kansei-mcp-server

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 that feedback 'shapes the future of these services,' hinting at impact, but fails to disclose critical traits such as whether this is a read or write operation, if it requires authentication, what happens after submission (e.g., storage, visibility), or any rate limits. This leaves significant gaps for an agent to understand the tool's behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is two sentences that efficiently convey the tool's intent and impact, with no wasted words. It is appropriately sized and front-loaded, though it could be slightly more direct in stating the action (e.g., 'Submit feedback').

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

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

Given the complexity of a feedback submission tool with 7 parameters, no annotations, and no output schema, the description is incomplete. It lacks details on behavioral aspects (e.g., mutation effects, response format) and doesn't compensate for the absence of structured fields, leaving the agent with insufficient context for proper use.

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

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

The schema description coverage is 100%, with detailed descriptions for all parameters, including enums that clarify options like question types and response choices. The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline of 3 without compensating or detracting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the tool is for sharing experiences about services through structured questions, which provides a general purpose. However, it doesn't specify the exact action (e.g., 'submit feedback' or 'record voice') and doesn't clearly differentiate from sibling tools like 'submit_feedback' or 'read_agent_voices', making it somewhat vague.

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

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

The description mentions 'Answer structured questions that help SaaS companies understand how agents actually choose, use, and struggle with their APIs,' which implies usage for feedback purposes. However, it provides no explicit guidance on when to use this tool versus alternatives like 'submit_feedback' or 'read_feedback', and lacks any exclusions or prerequisites.

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

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

Annotations indicate read-only and closed-world behavior, which the description aligns with by not implying mutations. The description adds value beyond annotations by specifying the 4 analysis layers (model selection, service alternatives, architecture improvements, infrastructure tips), providing useful context about what the tool examines, though it doesn't detail rate limits or auth needs.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is a single, efficient sentence that front-loads the core purpose ('analyze API spending') and adds necessary detail ('across 4 layers') without waste. Every word contributes to understanding the tool's function.

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

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

Given the tool's complexity (cost analysis with optimization recommendations), annotations cover safety (read-only), but no output schema exists. The description is complete enough by outlining the 4 analysis layers, though it could benefit from mentioning output format or examples to fully compensate for the lack of output schema.

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

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 fully. The description does not add meaning beyond the schema, such as explaining how 'service_id' relates to the 4 layers or providing examples. Baseline 3 is appropriate as the schema handles parameter documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

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 ('analyze', 'get optimization recommendations') and resources ('API spending'), and distinguishes it from siblings by specifying its unique focus on cost analysis across 4 layers. It goes beyond a tautology by detailing the scope of analysis.

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

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

The description implies usage for cost analysis and optimization, but does not explicitly state when to use this tool versus alternatives like 'get_service_detail' or 'get_service_history', nor does it provide exclusions or prerequisites. The context is clear but lacks sibling differentiation.

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

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

Annotations provide readOnlyHint=true and openWorldHint=false, indicating a safe, non-destructive read operation with limited scope. The description adds value by specifying the types of changelog information returned (new features, breaking changes, fixes, deprecations), which isn't covered by annotations. However, it doesn't disclose additional behavioral traits like rate limits, authentication needs, or response format details, keeping the score at a baseline level with annotations present.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is front-loaded with the core purpose in the first sentence and efficiently details the return information in the second. Both sentences earn their place by providing essential context without redundancy, making it appropriately sized and well-structured for quick comprehension.

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

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

Given the tool's moderate complexity (2 parameters, read-only operation) and rich annotations (readOnlyHint, openWorldHint), the description is mostly complete. It clearly states the purpose and return information, but lacks an output schema, leaving the agent to infer the exact structure of the changelog data. With annotations covering safety and scope, the description provides adequate context, though a brief mention of output format could enhance completeness.

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

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

Schema description coverage is 100%, with both parameters ('service' and 'since_days') well-documented in the schema. The description doesn't add any parameter-specific semantics beyond what the schema provides, such as clarifying the format of 'service' or usage of 'since_days'. Given the high schema coverage, a baseline score of 3 is appropriate as the description doesn't compensate but also doesn't need to.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Check if an MCP service has changed recently') and resource ('MCP service'), distinguishing it from siblings like 'list_pending_updates' or 'get_service_history' by focusing on changelog information. It explicitly mentions what information is returned, making the purpose distinct and unambiguous.

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

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

The description implies usage context by specifying it returns changelog information, suggesting it's for monitoring updates rather than other service-related tasks. However, it lacks explicit guidance on when to use this tool versus alternatives like 'list_pending_updates' or 'get_service_history', leaving the agent to infer based on the description's focus on recent changes and changelog details.

