensive MMI analysis across three dimensions, suggesting it's for overall architecture assessment rather than individual dimension analysis. However, it lacks explicit guidance on when to use this tool versus alternatives like analyze_abstraction (for single dimension) or visualize_architecture (for visualization), and doesn't mention prerequisites or exclusions.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It adds some context by stating 'Preserves history,' which hints at non-destructive behavior regarding historical data, but it doesn't cover other aspects like permissions needed, side effects, or what happens after stopping (e.g., does monitoring cease immediately?). This is a minimal but not comprehensive disclosure.
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 and front-loaded, consisting of just two short sentences that directly state the tool's action and a key behavioral trait. Every word earns its place, with no wasted information or redundancy, making it efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (a single-parameter action with no output schema and no annotations), the description is somewhat complete but has gaps. It covers the basic purpose and hints at behavior ('Preserves history'), but lacks details on usage guidelines, full behavioral transparency, and output expectations. It's adequate as a minimum viable description but not fully comprehensive.
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 has 100% description coverage, with 'projectPath' clearly documented as 'Path to the C# project directory.' The description adds no additional meaning beyond this, as it doesn't elaborate on parameter usage or constraints. Given the high schema coverage, a baseline score of 3 is appropriate, as 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 with a specific verb ('stops') and resource ('MMI monitoring for a project'), making it immediately understandable. However, it doesn't explicitly differentiate from its sibling 'get_monitoring_status' or 'start_monitoring' beyond the action verb, which is why it doesn't reach a score of 5.
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 'get_monitoring_status' or 'start_monitoring', nor does it mention prerequisites or context for stopping monitoring. It lacks explicit usage instructions, leaving the agent to infer based on the action alone.
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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