Partle Marketplace

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

Adds authentication requirement ('Requires an API key') and critical price constraints ('NOT cents', 'Max 100000', currency defaults) not present in annotations. Annotations already indicate mutable non-destructive operation, so description appropriately focuses on domain-specific validation rules rather than repeating safety hints.

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

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

Well-structured with purpose front-loaded, followed by Args section. Every sentence earns its place. Slightly unusual docstring format for MCP descriptions but information-dense with no redundancy.

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

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

Excellent coverage of input semantics for all 7 parameters including validation constraints. No output schema present, and description doesn't specify return value structure (e.g., created product ID), but input documentation is complete enough for invocation.

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

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

With 0% schema description coverage, the description comprehensively documents all 7 parameters in the Args block, including format hints (api_key starts with pk_), units (whole currency), optionality markers, and default values (currency defaults to €). Fully compensates for schema deficiency.

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

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

Clear specific verb 'Create' with resource 'product listing'. Distinct from siblings like update_product, delete_product, get_product through explicit creation semantics.

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

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

Provides prerequisite (API key requirement) and usage context (store_id omission creates personal listing). Lacks explicit comparison to siblings (e.g., when to use update_product instead) or 'when-not-to-use' exclusions.

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

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

Adds valuable cascading effect detail ('and all its images') not present in annotations, confirming the scope of destruction beyond the annotation's boolean flag.

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

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

Information is front-loaded, but the 'Args:' docstring format is slightly informal/awkward for an MCP tool description; could be more polished.

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

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

Adequate for a simple two-parameter destructive operation; covers the action and inputs sufficiently given no output schema exists to describe.

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

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

Compensates effectively for 0% schema description coverage by providing semantic meaning and format hints (pk_ prefix) for both required parameters.

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

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

Clear verb ('delete') and resource ('product listing'), and explicitly distinguishes from sibling delete_product_image by stating it removes 'all its images' as well.

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

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

No guidance on when to use versus alternatives (e.g., delete_product_image for single images, update_product for modifications) or warnings about permanence beyond the word itself.

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

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

Adds useful behavioral context beyond annotations: api_key format ('starts with pk_') and image_id location ('visible in product details').

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

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

Appropriately brief and front-loaded; 'Args:' section is functional though slightly informal.

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

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

Adequate for a simple 3-parameter destructive operation, though could briefly mention success/failure outcomes given no output schema exists.

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

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

Effectively compensates for 0% schema description coverage by defining all three parameters, including specific format hints and data location guidance.

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

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

Specific verb ('Delete') and resource ('image from a product') clearly distinguish it from siblings like delete_product or upload_product_image.

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

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

No explicit guidance on when to use versus alternatives, workflow prerequisites, or error handling.

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

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

Adds the 1-200 range constraint for limit not present in annotations, but lacks details on result ordering or rate limiting despite readOnlyHint.

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

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

Extremely tight structure with purpose front-loaded and Args section providing dense, relevant parameter details.

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

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

Sufficient for a simple read operation with output schema available, though could mention pagination cursor handling if applicable.

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

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

Effectively compensates for 0% schema coverage by providing format hint (pk_) for api_key and valid range/default for limit.

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

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

Clearly states the listing action and scope (API key owner's products), implicitly distinguishing from get_product (singular) and search_products (filtered).

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

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

No guidance on when to use this versus search_products or get_product, nor pagination behavior for large result sets.

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

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

Annotations declare readOnlyHint=true covering safety; description adds that information is 'detailed', providing scope context, but lacks error behaviors, rate limits, or data sensitivity details.

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

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

Front-loaded purpose statement is efficient, but the Args section is redundant (repeats schema info without adding meaning) and could be removed or enriched.

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

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

Adequate for a simple single-parameter read operation with safety annotations, though lacks output format details and parameter value semantics given zero schema coverage.

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

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

With 0% schema description coverage, the Args section provides only tautological description 'The product ID' which adds no semantic value beyond the parameter name and type.

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

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

Clear specific verb 'Get' with resource 'product' and method 'by its ID', clearly distinguishes from siblings like 'search_products' (finding multiple) and 'get_my_products' (plural/personal scope).

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

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

No explicit guidance on when to use this versus 'search_products' or 'get_my_products'; agent must infer based on the ID lookup mechanism described.

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

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

Annotations already declare readOnlyHint=true. The description adds value by specifying exactly which statistics are returned (product and store totals). However, it omits details about the return format (object structure vs raw numbers), caching behavior, or whether these are real-time counts.

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

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

Single sentence that efficiently combines the action, resource, and specific data points returned. Every clause earns its place with no redundancy or filler.

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

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

Appropriately complete for a zero-parameter, read-only tool. While it lacks an output schema, the parenthetical enumeration of the two statistics provides sufficient context for invocation, though the exact return structure (JSON keys, value types) remains unspecified.

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

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

Zero parameters per the empty input schema, which warrants the baseline score of 4. No parameter documentation is required or expected.

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

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

States specific resource ('platform statistics') and lists the returned metrics ('total products, total stores'). Implicitly distinguishes from sibling tools like get_product and get_store by describing aggregate platform-wide data rather than individual resource retrieval, though it doesn't explicitly contrast with them.

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

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

Provides no guidance on when to use this versus sibling retrieval tools like get_product, get_my_products, or search_products. Does not clarify that this returns aggregate counts versus detailed records.

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

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

Annotations declare readOnlyHint=true, and description adds 'detailed information' indicating richness of returned data. However, it lacks disclosure of what happens when store_id is not found (404 behavior) or authorization requirements beyond the read-only hint.