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

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 the tool 'Builds historical design scores' which implies data persistence, but doesn't clarify whether this is a write operation, what permissions are needed, or how the data is stored. The description lacks information about rate limits, error handling, or response format, leaving 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.

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

The description is extremely concise with just two sentences that efficiently convey the core purpose and scale. Every word earns its place - the first sentence defines the action and scope, while the second adds critical context about the scoring system and purpose. No wasted words or redundancy.

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

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

For a tool with 6 parameters (5 required) and no output schema, the description provides adequate but minimal context. It covers the basic purpose and scoring scale but lacks information about what the tool returns, how results are formatted, or any system constraints. The absence of annotations means the description should do more to compensate, but it doesn't fully address the complexity of the operation.

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

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 6 parameters thoroughly. The description adds minimal value beyond the schema - it mentions '4 dimensions' which corresponds to the four score parameters, but doesn't provide additional context about parameter relationships or usage patterns. This meets the baseline for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

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 ('Rate', 'Builds') and resources ('service's API/MCP design quality', 'historical design scores for consulting reports'). It distinguishes itself from siblings by focusing on design evaluation rather than operations like 'get_service_detail' or 'search_services'. The scale specification (0.0 to 1.0) adds precision.

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

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 prerequisites, appropriate contexts, or exclusions. While it implies usage for consulting reports, it doesn't specify whether this is the only use case or how it differs from other evaluation tools like 'audit_cost' or 'review_update'.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=false, indicating a safe, read-only operation with limited scope. The description adds useful context about the 'reverse recipe lookup' behavior and output format ('show what other services it can be combined with'), but doesn't disclose additional traits like rate limits, auth needs, or pagination. With annotations covering safety and scope, this meets the baseline for adding some value.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is a single, well-structured sentence that efficiently conveys the tool's purpose, input, and output without any wasted words. It's front-loaded with the core concept ('reverse recipe lookup') and directly addresses usage, making it highly concise and effective.

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

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

Given the tool's moderate complexity (1 parameter, no output schema), the description is largely complete: it explains the purpose, input, and expected output behavior. However, without an output schema, it could benefit from more detail on the return format (e.g., structure of recipes or combinations). The annotations provide safety and scope context, but the description doesn't fully compensate for the missing output schema.

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

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

Schema description coverage is 100%, with the 'service' parameter fully documented in the schema. The description adds marginal semantic context by explaining it's for 'service or MCP name' and giving examples ('freee', 'chatwork'), but doesn't provide syntax or format details beyond what the schema already states. This aligns with 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.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific verb ('find') and resource ('combinations'), with the unique purpose of 'reverse recipe lookup' that distinguishes it from siblings like 'get_recipe' (which presumably fetches a single recipe) or 'search_services' (which likely searches for services directly). It explicitly defines the input-output relationship: given a service/MCP name, it finds recipes containing it and shows combinable services.

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

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

The description provides clear context for when to use this tool ('given a service or MCP name'), implying it's for discovering related recipes and service combinations. However, it doesn't explicitly state when not to use it or name alternatives among siblings (e.g., 'get_recipe' for direct recipe retrieval or 'search_services' for service search), which prevents a perfect score.

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

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

Annotations indicate read-only and closed-world operation, which the description aligns with by not contradicting. The description adds valuable context beyond annotations: it specifies the article is 'publishable', includes grades and methodology, and outputs in markdown or JSON formats, enhancing behavioral understanding without contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is front-loaded with the core purpose in the first sentence, followed by specific details in a second sentence. Every sentence adds value without redundancy, making it efficiently structured and appropriately sized for the tool's complexity.

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

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

Given the tool's complexity (generating ranking articles), annotations cover safety, and schema covers inputs well, the description is mostly complete. It lacks output schema details, but compensates by specifying output formats and content structure, though could benefit from more on behavioral aspects like data sources.

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

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

With 100% schema description coverage, the input schema fully documents all parameters. The description adds minimal semantics by mentioning output formats and ranking aspects, but does not provide significant additional meaning beyond what the schema already covers, meeting the baseline for high coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Generate a publishable AEO Ranking article') and resource ('SaaS/API services'), distinguishing it from siblings like 'generate_aeo_report' by specifying the output is a ranking article with grades and methodology, not just any report.

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

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

The description implies usage for generating ranking articles in specific formats (markdown or JSON), but does not explicitly state when to use this tool versus alternatives like 'generate_aeo_report' or 'search_services'. It provides context on output formats but lacks explicit when/when-not guidance.

