MCP Inflow Ingredients

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Every tool has a clearly distinct purpose with no ambiguity. For example, create_ingredient, get_ingredient, update_ingredient, and list_ingredients each handle different CRUD operations for ingredients, while stock adjustment tools focus on inventory modifications. The separation between ingredient management and stock adjustments is well-defined, preventing misselection.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern using snake_case, such as create_ingredient, get_inventory_summary, and list_stock_adjustments. This uniformity makes the tools predictable and easy to understand, with no deviations in naming conventions across the set.
Tool Count5/5
With 9 tools, the count is well-scoped for inventory and ingredient management. Each tool serves a specific function, such as CRUD operations for ingredients and stock adjustments, without redundancy. This number is appropriate for the domain, providing comprehensive coverage without being overwhelming.
Completeness4/5
The tool set offers strong coverage for ingredient and inventory management, including create, read, update, list, and search operations, as well as stock adjustments. A minor gap exists in the lack of an update_stock_adjustment tool, which could limit flexibility in modifying adjustments, but agents can work around this by creating new adjustments.
Average 2.9/5 across 9 of 9 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only states the tool lists with filters, missing critical details like whether it's read-only (implied but not explicit), pagination behavior (only mentions a default limit), error handling, or authentication needs. This is inadequate for a tool with parameters and no output schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
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 ('list stock adjustments') and adds key detail ('with optional filters'). There's no wasted text, making it appropriately sized for a basic tool. However, it could be more structured by explicitly mentioning the resource scope or sibling differentiation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 3 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what 'stock adjustments' entail, how results are returned (e.g., list format, pagination beyond limit), or behavioral traits like safety or rate limits. For a listing tool with filtering, 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 100% description coverage, so parameters are documented in the schema (e.g., 'adjustmentNumber' filters by number, 'include' adds related entities, 'limit' sets max results). The description adds no additional meaning beyond stating 'optional filters', which is already implied by the schema. Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool lists stock adjustments with optional filters, which provides a basic purpose. However, it's vague about what 'stock adjustments' specifically are (e.g., inventory changes, corrections) and doesn't distinguish from siblings like 'get_stock_adjustment' (singular vs. plural). It uses a clear verb ('list') but lacks specificity in resource scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. It mentions optional filters but doesn't specify scenarios for using this over 'get_stock_adjustment' (for single adjustments) or 'search_ingredients' (for related searches). There's no mention of prerequisites, exclusions, or typical use cases, leaving the agent to infer 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 mentions creation but fails to detail critical aspects like required permissions, whether the operation is idempotent, error handling, or response format. This leaves significant 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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded and appropriately sized for its 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?
For a creation tool with no annotations and no output schema, the description is incomplete. It lacks information on behavioral traits (e.g., permissions, side effects), response format, and error handling, which are essential for effective tool use in this context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all parameters. The description does not add any semantic details beyond what the schema provides, such as explaining relationships between parameters or usage examples. 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 action ('Create') and resource ('new ingredient/product in Inflow inventory'), making the purpose unambiguous. However, it does not explicitly differentiate from sibling tools like 'update_ingredient' or 'list_ingredients', which would be needed for a score of 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives such as 'update_ingredient' or 'list_ingredients', nor does it mention prerequisites like authentication or context. It only states what the tool does, without 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states it 'modifies inventory quantities,' implying a write operation, but lacks details on permissions required, whether changes are reversible, rate limits, or what happens on success/failure. This is inadequate for a mutation 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 key action and resource. There's no redundancy or unnecessary detail, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with no annotations and no output schema, the description is insufficient. It doesn't explain what the tool returns (e.g., confirmation, error details), behavioral constraints, or how it fits with sibling tools, leaving significant gaps for an agent to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all parameters. The description adds no additional meaning beyond implying adjustments can be for 'one or more products' (hinting at the 'lines' array), but doesn't clarify parameter interactions or usage nuances. 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 ('create a stock adjustment') and the resource ('inventory quantities for one or more products'), making the purpose evident. However, it doesn't explicitly differentiate from sibling tools like 'list_stock_adjustments' or 'get_stock_adjustment', which would require mentioning it's for creating new adjustments rather than retrieving existing ones.
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 valid product IDs or location IDs), nor does it compare to sibling tools like 'update_ingredient' for inventory changes, leaving the agent without context for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 action ('Get detailed information') but doesn't cover critical aspects like authentication needs, rate limits, error handling, or what 'detailed information' entails. This leaves significant gaps for an agent to understand the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It's front-loaded with the core purpose and appropriately sized for a simple retrieval tool, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (a read operation with 2 parameters) and lack of annotations and output schema, the description is incomplete. It doesn't explain the return format, error conditions, or behavioral traits, leaving the agent with insufficient context to use the tool 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, documenting both parameters clearly. The description adds no additional meaning beyond the schema, such as explaining the format of 'detailed information' or examples for the 'include' parameter. Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
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 resource ('detailed information about a specific ingredient/product'), making the purpose understandable. However, it doesn't explicitly differentiate from siblings like 'list_ingredients' or 'search_ingredients', which might retrieve similar information in bulk or with filters.
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 'list_ingredients' or 'search_ingredients'. It lacks context on prerequisites, exclusions, or specific scenarios where this tool is preferred, 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states this is a 'Get' operation, implying read-only behavior, but doesn't clarify permissions, rate limits, error handling, or response format. The description adds minimal context beyond the basic action, leaving 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 a single, efficient sentence that front-loads the core purpose ('Get inventory summary for a product') and adds useful detail ('including quantities on hand, available, reserved, etc.'). There's no wasted text, making it appropriately sized for a simple tool, though it could be slightly more structured with usage hints.
