MCP Art Supply Store

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation3/5
The tools cover distinct functions like inventory, sales, social media, and customer management, but there is notable overlap in areas like social media analytics (analyze_post_performance, get_social_analytics) and reporting (generate_eod_report, get_sales_report), which could cause confusion. Most tools have clear purposes, but the redundancy in some domains reduces clarity.
Naming Consistency4/5
The naming follows a consistent verb_noun pattern throughout (e.g., check_inventory, calculate_discount, generate_post_ideas), with only minor deviations like 'auto_respond_common_questions' using a verb_verb_noun structure. Overall, the naming is predictable and readable, supporting easy tool identification.
Tool Count2/5
With 36 tools, the count is excessive for an art supply store server, leading to potential overwhelm and inefficiency. A more focused set of 10-20 tools would better cover the domain without unnecessary complexity, as many tools could be consolidated or omitted for a cleaner interface.
Completeness5/5
The tool set provides comprehensive coverage for an art supply store, including inventory management, sales tracking, customer relations, social media marketing, and financial operations. It supports full CRUD-like workflows (e.g., check_inventory, update_stock, create_purchase_order) and addresses key business needs without obvious gaps, ensuring agents can handle typical tasks effectively.
Average 3.1/5 across 36 of 36 tools scored.
See the Tool Scores section below for per-tool breakdowns.
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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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'insights on what content performs best', which hints at analytical output, but fails to describe key behaviors: whether it's read-only or mutative, what data sources it uses (e.g., Facebook/Instagram as per schema), response format, or any limitations like rate limits or authentication needs. This leaves significant gaps for an agent to understand how to invoke it effectively.
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 directly states the tool's purpose without unnecessary words. It is front-loaded with the core action and resource, making it easy to parse. Every part of the sentence contributes to understanding, with no wasted verbiage.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of analyzing social media performance, the description is incomplete. No annotations are provided to clarify safety or behavior, and there is no output schema to describe return values. The description lacks details on what 'insights' entail, how results are structured, or any contextual constraints. This makes it inadequate for an agent to fully grasp the tool's operation and output.
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 clear documentation for both parameters: 'postId' as a Facebook or Instagram post ID and 'compareToAverage' as a boolean for comparing to account average. The description adds no additional parameter semantics beyond what the schema provides, such as format examples or usage tips. Baseline score of 3 is appropriate since the schema adequately covers parameter details.
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 as analyzing individual post performance with insights on content performance. It specifies the verb 'analyze' and resource 'individual post performance', making it understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_social_analytics' or 'track_competitor_activity', which might also involve performance analysis.
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 prerequisites, context for analysis, or compare it to sibling tools such as 'get_social_analytics' or 'generate_post_ideas' that might overlap in functionality. Usage is implied 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.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions enabling/disabling auto-responses but doesn't describe what happens when enabled (e.g., whether responses are immediate, what format they take, if they're customizable, or any rate limits). This leaves significant gaps for a configuration tool that likely modifies system behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that gets straight to the point with zero wasted words. It's appropriately sized for a simple configuration tool and front-loads the core functionality.
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 configuration tool with no annotations and no output schema, the description is insufficient. It doesn't explain what 'automatic responses' entail, how they're delivered, what the response content looks like, or any side effects. The agent would need to guess about the tool's behavior and outcomes.
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 minimal value beyond the schema - it mentions 'hours, location, stock availability' which partially overlaps with the 'questionTypes' parameter examples, but doesn't provide additional semantic context. 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.
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: 'Enable automatic responses to frequently asked questions' with specific examples (hours, location, stock availability). This provides a clear verb+resource combination, though it doesn't explicitly differentiate from sibling tools like 'suggest_comment_reply' or 'check_inventory' which might handle similar domains differently.
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 prerequisites, appropriate contexts, or exclusions. For example, it doesn't clarify if this should be used instead of manual responses or other automation tools in the sibling list.
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. While 'Schedule a new appointment' implies a write/mutation operation, the description doesn't address important behavioral aspects like authentication requirements, error conditions, whether appointments can be modified/canceled, confirmation mechanisms, or what happens on success/failure.
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 gets straight to the point with no wasted words. It's appropriately sized for the tool's complexity and front-loads the essential information about what the tool does.
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 mutation tool with no annotations and no output schema, the description is insufficient. It doesn't address what happens after scheduling (confirmation? ID returned?), error handling, or behavioral constraints. The description should provide more context about the operation's implications and expected outcomes.
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?
With 100% schema description coverage, the schema already documents all 4 parameters thoroughly. The description adds minimal value by mentioning 'custom framing, consultations, or workshops' which relates to the 'service' parameter, but doesn't provide additional context beyond what's in the schema descriptions. 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.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Schedule a new appointment') and specifies the resource types ('custom framing, consultations, or workshops'), making the purpose immediately understandable. However, it doesn't differentiate from potential sibling tools like 'check_appointments' or 'get_employee_schedule' that might involve appointment-related operations.
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. There are multiple sibling tools that might be relevant (check_appointments, get_employee_schedule), but the description offers no context about prerequisites, appropriate scenarios, or when other tools might be more suitable.
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 but only states what the tool does, not how it behaves. It doesn't disclose whether this is a read-only calculation, if it requires specific permissions, how it handles missing data, or what format the output takes. For a calculation tool with zero annotation coverage, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. Every element ('calculate labor costs', 'specific period', 'employee hours and rates') contributes directly to understanding the tool's function.
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 calculation tool with no annotations and no output schema, the description is insufficient. It doesn't explain what the calculation returns (e.g., total cost, breakdown by employee), how it sources employee data, or error conditions. The context signals indicate this tool needs more behavioral disclosure than provided.
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 description mentions 'specific period' which aligns with the startDate and endDate parameters in the schema. With 100% schema description coverage providing format details (YYYY-MM-DD), the description adds minimal value beyond what the schema already documents, meeting the baseline for high coverage.
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 'calculate' and the resource 'labor costs', specifying it's based on employee hours and rates for a specific period. It distinguishes from siblings like 'calculate_discount' or 'calculate_profit_margin' by focusing on labor, but doesn't explicitly differentiate from other financial tools beyond naming.