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

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

Annotations provide readOnlyHint=true and openWorldHint=false, indicating a safe read operation with limited scope. The description adds context about scoring criteria and the analogy to ESG ratings, which helps understand the report's nature, but it does not disclose behavioral traits like rate limits, data freshness, or output format details beyond what annotations cover.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is front-loaded with the core purpose in the first sentence, followed by scoring details and an analogy. It is appropriately sized with two sentences that efficiently convey the tool's function, though the analogy could be considered slightly extraneous.

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

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

Given the tool has annotations (readOnlyHint, openWorldHint) but no output schema, the description provides adequate context on what the tool does and scoring criteria. However, it lacks details on report format, data sources, or how scores are calculated, which could be important for a readiness assessment tool with no output schema.

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

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

Schema description coverage is 100%, with clear descriptions for all three parameters (top_n, category, include_recommendations). The description does not add meaning beyond the schema, such as explaining parameter interactions or constraints, so it meets the baseline for high schema coverage without extra value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool generates an AEO Readiness Report for Japanese SaaS services, specifying it scores services on four specific criteria (MCP availability, API docs, auth guides, agent success rates) and provides an analogy to ESG ratings. This distinguishes it from siblings like 'get_service_detail' or 'search_services' by focusing on readiness assessment rather than general service information.

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

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

The description implies usage for evaluating agent-readiness of Japanese SaaS services, but it does not explicitly state when to use this tool versus alternatives like 'search_services' or 'get_service_detail'. No exclusions or prerequisites are mentioned, leaving the context somewhat open-ended.

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

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

Annotations already declare readOnlyHint=true (safe read operation) and openWorldHint=false (limited scope), so the agent knows this is a non-destructive, bounded query. The description adds value by specifying the types of aggregated data returned (success rate, latency, etc.), which provides useful context beyond the annotations. However, it doesn't describe behavioral aspects like rate limits, authentication needs, or response format.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is a single, efficient sentence that front-loads the core purpose and enumerates the specific data types included. Every word contributes to understanding what the tool retrieves, with zero wasted verbiage or redundancy.

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

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

For a read-only tool with good annotations and full schema coverage, the description adequately covers what data is returned. However, with no output schema and multiple sibling tools that might overlap in functionality, the description could better clarify the tool's unique role. It's minimally complete but leaves ambiguity about differentiation from similar tools.

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

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

Schema description coverage is 100%, with the single parameter 'service_id' fully documented in the schema. The description doesn't add any parameter-specific information beyond what the schema provides (e.g., format examples, validation rules). Baseline score of 3 is appropriate when the schema handles parameter documentation adequately.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Get' and resource 'aggregated agent experience data for an MCP service', specifying what data is retrieved (success rate, latency, common errors, usage trends, confidence score). However, it doesn't explicitly differentiate from sibling tools like 'get_service_detail' or 'get_service_history', which might also retrieve service-related data.

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

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. With sibling tools like 'get_service_detail' and 'get_service_history' that might retrieve similar service data, there's no indication of what distinguishes this tool (aggregated insights vs. detailed records vs. historical data). No prerequisites or exclusions are mentioned.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=false, indicating this is a safe read operation with limited scope. The description adds valuable context beyond annotations by explaining what triggers anomalies (success rate crashes, contradicted workarounds, error spikes) and the workflow context (for scout agents to investigate). However, it doesn't mention rate limits, pagination, 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.

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

The description is efficiently structured in three sentences that each earn their place: first states the purpose, second provides context with analogy, third gives usage guidance. No wasted words, and the most important information (what the tool does) is front-loaded.

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

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

For a read-only tool with good annotations and full schema coverage, the description provides adequate context about the anomaly detection system and workflow. However, without an output schema, it doesn't describe the return format or structure of the inspection queue items, which would be helpful for the agent to understand what to expect.

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

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

Schema description coverage is 100%, so all parameters are well-documented in the schema itself. The description doesn't add any parameter-specific information beyond what's in the schema. The baseline score of 3 is appropriate when the schema does the heavy lifting for parameter documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

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 ('view anomalies that need verification') and identifies the resource ('inspection queue'). It distinguishes from sibling tools by explaining this is for viewing anomalies that need investigation, unlike other tools like submit_inspection or get_service_detail which have different functions.

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

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

The description provides explicit guidance on when to use this tool ('when KanseiLink detects suspicious patterns... they appear here for agents to investigate') and when to use an alternative ('Pick one and use submit_inspection to report your findings'). This clearly distinguishes it from the submit_inspection sibling tool.