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 for a tool that returns summary data. It mentions what data is included but doesn't describe the return structure, potential errors, or behavioral traits like idempotency. For a read operation with one parameter, it's minimally adequate but lacks depth needed for full agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the input schema fully documenting the 'productId' parameter as a UUID. The description adds no additional parameter semantics beyond what's in the schema (e.g., it doesn't explain format examples or constraints). Baseline 3 is appropriate since the schema handles the heavy lifting, but the description doesn't compensate or add 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 tool's purpose: 'Get inventory summary for a product' specifies the verb ('Get') and resource ('inventory summary'), with additional detail on what data is included ('quantities on hand, available, reserved, etc.'). It distinguishes itself from siblings like 'get_ingredient' or 'list_stock_adjustments' by focusing on summary data rather than individual records or lists, 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. It doesn't mention prerequisites (e.g., needing a valid product ID), exclusions, or compare it to siblings like 'search_ingredients' or 'list_stock_adjustments' for broader inventory queries. Usage is implied by the purpose but lacks explicit 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states this is a read operation ('Get'), implying it's likely safe and non-destructive, but doesn't confirm this or address other behaviors like error handling, authentication needs, rate limits, or response format. For a tool with zero annotation coverage, 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 with zero waste. It's front-loaded with the core purpose and appropriately sized for a simple retrieval tool. Every word earns its place, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (a read operation with 2 parameters) and lack of annotations and output schema, the description is incomplete. It doesn't explain what 'details' entail, how errors are handled, or any behavioral traits. For a tool with no structured output information, the description should provide more context 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 schema description coverage is 100%, so the schema already documents both parameters ('stockAdjustmentId' as required, 'include' as optional). The description adds no additional meaning beyond what the schema provides, such as explaining what 'details' might include or how the 'include' parameter affects output. 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 verb ('Get') and resource ('details of a specific stock adjustment'), making the purpose understandable. It distinguishes from sibling tools like 'list_stock_adjustments' by specifying retrieval of a single item rather than a collection. However, it doesn't explicitly mention what 'details' include, 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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a valid stock adjustment ID), exclusions, or comparisons to siblings like 'list_stock_adjustments' or 'get_inventory_summary'. This leaves the agent without context for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 it's a list operation with filters but doesn't mention whether it's read-only, paginated (beyond the 'limit' param), requires authentication, has rate limits, or what the output format looks like. This is inadequate for a tool with 7 parameters and no output schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
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 ('List all ingredients/products in Inflow inventory') and adds essential qualification ('with optional filters'). There's no wasted wording, 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 (7 parameters, no annotations, no output schema, and multiple sibling tools), the description is insufficient. It lacks behavioral context, usage differentiation, and output details, leaving significant gaps for an agent to understand how to properly invoke and interpret results from this 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 fully documents all 7 parameters. The description adds minimal value by mentioning 'optional filters', but doesn't provide additional context like filter combinations or the 'include' parameter's purpose beyond what's in the schema. 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 ('List all ingredients/products') and resource ('in Inflow inventory'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'search_ingredients' or 'get_ingredient', 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 mentions 'optional filters' but provides no guidance on when to use this tool versus alternatives like 'search_ingredients' or 'get_ingredient'. There's no indication of prerequisites, typical use cases, or exclusions, leaving the agent with minimal context for 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 'full-text search' but doesn't cover key aspects like whether the search is case-sensitive, how results are ranked, if there's pagination or rate limiting, or what the output format is. For a search 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 a single, efficient sentence that front-loads the core functionality ('Search for ingredients using full-text search') and specifies the search scope. There's no wasted language or redundancy, 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 as a search function with no annotations and no output schema, the description is incomplete. It doesn't explain the return values, result ordering, or error handling, which are critical for an agent to use the tool effectively. The description alone is insufficient for full contextual 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 description adds minimal value beyond the input schema, which has 100% coverage. It implies the 'query' parameter is used for full-text search across multiple fields, but doesn't provide additional details like search syntax or examples. With high schema coverage, the baseline is 3, as the schema already documents parameters adequately, and the description doesn't compensate with extra insights.
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: 'Search for ingredients using full-text search across name, description, SKU, and barcode.' It specifies the verb ('search'), resource ('ingredients'), and scope ('full-text search across name, description, SKU, and barcode'), which is specific and actionable. However, it doesn't explicitly distinguish from sibling tools like 'list_ingredients' or 'get_ingredient', which could have overlapping functionality, so it doesn't achieve 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 Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'list_ingredients' or 'get_ingredient', nor does it specify contexts where full-text search is preferred over other methods. This lack of comparative usage information leaves the agent without clear direction on 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 this is an update operation, implying mutation, but doesn't mention permissions required, whether changes are reversible, potential side effects (e.g., on inventory data), or rate limits. This is inadequate for a mutation 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 directly states the tool's purpose without unnecessary words. It's front-loaded with the core action and resource, 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 (6 parameters, mutation operation) and lack of annotations and output schema, the description is insufficient. It doesn't explain what happens on success or failure, what fields can be updated, or how it interacts with sibling tools. For a mutation tool in an inventory context, more behavioral and contextual details are 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?
The input schema has 100% description coverage, so parameters are well-documented in the schema itself. The description adds no specific parameter information beyond implying an update action, which the schema already covers through parameter names and 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 ('Update') and target resource ('existing ingredient/product in Inflow inventory'), making the purpose immediately understandable. However, it doesn't distinguish this tool from its sibling 'create_ingredient' beyond the update vs. create distinction, missing an opportunity to clarify the specific update context versus creation.
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 'create_ingredient' or 'get_ingredient'. It mentions updating an existing ingredient but doesn't specify prerequisites (e.g., needing a valid productId) or exclusions (e.g., not for creating new items), leaving usage context vague.
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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