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_daily_sales' or 'calculate_profit_margin'. It mentions 'specific period' but doesn't specify prerequisites, exclusions, or comparative contexts with sibling tools.
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. It states it 'calculates' profit margin, implying a read-only operation, but doesn't disclose behavioral traits such as whether it requires specific permissions, how it handles missing data, or what the output format might be. For a calculation tool with zero annotation coverage, 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 a single, efficient sentence that front-loads the key action and resource. It avoids unnecessary words, though it could be slightly more precise (e.g., clarifying the missing 'sellingPrice' parameter). Overall, it's appropriately sized with no wasted content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a calculation tool with no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., percentage, value), how errors are handled, or dependencies on other data. With 2 parameters and 100% schema coverage, the input is covered, but the overall context lacks sufficient detail for reliable 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?
The input schema has 100% description coverage, with 'costPrice' and 'sku' clearly documented. The description adds that the calculation is 'based on cost and selling price', but 'sellingPrice' is not a parameter in the schema, which might cause confusion. It provides minimal additional meaning beyond the schema, so the baseline score of 3 is appropriate given the high schema coverage.
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 'calculate' and the resource 'profit margin for a product or category', specifying it's based on cost and selling price. It distinguishes from siblings like 'calculate_discount' or 'calculate_labor_cost' by focusing on profit margin, though it doesn't explicitly contrast them. The purpose is specific but could be more distinct from similar 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 like 'calculate_discount' or 'get_inventory_value'. It mentions the calculation is based on cost and selling price, but doesn't specify prerequisites, exclusions, or ideal contexts. Usage is implied from the purpose but lacks explicit direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool 'checks' appointments, implying a read-only operation, but doesn't clarify permissions, rate limits, output format, or whether it returns all appointments or filtered results. This leaves significant gaps for a tool that likely queries sensitive data.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part earns its place by specifying the action, resource, and scope concisely.
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, no output schema, and potential complexity in handling appointments (e.g., privacy, filtering), the description is inadequate. It doesn't explain return values, error conditions, or behavioral nuances, leaving the agent under-informed about how to interpret results.
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 the 'date' parameter fully documented in the schema itself. The description adds no additional parameter semantics beyond implying a 'customer' parameter that isn't in the schema, creating confusion rather than value. Baseline 3 is appropriate given 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 ('check') and resource ('scheduled appointments'), and specifies the scope ('for a specific date or customer'). However, it doesn't explicitly differentiate from sibling tools like 'get_employee_schedule' or 'book_appointment', which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides minimal guidance by mentioning 'for a specific date or customer', but offers no explicit advice on when to use this tool versus alternatives like 'get_employee_schedule' or 'lookup_customer'. There's no mention of prerequisites, exclusions, or recommended contexts, leaving usage ambiguous.
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 mentions comparing 'prices and terms' but does not specify how the comparison is performed (e.g., real-time vs. cached data), what the output format might be, or any limitations like rate limits or authentication needs. This leaves significant gaps in understanding the tool's behavior beyond its basic function.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose without any wasted words. It is appropriately sized for the tool's complexity, making it easy to parse and understand 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 lack of annotations and output schema, the description is incomplete for a tool that performs comparison operations. It does not explain what the comparison output includes (e.g., structured data, summary), how results are formatted, or any behavioral traits like data freshness or error handling. This leaves the agent with insufficient context to use the tool effectively beyond basic invocation.
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 'productName' clearly documented as 'Product name or category'. The description adds no additional parameter semantics beyond what the schema provides, such as examples or constraints. Since the schema coverage is high, the baseline score of 3 is appropriate, as the description does not compensate but also does not 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 with a specific verb ('compare') and resources ('prices and terms across suppliers for a specific product or category'), making it easy to understand what it does. However, it does not explicitly differentiate from sibling tools like 'get_supplier_info' or 'search_products', which might also involve supplier or product data, leaving some ambiguity about uniqueness.
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, such as 'get_supplier_info' for general supplier details or 'search_products' for product searches. It lacks explicit context, prerequisites, or exclusions, leaving the agent to infer usage based on the purpose alone without clear differentiation.
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 mentions creating a campaign but doesn't disclose critical behavioral traits such as required permissions, whether this is a write operation (implied by 'Create'), potential side effects, rate limits, or what happens upon execution. This leaves significant gaps for a tool that likely modifies system state.
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, well-structured sentence that efficiently conveys the tool's purpose without unnecessary words. It's front-loaded with the core action and resource, making it easy to parse. Every part of the sentence earns its place by contributing essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations and no output schema that creates campaigns (likely a write operation), the description is incomplete. It doesn't address behavioral aspects like permissions, side effects, or response format, nor does it provide usage differentiation from siblings. The high schema coverage helps with parameters, but other critical context is missing.
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, clearly documenting all three parameters. The description adds no additional parameter semantics beyond what's in the schema, such as format details or examples. 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 action ('Create a multi-post campaign') and the resource ('to promote a specific product or collection across platforms'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'post_to_social_media' or 'schedule_weekly_posts', 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.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides minimal guidance on when to use this tool, stating it's for promoting products/collections across platforms, but offers no explicit when-not-to-use advice or alternatives. It doesn't clarify how this differs from similar tools like 'post_to_social_media' or 'generate_post_ideas', leaving usage context implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the report includes 'sales summary, cash reconciliation needs, and action items,' which gives some output context, but doesn't cover critical aspects like whether this is a read-only operation, if it requires specific permissions, or how it handles errors. 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part ('generate end-of-day report' and the listed components) contributes directly to understanding the tool's function, making it highly concise and well-structured.
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 lack of annotations and output schema, the description is incomplete. It hints at output components but doesn't detail format, structure, or behavioral traits like side effects or error handling. For a report-generation tool with no structured support, more context is needed to guide the agent effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 1 parameter with 100% description coverage, documenting the 'date' field format and default. The description adds no parameter-specific information beyond implying a date context, so it doesn't compensate but doesn't detract either. Baseline 3 is appropriate as the schema handles the parameter documentation adequately.