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

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

Annotations indicate readOnlyHint=true and openWorldHint=false, covering safety and scope. The description adds context by specifying the return format ('step-by-step instructions with input/output mappings'), which isn't covered by annotations. However, it lacks details on rate limits, error handling, or authentication needs, leaving behavioral gaps despite the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is concise and front-loaded: a single sentence clearly states the tool's purpose and output. Every word earns its place with no redundancy or fluff, making it easy for an agent to quickly understand the core functionality.

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

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

Given the tool's complexity (workflow generation with multiple services) and lack of output schema, the description is moderately complete. It covers the purpose and output format but misses details on error cases, example outputs, or integration with sibling tools. Annotations help with safety, but more context would improve completeness for this non-trivial tool.

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

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. The description doesn't add any semantic details beyond what's in the schema (e.g., it doesn't explain 'goal' or 'services' further). Baseline 3 is appropriate as the schema handles parameter documentation adequately.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get a structured workflow recipe combining multiple MCP services' with 'step-by-step instructions with input/output mappings.' This specifies the verb ('Get'), resource ('structured workflow recipe'), and output format. However, it doesn't explicitly differentiate from sibling tools like 'find_combinations' or 'search_services,' which might have overlapping functionality.

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

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 'combining multiple MCP services' but doesn't specify prerequisites, constraints, or when other tools like 'find_combinations' or 'search_services' might be more appropriate. Usage is implied through the parameter descriptions but not explicitly stated.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=false, indicating a safe, bounded read operation. The description adds valuable behavioral context beyond annotations by specifying what information is returned (authentication setup, rate limits, quickstart example, agent tips), which helps the agent understand the tool's output format and utility.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is efficiently structured in two sentences: the first explains what the tool returns, and the second provides usage guidance. Every sentence adds value with no wasted words, making it easy to parse.

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

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

Given the tool's moderate complexity (single parameter, read-only operation) and lack of output schema, the description does well by detailing the return content (authentication, endpoints, rate limits, etc.) and providing usage context. However, it could slightly improve by mentioning any limitations or prerequisites beyond the sibling tool reference.

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

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

Schema description coverage is 100%, with the parameter 'service_id' fully documented in the schema. The description doesn't add any additional parameter semantics beyond what the schema provides, so it meets the baseline score of 3 for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Get' and the resource 'full API connection guide for a Japanese SaaS service', specifying the exact content returned (authentication setup, key endpoints, etc.). It explicitly distinguishes from sibling tool 'search_services' by indicating this is for learning 'HOW to connect' after using that tool.

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

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

The description provides explicit guidance on when to use this tool ('Use after search_services to learn HOW to connect'), naming the alternative tool and specifying the sequential workflow. This gives clear context for tool selection.

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

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 the tool shows 'trends, regressions, competitive position changes, and event correlations', which adds some context about output behavior. However, it fails to disclose critical traits like whether this is a read-only operation, potential rate limits, authentication needs, or data format. For a tool with no annotations, this is insufficient.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is appropriately sized with two sentences that are front-loaded: the first states the core purpose, and the second adds usage context. There's minimal waste, though the second sentence could be slightly more precise. Overall, it's efficient.

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

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 3 parameters with full schema coverage, the description is moderately complete. It covers the purpose and usage context but lacks details on behavioral traits and output format. For a tool that returns time-series data, more information on result structure would be helpful, making it adequate but with clear gaps.

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

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 well. The description adds no specific parameter semantics beyond what's in the schema (e.g., it doesn't explain 'compare_with' further or detail 'period' options). Baseline 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.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Get' and resource 'time-series metrics history for a service', specifying what the tool does. It distinguishes from siblings like 'get_service_detail' by focusing on historical trends rather than current details. However, it doesn't explicitly contrast with all similar tools like 'get_insights' or 'search_services', keeping it at 4 rather than 5.

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

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

The description provides implied usage guidance with 'Use for consulting reports', suggesting a specific context. However, it lacks explicit when-to-use vs. alternatives (e.g., compared to 'get_insights' or 'search_services') and doesn't mention prerequisites or exclusions. This leaves gaps in fully guiding the agent.

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

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

Annotations indicate readOnlyHint=true and openWorldHint=false, which the description aligns with by describing a read-only informational tool. The description adds value beyond annotations by specifying the types of tips returned (auth setup, common pitfalls, workarounds, reliability data) and the preparatory use case. However, it doesn't disclose additional behavioral traits like rate limits, error conditions, or data freshness, which could be useful given the lack of output schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is efficiently structured in two sentences: the first states the purpose and return values, and the second provides a helpful analogy. Every sentence adds value without redundancy, and it's front-loaded with key information. It's appropriately sized for a single-parameter tool with good annotations.