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 end-of-day report with sales summary, cash reconciliation needs, and action items.' It specifies the verb ('generate') and resource ('end-of-day report') with concrete components. However, it doesn't explicitly differentiate from siblings like 'get_daily_sales' or 'get_sales_report', which might have overlapping functions.
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 prerequisites, timing (e.g., at end of business day), or how it differs from sibling tools such as 'get_daily_sales' or 'get_sales_report', leaving the agent to infer usage context.
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 states the tool generates hashtags but does not describe how it works (e.g., based on algorithms, trends, or databases), potential limitations (e.g., rate limits, accuracy), or output format (e.g., list of strings). This leaves gaps in understanding the tool's behavior and constraints, which is critical for an AI agent to invoke it correctly.
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: 'Generate relevant hashtags for posts to maximize reach and engagement.' It is front-loaded with the core purpose and avoids unnecessary words or redundancy. Every part of the sentence contributes value, 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.
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 (3 parameters, no annotations, no output schema), the description is incomplete. It lacks details on behavioral aspects (e.g., how hashtags are generated, any dependencies), output format, and error handling. Without annotations or an output schema, the description should provide more context to ensure the agent can use the tool effectively, but it falls short, leaving significant gaps in understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with clear documentation for each parameter (count, includeLocation, postTopic). The description does not add any additional meaning beyond the schema, such as explaining parameter interactions or usage tips. Since schema coverage is high, the baseline score of 3 is appropriate, as the schema adequately handles parameter semantics without extra description input.
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 relevant hashtags for posts to maximize reach and engagement.' It specifies the verb ('generate'), resource ('hashtags'), and goal ('maximize reach and engagement'), making it easy to understand. However, it does not explicitly differentiate from sibling tools like 'generate_post_ideas' or 'get_instagram_story_ideas', which could also involve content generation, so it misses full sibling distinction.
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 the goal of maximizing reach and engagement but does not specify contexts, prerequisites, or exclusions. For example, it does not clarify if this is for social media posts, blogs, or other content, or how it differs from sibling tools like 'generate_post_ideas'. This lack of explicit usage instructions limits its effectiveness in 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool 'Get[s] personalized product recommendations' but doesn't specify whether this is a read-only operation, what permissions might be required, how recommendations are generated (e.g., algorithm details, data sources beyond purchase history), or any rate limits. For a tool with no annotation coverage, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded with the core action and resource, making it easy to parse quickly, and every part of the sentence contributes meaningfully to understanding the tool.
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 lack of annotations and output schema, the description is incomplete for effective tool use. It doesn't explain what the recommendations output looks like (e.g., format, fields), behavioral traits like safety or performance, or how to handle edge cases. For a tool that likely returns complex data (product recommendations), this leaves the agent under-informed.
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 the parameter 'customerId' documented as 'Customer ID'. The description adds context by implying that recommendations are based on 'customer purchase history and preferences', which relates to the 'customerId' parameter, but it doesn't provide additional semantic details beyond what the schema already covers. This 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.
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 ('Get') and resource ('personalized product recommendations'), and it specifies the basis ('based on customer purchase history and preferences'). However, it doesn't explicitly distinguish this tool from potential siblings like 'suggest_bundle' or 'get_best_sellers', which might also involve recommendations, leaving some ambiguity in sibling differentiation.
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 prerequisites (e.g., needing a valid customer ID), exclusions, or comparisons to sibling tools like 'suggest_bundle' or 'lookup_customer', leaving the agent to infer usage context without explicit direction.
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 states 'Get' implying a read operation, but doesn't disclose behavioral traits like whether it returns all employees or filtered results, pagination, authentication needs, rate limits, or error conditions. This is inadequate for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It front-loads the core purpose ('Get employee schedule and shift information') and adds brief context ('for staffing planning'), making it appropriately sized and well-structured.
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 no annotations, no output schema, and a single parameter with full schema coverage, the description is incomplete. It lacks details on return values (e.g., format, scope), behavioral constraints, or error handling, which are critical for a tool with no structured data to compensate.
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, documenting the 'date' parameter as 'Date in YYYY-MM-DD format or day of week'. The description adds no additional parameter semantics beyond this, so it meets the baseline of 3 where 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 verb 'Get' and the resource 'employee schedule and shift information', with the purpose 'for staffing planning' providing context. It distinguishes from most siblings (e.g., sales or inventory tools), but doesn't explicitly differentiate from similar scheduling tools like 'check_appointments' or 'book_appointment'.
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 explicit guidance on when to use this tool versus alternatives is provided. The description mentions 'staffing planning' as context, but doesn't specify prerequisites, exclusions, or compare to other tools like 'check_appointments' or 'get_daily_sales' for related 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 the full burden of behavioral disclosure. It mentions the tool is 'critical for preventing stockouts', which hints at its importance but does not detail behavioral traits such as read-only vs. destructive operations, authentication needs, rate limits, or response format. The description is too vague to adequately inform the agent about how the tool behaves beyond its basic purpose.
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 and front-loaded, consisting of two sentences that directly state the tool's purpose and its importance. There is no unnecessary information or redundancy. However, it could be slightly improved by integrating usage guidance more seamlessly, but overall, it is efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of inventory management and the lack of annotations and output schema, the description is incomplete. It does not explain what the return values look like (e.g., list format, fields included), behavioral constraints, or how it differs from related tools. For a tool that likely returns critical business data, more context is needed to ensure the agent can use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with one optional parameter 'category' fully documented in the schema. The description does not add any parameter-specific information beyond what the schema provides, such as explaining the 'reorder level' concept or how filtering interacts with it. Since the schema coverage is high, the baseline score of 3 is appropriate, as the description does not compensate but also does not 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: 'Get a list of all items that are at or below their reorder level.' It specifies the verb ('get'), resource ('items'), and condition ('at or below their reorder level'), making the function unambiguous. However, it does not explicitly differentiate from sibling tools like 'check_inventory' or 'update_stock', which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides minimal usage guidance: 'Critical for preventing stockouts' implies a use case but does not specify when to use this tool versus alternatives like 'check_inventory' or 'update_stock'. No explicit when/when-not instructions or prerequisite conditions are mentioned, leaving the agent with insufficient context 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 the full burden of behavioral disclosure. It states the tool 'generates' a report, implying a read operation, but lacks details on permissions, rate limits, output format, or whether it's cached/real-time. For a reporting tool with zero annotation coverage, 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 a single, efficient sentence that front-loads the core action ('Generate comprehensive sales report') and includes key scope details. There's no wasted verbiage, though it could be slightly more structured for 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 the tool's moderate complexity (2 required parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on output format, behavioral traits, and usage context, which are important for a reporting tool with no structured metadata to compensate.
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 both parameters clearly documented in the input schema. The description adds context by mentioning 'date range', which aligns with the schema's startDate and endDate parameters, but doesn't provide additional semantic details beyond what the schema already specifies.
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 ('Generate') and resource ('comprehensive sales report'), including scope details like 'date range with trends, comparisons, and insights'. It distinguishes from obvious siblings like 'get_daily_sales' by emphasizing comprehensiveness, though it doesn't explicitly name alternatives.
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_daily_sales' or 'generate_eod_report'. It mentions a date range but doesn't specify use cases, prerequisites, or exclusions, leaving the agent to infer usage from context alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It implies a read-only operation ('Get'), but does not specify if it requires authentication, has rate limits, returns paginated results, or what happens on errors. For a tool with no annotation coverage, this is a significant gap in transparency about its behavior and constraints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the main purpose and includes relevant details without unnecessary words. It avoids redundancy and is appropriately sized for the tool's complexity, though it could be slightly more structured by separating usage guidelines.
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 lack of annotations and output schema, the description is incomplete. It does not explain the return format, error handling, or behavioral traits like data freshness or access permissions. For a tool that retrieves detailed information, more context is needed to guide the agent effectively, making it inadequate for full understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the parameter 'supplierName' documented as 'Supplier name or ID'. The description does not add any additional meaning beyond this, such as format examples or validation rules. With high schema coverage, the baseline score of 3 is appropriate as the schema handles the parameter documentation adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get detailed supplier information') and specifies the resource ('supplier'), along with examples of what information is included ('contact details, lead times, and minimum order requirements'). However, it does not explicitly differentiate this tool from sibling tools like 'compare_supplier_prices' or 'lookup_customer', which might also involve supplier data, so it lacks sibling differentiation.
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. For example, it does not mention when to prefer this over 'compare_supplier_prices' or if it's for retrieving basic vs. detailed supplier info. There is no context on prerequisites, exclusions, or typical use cases, leaving the agent with no usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'Saves hours of planning time' as a benefit but does not describe what the tool actually does (e.g., generates a schedule, saves it to a database, outputs a plan) or any behavioral traits like permissions, side effects, or output format. This leaves the agent with insufficient information to understand the tool's behavior beyond the basic purpose.
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 and front-loaded, with two sentences that directly state the purpose and benefit. There is no wasted language or redundancy. However, it could be slightly improved by integrating the benefit more seamlessly, but it remains efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a scheduling tool with no annotations and no output schema, the description is incomplete. It lacks details on what the tool outputs (e.g., a schedule object, a file, a confirmation), how it interacts with other systems, or any behavioral context. This makes it inadequate for an agent to fully understand the tool's operation and results.
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, so the schema already documents all parameters ('focusProducts', 'postsPerDay', 'startDate'). The description does not add any meaning beyond what the schema provides (e.g., it does not explain how 'varied content types' relate to parameters). With high schema coverage, the baseline score of 3 is appropriate, as the description adds no extra parameter semantics.
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: 'Create a weekly posting schedule with varied content types.' It specifies the verb ('Create') and resource ('weekly posting schedule'), and mentions a benefit ('Saves hours of planning time'). However, it does not explicitly differentiate from sibling tools like 'generate_post_ideas' or 'post_to_social_media', which reduces the score from a perfect 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. It mentions a benefit ('Saves hours of planning time') but does not specify contexts, prerequisites, or exclusions. With many sibling tools related to social media and content (e.g., 'generate_post_ideas', 'post_to_social_media'), the lack of differentiation 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 the full burden of behavioral disclosure. It states the tool returns 'detailed product information', which gives some output context, but lacks details on permissions, rate limits, pagination, or error handling. This is inadequate for a search tool with zero annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core functionality ('Search for products') and includes key details without waste. Every part earns its place by specifying search criteria and return information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete. It lacks behavioral details like authentication needs or result format, and doesn't compensate for the absence of structured fields. For a search tool with two parameters, this leaves significant gaps in understanding how to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters fully. The description adds minimal value by listing search criteria ('name, category, or supplier'), which partially aligns with the 'filterBy' parameter, but doesn't provide additional syntax or format details beyond the schema.
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 ('Search for') and resource ('products'), and specifies search criteria ('by name, category, or supplier'). It distinguishes from siblings like 'check_inventory' or 'get_best_sellers' by focusing on search functionality, though it doesn't explicitly differentiate them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like 'check_inventory' or 'get_best_sellers'. The description mentions search criteria but doesn't specify contexts, prerequisites, or exclusions for usage.
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 states the tool suggests bundles but doesn't describe how suggestions are generated (e.g., algorithm details, data sources), whether it's read-only or has side effects, performance characteristics, or error handling. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose without unnecessary details. It uses clear language ('suggest product bundles') and specifies the basis ('frequently bought together items or complementary products'), making it easy to parse. 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.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a suggestion tool with no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits (e.g., how suggestions are ranked), output format (e.g., list of bundles with prices), and error cases (e.g., invalid SKU handling). For a tool that likely returns structured data, more context is needed to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the parameter 'baseSku' documented as 'Base product SKU to build bundle around.' The description adds no additional parameter semantics beyond this, such as SKU format examples or constraints. Since schema coverage is high, 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: 'Suggest product bundles based on frequently bought together items or complementary products.' It specifies the verb ('suggest') and resource ('product bundles'), and distinguishes itself from siblings like 'get_customer_recommendations' or 'search_products' by focusing on bundle creation rather than general recommendations or product search. However, it doesn't explicitly differentiate from potential overlapping tools like 'calculate_discount' for bundle pricing.