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

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

Given the tool's moderate complexity (informational lookup), rich annotations (readOnlyHint, openWorldHint), and high schema coverage, the description is mostly complete. It clearly explains what the tool does and when to use it. The main gap is the lack of output schema, but the description compensates by listing the types of information returned. However, it could benefit from more detail on output structure or limitations.

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

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

The input schema has 100% description coverage, with one required parameter 'service_id' fully documented in the schema. The description doesn't add any parameter-specific details beyond what the schema provides, such as format examples or validation rules. Since schema coverage is high, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't need to given the comprehensive schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

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 ('get practical tips') and resources ('MCP service'), and outlines what information is returned (auth setup, common pitfalls, workarounds, reliability data). It distinguishes from siblings like get_service_detail or get_service_history by focusing on preparatory guidance rather than operational details or historical data. However, it doesn't explicitly contrast with all sibling tools, keeping it at a 4 rather than a 5.

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

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

The description provides clear context for when to use this tool: 'before using an MCP service' and includes a helpful analogy ('Like checking restaurant reviews before visiting'). This implies it's for preparatory research. It doesn't explicitly state when not to use it or name alternatives among siblings, but the context is sufficiently clear to guide appropriate usage.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=false, indicating this is a safe read operation with closed-world data. The description adds useful context about the queue nature and ordering by creation date, but doesn't provide additional behavioral details like pagination, rate limits, or authentication requirements beyond what annotations cover.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is perfectly concise with two sentences that each serve a clear purpose: the first explains what the tool does, and the second provides usage guidance. There's no wasted language or redundancy.

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

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

For a read-only tool with good annotations and full schema coverage, the description provides adequate context about purpose and usage. However, without an output schema, the description doesn't explain what the return data looks like (e.g., format, fields, structure), which would be helpful for an agent to understand the response.

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

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

Schema description coverage is 100%, so all parameters are well-documented in the schema. The description doesn't add any parameter-specific information beyond what's in the schema, but the schema provides complete documentation for all three parameters with descriptions, defaults, and enum values.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('View the queue of proposed service updates awaiting review'), identifies the resource ('proposed service updates'), and distinguishes from siblings by mentioning the ordering ('ordered by creation date'). It explicitly differentiates from 'review_update' for approval/rejection actions.

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

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

The description provides explicit guidance on when to use this tool ('to view the queue of proposed service updates awaiting review') and when to use an alternative ('Use review_update to approve or reject individual proposals'). It clearly distinguishes between viewing pending updates and taking action on them.

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

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

The annotations indicate this is a write operation (readOnlyHint: false) and open-world (openWorldHint: true). The description adds valuable behavioral context beyond annotations: it explains the proposal-review-approval workflow, mentions trust score recalculation, and clarifies that changes aren't immediate but go through a queue. No contradictions with annotations exist.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is perfectly front-loaded with the core purpose in the first sentence, followed by usage guidance and behavioral context. Every sentence earns its place by providing distinct value: analogy, usage triggers, workflow explanation, and outcome. No wasted words or redundant information.

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

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

For a mutation tool with no output schema, the description provides excellent context about the proposal workflow and outcomes. It covers purpose, usage, and behavioral aspects well. The only minor gap is lack of explicit error case handling or what happens if a proposal is rejected, but given the rich annotations and clear schema, this is still quite complete.

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

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

With 100% schema description coverage, the schema already documents all 6 parameters thoroughly. The description doesn't add significant parameter-specific information beyond what's in the schema descriptions. It mentions 'changes' generally but doesn't provide additional syntax or format details. Baseline 3 is appropriate when schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Propose a change to a service's data') and resource ('service'), with a helpful analogy ('like a GitHub Pull Request'). It distinguishes this from potential siblings by focusing on proposing changes rather than directly applying them, which is unique among the listed tools.

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

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 ('when you discover that a service has new endpoints, changed APIs, updated auth methods, or any other data that should be corrected'). It also provides clear context about the workflow ('goes into a review queue') and outcome ('Once approved, the change is automatically applied'), which helps differentiate it from direct update tools.

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

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 what the tool does (read aggregated opinions) but lacks details on behavioral traits such as whether it's a read-only operation, potential rate limits, authentication needs, or how data is aggregated. The description adds some context about agent types and content but doesn't compensate for the absence of annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is concise and front-loaded, with two sentences that directly state the tool's purpose and context. Every sentence earns its place by providing essential information without redundancy. However, it could be slightly more structured by explicitly separating purpose from usage guidance.