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 prerequisites (e.g., needing a valid SKU), exclusions (e.g., not for single products), or comparisons to siblings like 'get_customer_recommendations' for individual product suggestions. The context is implied (bundle creation around a base product), but explicit usage instructions are absent.
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 mentions 'AI-powered' and tone maintenance ('friendly, professional'), but fails to detail critical aspects: whether it's read-only or has side effects, rate limits, authentication needs, output format, or error handling. For a tool with potential AI integration and no annotations, 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 concise and front-loaded, with two sentences that directly state the tool's function and key behavioral trait (tone). There's no wasted text, but it could be slightly more structured (e.g., separating purpose from guidelines). Overall, it's efficient and easy to parse.
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 (AI-powered, customer interaction) and lack of annotations and output schema, the description is insufficient. It doesn't explain return values, error cases, or operational constraints, leaving the agent with incomplete context for reliable invocation. More detail is needed to compensate for the missing structured data.
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 ('commentText' and 'tone') with descriptions and defaults. The description adds minimal value beyond the schema, only implying tone usage without elaborating on parameter interactions or constraints. Baseline 3 is appropriate as the schema handles most of the parameter documentation.
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: 'Get AI-powered reply suggestions for customer comments.' It specifies the verb ('Get'), resource ('reply suggestions'), and target ('customer comments'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'auto_respond_common_questions' or 'get_new_comments', which might handle similar content.
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 minimal guidance: it implies usage for replying to customer comments and mentions tone preferences. However, it lacks explicit when-to-use rules, prerequisites (e.g., comment context), or alternatives among siblings. No exclusions or comparisons are stated, leaving the agent to infer usage scenarios.
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 states the tool tracks activity but doesn't reveal key behaviors: whether it's a read-only operation, how data is retrieved (e.g., real-time vs. historical), rate limits, authentication needs, or what the output looks like. For a tool with no annotation coverage, this is inadequate.
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 ('Track competitor social media activity') followed by the benefit. There is no wasted verbiage or redundancy, making it highly concise and well-structured.
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 (tracking social media activity with 2 parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects, output format, or usage guidelines, leaving significant gaps for an AI agent to understand how to invoke it correctly.
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 ('competitors' and 'metrics') with descriptions. The tool description adds no additional parameter semantics beyond what's in the schema, such as format examples or usage context. Baseline 3 is appropriate when 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: 'Track competitor social media activity' with the goal 'to stay informed about market trends and pricing.' It specifies the resource (competitor social media) and outcome, though it doesn't explicitly differentiate from sibling tools like 'get_social_analytics' or 'analyze_post_performance' which might have overlapping domains.
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 the general purpose but doesn't specify scenarios, prerequisites, or exclusions. With sibling tools like 'get_social_analytics' and 'analyze_post_performance' present, this lack of differentiation 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool forecasts based on historical data and seasonal trends, which gives some context, but lacks details on permissions, rate limits, output format, or whether it's a read-only operation. For a tool with no annotations, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose without unnecessary details. Every word contributes to understanding the tool's function, making it appropriately sized and well-structured.
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 (forecasting with 2 parameters), no annotations, and no output schema, the description is minimally adequate. It covers the basic purpose but lacks behavioral context, usage guidelines, and output details, leaving gaps that could hinder an AI agent's effective 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%, so the schema already documents both parameters ('period' and 'sku') with descriptions. The description adds no additional parameter semantics beyond what's in the schema, such as explaining how 'period' affects the forecast or what 'sku' entails. Baseline 3 is appropriate when the schema handles parameter documentation.
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 as 'Forecast product demand based on historical sales data and seasonal trends,' which includes a specific verb ('forecast'), resource ('product demand'), and methodology. However, it doesn't explicitly differentiate from sibling tools like 'get_daily_sales' or 'get_sales_report,' which might also involve sales data analysis, though the forecasting focus is distinct.
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 prerequisites (e.g., needing historical data), exclusions (e.g., not for real-time updates), or compare to siblings like 'get_sales_report' for past data versus this for future predictions, leaving usage context implied at best.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool 'generates' ideas, implying a read-only or creative operation, but fails to detail behavioral traits such as whether it uses AI, has rate limits, requires specific permissions, or what the output format looks like (e.g., list of ideas). This leaves significant gaps in understanding how the tool behaves in practice.
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 and front-loaded, with two sentences that efficiently state the purpose and usage context. Every sentence earns its place by adding value, but it could be slightly more structured (e.g., by explicitly listing key parameters). Overall, it's appropriately sized without waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a generative tool with no annotations and no output schema, the description is incomplete. It lacks details on behavioral aspects (e.g., how ideas are generated, output format), and while the schema covers parameters, the description doesn't add context for usage or results. This leaves the agent with insufficient information to fully understand the tool's operation.
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%, meaning the input schema already documents all parameters (count, products, theme) with descriptions. The description adds no additional meaning beyond what's in the schema, such as explaining how products and themes interact or providing examples. 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: 'Generate creative post ideas based on products, themes, or seasons.' It specifies the verb ('generate') and resources ('post ideas'), and distinguishes it from siblings like 'generate_hashtags' or 'get_instagram_story_ideas' by focusing on general post ideas rather than specific content types. However, it doesn't explicitly differentiate from all siblings, keeping it from a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context with 'Perfect for content planning,' suggesting it's for planning phases, but lacks explicit guidance on when to use this tool versus alternatives like 'generate_hashtags' or 'schedule_weekly_posts.' No exclusions or specific alternatives are named, leaving usage somewhat vague beyond the implied planning scenario.
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 implies a read-only operation by using 'Get', but doesn't address critical aspects like whether it requires authentication, has rate limits, returns paginated results, or what happens on errors. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core functionality and includes key details without any wasted words. It's appropriately sized for the tool's complexity, making it easy to parse and understand quickly.
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 (2 parameters, no output schema, no annotations), the description is adequate but incomplete. It covers the basic purpose and parameters but lacks behavioral details and usage guidelines. Without annotations or output schema, more context on return values or operational constraints would improve completeness 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?
The description adds some meaning by mentioning 'by category or overall', which clarifies the optional 'category' parameter, and implies time-based ranking through 'top-selling'. However, with 100% schema description coverage, the schema already documents both parameters well, so the description only provides marginal additional context without detailing syntax or format beyond what's in the schema.
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 ('Get') and resource ('top-selling products'), and it specifies what data is returned ('quantity sold and revenue generated'). However, it doesn't explicitly differentiate from sibling tools like 'get_daily_sales' or 'get_sales_report', which might also involve sales data, leaving some ambiguity about its unique role.
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 filtering by category or overall, but doesn't specify scenarios, prerequisites, or exclusions, such as compared to 'get_sales_report' or 'get_top_customers'. This lack of context makes it harder for an AI agent to choose appropriately among similar tools.
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 implies a read-only operation ('get') but doesn't specify permissions, rate limits, or response format. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior beyond basic functionality.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the key information ('get sales summary') and includes essential details without any waste. It's appropriately sized for the tool's complexity 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 (1 parameter, no output schema, no annotations), the description is adequate but incomplete. It covers the basic purpose and output structure but lacks behavioral context, usage guidelines, and differentiation from siblings, which are important for effective tool selection.
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 schema description coverage is 100%, with the single parameter 'date' well-documented in the schema. The description adds no additional parameter semantics beyond what the schema provides, such as format details or constraints, so it meets the baseline score for high schema coverage.
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 specific verbs ('get sales summary') and resources ('revenue, transaction count, and top-selling items'), making it easy to understand what it does. However, it doesn't explicitly differentiate from sibling tools like 'get_sales_report' or 'get_best_sellers', 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.
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_sales_report' or 'get_best_sellers', nor does it mention any prerequisites or exclusions. It only describes what the tool does, not when it's appropriate to use it.
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 the tool 'gets' ideas (implying read-only), but doesn't disclose behavioral traits like whether it generates new content, uses AI, has rate limits, requires authentication, or returns structured data. For a tool with no annotation coverage, this leaves significant gaps in understanding its 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 a single, efficient sentence that front-loads the core purpose. Every word earns its place: 'Get creative Instagram Story ideas' establishes the action, and 'featuring products, behind-the-scenes, or promotions' adds useful context without redundancy. No wasted words or unnecessary elaboration.
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 minimally adequate. It covers what the tool does but lacks details on behavior, output format, or integration context. For a simple idea-generation tool, it's functional but leaves questions about result structure and limitations unanswered.
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%, providing clear documentation for both parameters. The description adds no parameter-specific information beyond what's in the schema (e.g., it doesn't explain how 'occasion' influences ideas or format of 'products'). With high schema coverage, the baseline score of 3 is appropriate as the description doesn't enhance parameter understanding.
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: 'Get creative Instagram Story ideas' with specific content types (products, behind-the-scenes, promotions). It distinguishes from siblings like 'generate_post_ideas' by specifying 'Instagram Story' format, though it doesn't explicitly contrast with that sibling. The verb 'Get' and resource 'Instagram Story ideas' are specific.
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 when-not scenarios, prerequisites, or compare to similar tools like 'generate_post_ideas' or 'generate_hashtags'. The context is implied (need Instagram Story ideas) but lacks explicit usage boundaries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. It mentions 'calculate' and 'financial reporting', implying a read-only operation for reporting purposes, but doesn't clarify permissions needed, whether it accesses live or cached data, rate limits, or what the output format looks like. For a tool with no annotations, 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 a single, efficient sentence that front-loads the core purpose. Every word contributes meaning without redundancy, making it easy to parse quickly. There's no wasted text or unnecessary elaboration.
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 is moderately complete. It covers the purpose and hints at behavior but lacks details on output format, error handling, or integration with other tools. For a calculation tool with two parameters, it's adequate but leaves room for improvement in guiding an agent fully.
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 description adds some meaning beyond the input schema: it explains that 'valueType' determines calculation at 'cost or retail price', which aligns with the schema's description. However, with 100% schema description coverage, the schema already documents both parameters well. The description doesn't provide additional context like examples or constraints, so it 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.
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: 'Calculate total inventory value at cost or retail price for financial reporting.' It specifies the verb (calculate), resource (total inventory value), and context (financial reporting). However, it doesn't explicitly differentiate from sibling tools like 'check_inventory' or 'get_low_stock_items', which might also involve inventory data.
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 'financial reporting' as context but doesn't specify prerequisites, exclusions, or compare it to similar tools like 'check_inventory' or 'calculate_profit_margin'. Without this, an agent might struggle to choose appropriately among inventory-related tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions what data is retrieved (reach, engagement, top posts) but doesn't cover critical aspects like authentication requirements, rate limits, data freshness, error handling, or output format. For a data retrieval tool with zero annotation coverage, this leaves significant gaps in understanding how it behaves.
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 with no wasted words. It directly states what the tool does and includes key details (platforms and metrics) without unnecessary elaboration.
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 is moderately complete for a simple retrieval tool. It covers the what (analytics) and scope (platforms/metrics), but lacks behavioral context and output details. For a tool with 2 parameters and 100% schema coverage, it's adequate but could better address missing annotation information.
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 both parameters ('period' and 'platform') well-documented in the schema. The description adds no additional parameter semantics beyond implying analytics are for Facebook/Instagram, which the schema already specifies. This meets the baseline of 3 since the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get performance analytics from Facebook and Instagram including reach, engagement, and top posts.' It specifies the action (get), resource (performance analytics), and scope (Facebook/Instagram with specific metrics). However, it doesn't explicitly differentiate from sibling tools like 'get_sales_report' or 'track_competitor_activity' that might also involve analytics.