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

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

Given the complexity (3 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain return values, aggregation methods, or error conditions, leaving gaps for the agent to infer. While the purpose is clear, the lack of behavioral and output details makes it inadequate for full contextual understanding.

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

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 (agent_type, service_id, question_id) with descriptions. The description adds no additional parameter semantics beyond what's in the schema, such as explaining relationships between parameters or usage examples. Baseline 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.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Read aggregated agent opinions about a service' with specific details about what is shown (selection criteria, frustrations, recommendations) and the agent types involved (Claude, GPT, Gemini). It distinguishes from siblings by focusing on aggregated opinions rather than individual feedback or other service-related operations. However, it doesn't explicitly differentiate from all siblings like 'read_feedback' which might be similar.

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

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

The description provides minimal usage guidance: it states the tool is 'Essential for consulting reports,' implying a specific context, but doesn't specify when to use this tool versus alternatives like 'read_feedback' or 'get_insights.' No explicit when-not-to-use or prerequisite information is provided, leaving the agent to infer usage based on the purpose alone.

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

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

Annotations provide readOnlyHint=true and openWorldHint=false, indicating this is a safe read operation with limited scope. The description adds minimal behavioral context beyond this—it mentions 'recent feedback' but doesn't specify time ranges, pagination, or data freshness. It doesn't contradict annotations, but with annotations covering safety, the description could add more about what 'recent' means or how results are ordered.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is concise and front-loaded, with two sentences that directly state the purpose and usage context. There's no wasted verbiage, and it efficiently communicates the core function. However, it could be slightly more structured by explicitly separating purpose from guidelines, but this is minor.

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

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

Given the tool's complexity (a read operation with 4 optional parameters), annotations cover safety, and schema coverage is complete, the description is adequate but has gaps. It doesn't explain output format (no output schema exists), doesn't detail ordering or recency of results, and lacks sibling differentiation. For a tool with good annotations and schema, it meets minimum viability but isn't fully comprehensive.

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

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

Schema description coverage is 100%, so all parameters are documented in the schema itself. The description doesn't add any parameter-specific details beyond what the schema provides—it doesn't explain what 'type' or 'service_id' refer to, or clarify the 'status' enum values. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate with additional semantic context.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'View recent feedback from agents.' It specifies the resource (feedback from agents) and the action (view). However, it doesn't explicitly differentiate this tool from potential siblings like 'read_agent_voices' or 'submit_feedback', which might handle similar feedback-related operations. The mention of 'operators and scout agents' provides some context but doesn't fully distinguish it from alternatives.

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

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

The description provides implied usage guidance by stating it's 'Useful for operators and scout agents to see what the community is asking for.' This suggests a monitoring or insight-gathering context. However, it lacks explicit guidance on when to use this tool versus alternatives like 'get_insights' or 'read_agent_voices', and doesn't specify any exclusions or prerequisites for usage.

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

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 explains the purpose and usage context, it doesn't disclose important behavioral traits like whether this creates permanent records, requires specific permissions, has rate limits, or what happens on success/failure. For a write operation with zero annotation coverage, 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.

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

Two sentences with zero waste - the first defines the tool's purpose with examples, the second explains its specific usage context. Every word earns its place, and the most important information (what it does) comes first.

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

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

For a write operation with no annotations and no output schema, the description provides adequate purpose and usage context but lacks behavioral details about the operation itself. The 100% schema coverage helps with parameters, but the description doesn't compensate for missing annotation information about the tool's behavior and effects.

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

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 6 parameters thoroughly with descriptions, enums, and defaults. The description doesn't add any parameter-specific information beyond what's in the schema, so it meets the baseline of 3 when schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

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 verb ('Record') and resource ('external event'), plus examples of event types. It distinguishes from siblings by explaining this is for correlating metric changes with real-world causes in consulting reports, which no other tool name suggests.

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

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

The description provides clear context for when to use this tool ('to correlate metric changes with real-world causes in consulting reports') and gives examples of event types. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools.

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

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

Annotations indicate this is not read-only and not idempotent, which the description doesn't contradict. The description adds valuable behavioral context beyond annotations: it explains the purpose ('Helps other agents make better decisions') and data handling practices ('All data is anonymized and PII is auto-masked'). However, it doesn't mention rate limits, authentication requirements, or specific side effects, which could be useful for a non-idempotent tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is extremely concise and front-loaded: two sentences that directly state the tool's purpose and key behavioral aspects. Every word earns its place, with no redundancy or unnecessary elaboration. It efficiently communicates the essential information without wasting space.

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

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

For a tool with 14 parameters, 100% schema coverage, and no output schema, the description provides adequate context. It explains the tool's purpose and data handling, which complements the detailed parameter documentation in the schema. However, it could be more complete by mentioning the tool's non-idempotent nature or providing examples of when to use it, especially given the lack of output schema.

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

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

The input schema has 100% description coverage, so parameters are well-documented in the schema itself. The description doesn't add any specific parameter details beyond what's in the schema, such as explaining relationships between parameters or providing examples. Given the high schema coverage, a baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't need to.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Report your experience using an MCP service' with the goal of 'Helps other agents make better decisions.' This specifies both the action (reporting) and the resource (MCP service experience). However, it doesn't explicitly differentiate this from sibling tools like 'submit_feedback' or 'record_event,' which might 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.

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

The description implies usage context ('using an MCP service') and mentions data handling ('All data is anonymized and PII is auto-masked'), but doesn't provide explicit guidance on when to use this tool versus alternatives like 'submit_feedback' or 'record_event.' It also doesn't specify prerequisites or exclusions, leaving usage somewhat open to interpretation.

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

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

Annotations indicate this is a write operation (readOnlyHint=false) and closed-world (openWorldHint=false), but the description adds valuable behavioral context beyond that: it explains the consequences of approval (changes applied, changelog created, trust scores recalculated) and specifies user restrictions ('operators and admin agents only'). This provides important operational details not captured in annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is efficiently structured in two sentences: the first states the core purpose and outcomes, the second adds crucial access restrictions. Every word serves a purpose with no redundancy, making it easy to parse while being information-dense.

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

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

For a mutation tool with no output schema, the description does well by explaining the behavioral outcomes of approval. However, it doesn't mention what happens on rejection (e.g., whether the proposal is archived or deleted) or describe the return format. Given the annotations cover safety aspects and the schema handles parameters, it's mostly complete but has minor gaps.

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

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

With 100% schema description coverage, the input schema already documents all 4 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't explain proposal_id format or action implications). The baseline score of 3 reflects adequate coverage through the schema alone.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Approve or reject') and resource ('pending service update proposal'), distinguishing it from siblings like 'propose_update' (which creates proposals) and 'list_pending_updates' (which only lists them). It explicitly identifies the tool's function as a decision-making operation on pending updates.

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

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

The description provides clear context for when to use this tool ('for operators and admin agents only'), indicating it's restricted to privileged users. However, it doesn't explicitly mention when NOT to use it or name alternatives (e.g., 'propose_update' for creating proposals or 'list_pending_updates' for viewing them), though the sibling tools suggest a workflow context.

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

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

Annotations provide readOnlyHint=true and openWorldHint=false, indicating a safe read operation with a closed dataset. The description adds value by specifying the search scope ('Japanese SaaS MCP services') and output characteristics ('ranked results with trust scores and usage data'), which aren't covered by annotations. However, it doesn't disclose behavioral details like rate limits, authentication needs, or pagination behavior beyond the 'limit' parameter in the schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is a single, efficient sentence that front-loads the core purpose ('Search for Japanese SaaS MCP services by intent or category') and follows with essential output details. Every word earns its place, with no redundancy or unnecessary elaboration, making it highly concise and well-structured for quick comprehension.

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

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

Given the tool's moderate complexity (5 parameters, 1 required), rich schema coverage (100%), and annotations (readOnlyHint, openWorldHint), the description is largely complete. It clearly states the tool's purpose and output format. However, without an output schema, it could benefit from more detail on result structure (e.g., fields in ranked results), though the mention of 'trust scores and usage data' provides some context. Sibling tools are distinct, reducing the need for extensive differentiation.

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

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

Schema description coverage is 100%, with all parameters well-documented in the input schema (e.g., 'intent' describes what to accomplish, 'agent_ready' explains readiness levels). The description adds minimal semantics beyond the schema, only implying that 'intent or category' are key filters. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description doesn't significantly enhance parameter understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Search for Japanese SaaS MCP services'), resource ('services'), and scope ('by intent or category'), with explicit mention of what is returned ('ranked results with trust scores and usage data'). It distinguishes itself from siblings like get_service_detail or get_service_history by focusing on search functionality rather than retrieval of specific records.