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 prerequisites, compare to siblings like 'analyze_post_performance' or 'get_instagram_story_ideas', or specify scenarios where this tool is preferred. The agent must infer usage from the purpose alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It indicates a write operation ('update', 'records the change') but fails to detail critical aspects such as required permissions, whether changes are reversible, potential side effects (e.g., triggering alerts), or response format. For a mutation tool with zero annotation coverage, this is a significant gap in transparency.
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 sized with two sentences that are front-loaded with the core purpose. Every sentence earns its place by clarifying the action and context, though it could be slightly more structured (e.g., explicitly listing key behaviors). No redundant or verbose elements are present.
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 as a mutation operation with no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits (e.g., error handling, idempotency), output expectations, and comprehensive usage guidelines. For a tool that modifies inventory data, this leaves significant gaps for an AI agent to operate effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all three parameters (sku, quantity, reason) with clear descriptions. The description adds minimal value beyond the schema by implying the 'reason' parameter's purpose through examples ('received, sold, damaged, count'), but does not provide additional syntax, constraints, or contextual meaning. Baseline 3 is appropriate when 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 specific verbs ('update inventory quantity') and resources ('inventory', 'system'), and distinguishes it from siblings like 'check_inventory' or 'get_low_stock_items' by focusing on modification rather than retrieval. However, it doesn't explicitly differentiate from potential write operations like 'create_purchase_order' or 'update_loyalty_points' beyond the inventory context.
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 provides implied usage guidelines by mentioning 'after receiving shipment or doing physical count', which suggests contexts for application. However, it lacks explicit when-to-use vs. alternatives (e.g., compared to 'check_inventory' for read-only needs or other inventory tools), and does not specify prerequisites or exclusions, leaving some ambiguity for the agent.
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 mentions what the tool does but lacks details on how it behaves: no information on whether it performs calculations only (read-only) or modifies data, what the output format might be, error handling, or any constraints like rate limits or authentication requirements. This is a significant gap for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part earns its place by specifying the action and key contexts, making it easy to scan and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (4 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain the return values, error conditions, or behavioral traits, leaving gaps that could hinder an AI agent's ability to use the tool correctly. For a calculation tool with multiple inputs, more context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters. The description adds minimal value beyond the schema by hinting at the discount types (promotions, bulk, loyalty) which loosely maps to 'discountType', but doesn't provide additional semantics or clarify relationships between parameters (e.g., how 'quantity' interacts with 'discountType'). Baseline 3 is appropriate when 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 as calculating discounted prices for specific scenarios (promotions, bulk orders, loyalty rewards), using a specific verb ('calculate') and resource ('discounted price'). It doesn't explicitly distinguish from sibling tools like 'calculate_labor_cost' or 'calculate_profit_margin', but the focus on discount calculation is sufficiently distinct.
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 contexts (promotions, bulk orders, loyalty rewards) but doesn't provide explicit guidance on when to use this tool versus alternatives like 'suggest_bundle' or 'compare_supplier_prices'. No exclusions or prerequisites are mentioned, 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.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions retrieving comments for 'timely responses', hinting at a read-only operation, but lacks details on permissions, rate limits, data format, pagination, or error handling, which are critical for a social media tool.
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 action and purpose without any wasted words, making it easy to parse and understand 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 complexity of social media data retrieval, no annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects like authentication needs, response format, or error cases, leaving gaps for an AI agent to operate effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, clearly documenting both parameters. The description adds no additional semantic details beyond what the schema provides, such as examples or constraints, so it meets the baseline for high schema coverage without compensating value.
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 'retrieve' and the resource 'new comments from Facebook and Instagram posts', making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'suggest_comment_reply' or 'get_social_analytics', which might involve similar data, so it doesn't reach the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for 'timely responses', suggesting a context of customer engagement or monitoring, but it doesn't provide explicit guidance on when to use this tool versus alternatives like 'suggest_comment_reply' or 'get_social_analytics', nor does it specify exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool is 'useful for VIP outreach and marketing,' which hints at a read-only, analytical purpose, but doesn't explicitly state whether it's safe (non-destructive), requires specific permissions, has rate limits, or describes the return format. This leaves significant gaps for an AI agent to understand behavioral traits.
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 and front-loaded, with two sentences that efficiently convey purpose and usage. The first sentence states the core functionality, and the second adds context. There's no wasted text, though it could be slightly more structured by explicitly separating purpose from guidelines.
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 (2 parameters, no output schema, no annotations), the description is somewhat complete but has gaps. It covers the purpose and basic usage but lacks details on behavioral aspects like safety, permissions, or output format. Without annotations or an output schema, the description should do more to compensate, especially for a tool that likely returns customer data.
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 schema description coverage is 100%, with clear descriptions for both parameters ('limit' and 'sortBy'). The description adds minimal value beyond the schema by implying the sorting criteria ('by total spending or loyalty points'), which aligns with the schema's 'sortBy' description. However, it doesn't provide additional context like default behavior beyond the schema's 'default: 10' for 'limit', so it 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.
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: 'Get list of top customers by total spending or loyalty points.' It specifies the verb ('Get'), resource ('top customers'), and criteria ('by total spending or loyalty points'). However, it doesn't explicitly distinguish this tool from sibling tools like 'lookup_customer' or 'get_customer_recommendations', which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides implied usage guidance: 'Useful for VIP outreach and marketing.' This suggests a context for when to use the tool, but it doesn't explicitly state when not to use it or mention alternatives among the sibling tools. For example, it doesn't clarify if this is for aggregated analysis versus individual customer lookups.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the return values ('stock quantity, reorder status, and supplier information'), which is helpful, but lacks details on permissions, rate limits, error handling, or whether this is a read-only operation. For a tool with zero annotation coverage, this is a significant gap in transparency.
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 efficiently structured in two sentences: the first states the purpose, and the second specifies the return values. Every sentence adds value with no wasted words, making it front-loaded and easy to parse for an AI agent.
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 (1 parameter, no output schema, no annotations), the description is partially complete. It covers the purpose and return values, but lacks behavioral details like permissions or error handling. Without annotations or an output schema, more context on how the tool behaves would improve completeness for safe invocation.
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 the parameter 'search' documented as 'Product name, SKU, or category to search for.' The description adds minimal value beyond this, only implying the parameter's purpose without providing additional syntax or format details. With high schema coverage, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Check current inventory levels for a specific product or category.' It specifies the verb ('Check') and resource ('inventory levels'), and distinguishes it from siblings like 'get_inventory_value' or 'get_low_stock_items' by focusing on specific product/category lookup. However, it doesn't explicitly differentiate from 'search_products' or 'update_stock', which slightly limits sibling distinction.
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 by specifying 'for a specific product or category,' suggesting when to use this tool. However, it provides no explicit guidance on when to use alternatives like 'get_low_stock_items' for alerts or 'search_products' for broader searches, and lacks prerequisites or exclusions. This leaves 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.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool 'returns' data, implying a read-only operation, but doesn't specify whether it requires authentication, has rate limits, or how it handles missing customers. For a lookup tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise—two sentences that efficiently convey the tool's purpose and return values without any wasted words. It's front-loaded with the core functionality, making it easy to grasp quickly.
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 (single parameter lookup), no annotations, and no output schema, the description is minimally adequate. It covers what the tool does and what it returns, but lacks details on error handling, authentication, or output structure. This leaves room for improvement in providing a complete operational picture.
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 the parameter 'identifier' clearly documented as 'Customer name, email, or phone number'. The description adds no additional semantic details beyond this, such as formatting examples or validation rules. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't enhance parameter understanding.
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: 'Find customer information' with specific criteria (by name, email, or phone) and mentions what information is returned (purchase history, loyalty points, preferences). It distinguishes itself from sibling tools like 'get_top_customers' or 'get_customer_recommendations' by focusing on individual lookup rather than aggregated data, though it doesn't explicitly name these alternatives.
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 when you need to retrieve a specific customer's details using an identifier, but it doesn't provide explicit guidance on when to use this tool versus alternatives like 'get_top_customers' or 'update_loyalty_points'. 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.
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. It mentions the tool 'Automatically suggests items below reorder level,' which adds some behavioral context, but fails to disclose critical details like required permissions, whether the purchase order is finalized or draft, error handling, or response format. For a mutation tool 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.
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 and key feature. There is no wasted verbiage, making it easy to parse and understand quickly.
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 (a mutation with 3 parameters) and lack of annotations and output schema, the description is minimally adequate. It covers the purpose but lacks details on behavioral traits, error cases, and return values, which are important for a creation tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all three parameters thoroughly. The description does not add any meaning beyond what the schema provides, such as explaining interactions between parameters or providing examples. Baseline 3 is appropriate when the schema does the heavy lifting.
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 ('Create a purchase order') and resource ('for restocking'), with the added functionality of 'Automatically suggests items below reorder level.' It distinguishes from siblings like 'check_inventory' or 'get_low_stock_items' by focusing on creation rather than retrieval.
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 restocking scenarios, but does not explicitly state when to use this tool versus alternatives like 'compare_supplier_prices' or 'get_supplier_info'. 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.
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. It mentions the store policy (1 point per $1 spent, 100 points = $10 off), which adds useful context about point valuation, but it does not disclose critical behavioral traits such as permission requirements, whether changes are reversible, rate limits, or what happens on redemption failure. For a mutation tool 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.
Conciseness5/5
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 adds policy details in the second. Both sentences earn their place by providing essential context without redundancy, making it appropriately sized and well-structured for quick understanding.
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 (a mutation with 3 parameters), no annotations, and no output schema, the description is moderately complete. It covers the purpose and policy but lacks details on behavioral aspects like error handling or return values. It compensates somewhat with parameter clarification but falls short of being fully comprehensive for safe 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%, so the schema already documents all three parameters (customerId, points, reason) adequately. The description adds some value by explaining that positive points mean 'add' and negative points mean 'redeem', which clarifies the 'points' parameter semantics beyond the schema's basic description. However, it does not provide additional details on parameter formats or constraints, meeting the baseline for high schema coverage.
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 ('Add or redeem loyalty points') and the target resource ('for a customer'), distinguishing it from all sibling tools which handle different business functions like inventory, sales, or social media. It provides concrete details about the store policy, making the purpose unambiguous and 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 through the store policy context (e.g., points tied to spending), but it does not explicitly state when to use this tool versus alternatives like 'calculate_discount' or 'lookup_customer'. No exclusions or prerequisites are mentioned, leaving some ambiguity about appropriate scenarios.
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 valuable context beyond the schema by mentioning authentication requirements ('Requires Meta API configuration') and scheduling capability ('Can schedule posts for future publishing'), but does not disclose other important traits like rate limits, error handling, whether posts are editable after creation, or what happens if scheduling fails. This is adequate but has clear gaps for a mutation tool.
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 sentence earns its place: the first states the action and platforms, the second adds crucial behavioral context (authentication and scheduling). There is zero waste or redundancy.
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 this is a mutation tool with no annotations and no output schema, the description is moderately complete. It covers the purpose, prerequisites, and scheduling behavior, but lacks details on return values, error conditions, or platform-specific constraints (e.g., Instagram requiring images). For a 5-parameter tool that creates social media posts, more context would be beneficial.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all 5 parameters thoroughly. The description does not add any parameter-specific semantics beyond what the schema provides (e.g., it doesn't explain format details for 'platforms' or constraints for 'scheduleTime'). The baseline score of 3 is appropriate when the schema does the heavy lifting.
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 ('Post content') and target resources ('Facebook and/or Instagram'), distinguishing it from sibling tools like 'schedule_weekly_posts' (which implies recurring scheduling) and 'generate_post_ideas' (which is idea generation rather than posting). The verb 'post' is precise and the platforms are explicitly named.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('Post content to Facebook and/or Instagram') and mentions prerequisites ('Requires Meta API configuration'), but does not explicitly state when not to use it or name specific alternatives among siblings (e.g., 'schedule_weekly_posts' for recurring posts). The guidance is helpful but lacks explicit exclusions.
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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