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

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

The description implies usage context through the mention of 'intent or category' and the return of 'ranked results', suggesting it's for discovery purposes. However, it lacks explicit guidance on when to use this tool versus alternatives like get_service_detail for specific service information or find_combinations for other types of searches. No exclusions or prerequisites are stated.

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

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

Annotations indicate readOnlyHint=false and openWorldHint=false, but the description adds valuable behavioral context: it clarifies that this is a submission tool for feedback that 'helps improve KanseiLink for all agents', implying it's a write operation with community impact. No contradictions exist with annotations, and the description enhances understanding of the tool's role beyond basic annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is efficiently structured in three sentences: it states the tool's purpose, provides usage examples and flexibility, and explains the broader impact. Every sentence adds value without redundancy, making it front-loaded and appropriately concise for its informative role.

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

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

Given the tool's moderate complexity (6 parameters, no output schema), the description is largely complete: it clarifies the tool's purpose, usage, and behavioral context. However, it lacks details on response format or confirmation mechanisms, which could be helpful since there's no output schema. Annotations cover basic hints, but the description compensates well overall.

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

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

With 100% schema description coverage, the schema fully documents all 6 parameters. The description adds minimal parameter semantics beyond the schema, mainly reinforcing the free-form nature of the 'body' parameter. It doesn't provide additional syntax or format details, so a baseline score of 3 is appropriate given the comprehensive schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

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 ('submit', 'write') and resource ('feedback'), distinguishing it from siblings like 'read_feedback' or 'audit_cost'. It explicitly defines the tool as a 'free-space suggestion box for agents' with examples of content types, making its function unambiguous and distinct.

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

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

The description provides explicit usage guidelines: it specifies when to use ('Write anything: improvement ideas, missing data reports, workaround tips, feature requests, bug reports, or corrections') and emphasizes flexibility ('No rigid format — express what you need in your own words'). It also implicitly distinguishes from structured reporting tools by highlighting its free-form nature.

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

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

Annotations indicate this is a non-readOnly, non-idempotent mutation tool. The description adds valuable behavioral context beyond annotations: it explains the tool updates 'trust scores' and helps 'the colony self-correct,' discloses that findings will have 'PII auto-masked' (though this is also in the schema), and clarifies the tool's role in a workflow (verifying anomalies). It doesn't mention rate limits or auth needs, but with annotations covering the mutation nature, this is sufficient.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is appropriately sized (three sentences) and front-loaded with the core purpose. The scout ant metaphor adds color without being verbose, and every sentence contributes to understanding the tool's role and effects. It could be slightly more direct but remains efficient.

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

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

For a mutation tool with 5 parameters, 100% schema coverage, and no output schema, the description provides good contextual completeness. It explains the tool's purpose, behavioral impact (updates trust scores, self-correction), and workflow context. The main gap is lack of output details, but given the annotations and schema richness, this is acceptable.

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

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 all 5 parameters. The description doesn't add any parameter-specific details beyond what's in the schema (e.g., it doesn't explain 'inspection_id' linkage or 'verdict' implications further). Baseline 3 is appropriate when the schema carries the full burden.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('submit your verification of an anomaly'), the resource ('inspection queue'), and distinguishes it from siblings like 'get_inspection_queue' (which retrieves inspections) and 'report_outcome' (which might be more general). The scout ant metaphor reinforces the verification/testing purpose.

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

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

The description provides clear context for when to use this tool ('verify an anomaly from the inspection queue') and implies it follows 'get_inspection_queue' (by referencing the ID parameter). However, it doesn't explicitly state when NOT to use it or name alternatives among siblings (e.g., how it differs from 'report_outcome').

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

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 implies a write operation ('capture') and specifies a daily cadence, but does not detail permissions, side effects, error handling, or output format, leaving gaps in behavioral understanding.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is appropriately sized with two sentences, front-loaded with the core purpose and followed by usage guidance, with no redundant or unnecessary information, making it highly efficient and well-structured.

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

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

Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is adequate but incomplete. It covers purpose and usage but lacks details on output, error cases, or integration with other tools, which would be helpful for full contextual understanding.

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

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 fully. The description adds no additional parameter semantics beyond what the schema provides, such as explaining the implications of omitting 'service_id' or the format of 'snapshot_date', meeting the baseline for high coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

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 ('capture daily metrics snapshot') and resource ('for time-series analysis'), and distinguishes it from siblings by focusing on snapshot creation rather than other operations like searching, generating reports, or recording events.

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

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

The description provides clear context on when to use it ('Run daily to build historical data for consulting reports'), but does not explicitly state when not to use it or name alternatives among the sibling tools, such as for real-time metrics or other data collection methods.

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