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

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

Front-loaded purpose statement followed by Args documentation. No wasted words, though the 'Args' format is slightly non-standard for MCP descriptions. Appropriate length for tool complexity.

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

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

Adequate for a simple retrieval tool with one required parameter. Covers the basic operation and parameter, but gaps remain regarding error handling and the relationship to search_stores. Given low complexity and presence of readOnly annotations, this is minimally complete.

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

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

Schema has 0% description coverage, requiring the description to compensate. The 'Args' block provides basic semantics 'The store ID' for store_id, which though tautological is necessary given the schema lacks descriptions. Minimal but sufficient given single parameter.

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

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

States specific verb 'Get' and resource 'detailed information about a single store'. The phrase 'single store' effectively distinguishes from sibling 'search_stores' and clarifies this retrieves one specific entity rather than a list.

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

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

Provides no guidance on when to use this tool versus siblings like 'search_stores' (when searching by criteria) or 'get_my_products' (different resource). No mention of prerequisites or error conditions like invalid IDs.

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

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

Annotations declare readOnlyHint=true and openWorldHint=true. Description adds valuable behavioral details including default values (limit: 20), valid ranges (1-100), currency unit (EUR), and comma-separated format for tags. However, it lacks guidance on result pagination, case sensitivity, or empty result handling.

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

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

Well-structured with purpose stated first followed by Args block. Front-loaded summary allows quick identification. Length is appropriate given zero schema coverage necessitates inline parameter documentation. No redundant filler text.

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

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

Complete for a search tool given the presence of output schema (not shown but indicated) and comprehensive parameter coverage. Annotations cover safety profile. Minor gap: could mention that results are paginated or indicate typical response size.

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

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

With 0% schema description coverage, the description fully compensates by documenting all 6 parameters with precise semantics: query examples (spanish/english), price currency, tag format syntax, sort_by enum values, and limit constraints. This is exemplary parameter documentation.

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

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

Clear verb (Search) and resource (marketplace products) with specific scope (by name or description). Effectively distinguishes from sibling get_product (single retrieval) and search_stores (different resource), 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.

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

Provides no guidance on when to use this versus get_product (direct lookup), get_my_products (user's own inventory), or search_stores. No mention of prerequisites or filtering strategies.

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

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

Annotations declare readOnlyHint=true and openWorldHint=true. Description adds value by specifying query filters by 'name or address' and documenting the limit range (1-50), providing behavioral constraints not in annotations.

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

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

Clear two-part structure: high-level purpose followed by Args section. Front-loaded with the core action. No redundant text, though 'Args:' format is slightly informal.

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

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

Appropriate for a simple 2-parameter search tool. Output schema exists (reducing need to describe returns) and annotations cover safety profile. Description adequately covers parameter semantics that schema lacks.

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

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

Schema has 0% description coverage (only titles). Description compensates effectively by documenting query filters 'by name or address' and limit's range constraint '(1-50, default 20)', adding crucial semantic context missing from the schema.

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

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

States specific actions ('Search or list') and resource ('stores in the marketplace'). Implicitly distinguishes from sibling 'get_store' (singular) by using plural 'stores', though it doesn't explicitly clarify when to use search vs get.

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

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

No guidance provided on when to use this vs 'get_store' or 'search_products', nor any mention of prerequisites or filtering strategies. Description only states what parameters exist, not when the tool is appropriate.

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

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

Adds the 5000 character constraint not present in schema or annotations, though could clarify what happens after submission (ticket created?).

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

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

Concise and front-loaded; 'Args:' format is slightly unconventional but every sentence provides value without redundancy.

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

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

Adequately covers the single-parameter, non-destructive operation without output schema; no gaps given the tool's simplicity.

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

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

With 0% schema description coverage, the description fully compensates by explaining the parameter's purpose (issue/suggestion) and constraint (max 5000 chars).

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

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

Clearly states it submits feedback about the Partle experience with specific examples (confusing, broken, better), distinguishing it clearly from product/store management siblings.

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

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

Provides clear examples of when to use (issues, suggestions) but lacks explicit guidance on when not to use or alternatives.

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

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

Clarifies PATCH-like partial update semantics beyond annotations, though could mention idempotency or error handling.

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

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

Well-structured with purpose front-loaded followed by Args block; no redundant text.

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

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

Fully covers all 7 parameters and key behavioral traits; no output schema exists to document.

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

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

Comprehensive Args block compensates for 0% schema coverage, adding critical semantics like price 'NOT cents', max 100000, and api_key format (pk_).

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

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

States specific action (update) and resource (existing product listing), distinguishing from create/delete siblings via 'existing' keyword.

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

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

Explains partial update behavior ('Only provided fields are changed'), but lacks explicit guidance on when to use vs create_product or delete_product.

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

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

Annotations declare destructive/idempotent hints, but description fails to explain what gets destroyed (overwrites existing main image? adds variant?) or how idempotency works. Adds valuable context about api_key format ('starts with pk_') and conditional requirement ('Required with image_base64') not in schema.

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

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

Information is front-loaded with the Args block providing necessary documentation given empty schema. However, the Args: format is structurally awkward for a description field and resembles auto-generated pydoc rather than curated prose.

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

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

Covers input parameters adequately and references the XOR logic, but fails to explain the destructive behavior flagged in annotations or describe expected return values/confirmation given no output schema exists.

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

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

With 0% schema coverage, the description fully compensates by documenting all 5 parameters with semantics: api_key format hint, product_id purpose, encoding types, and crucially the dependency constraint between content_type and image_base64.

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

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

Clear verb ('Upload') + resource ('image for a product'). States the core operation effectively. However, lacks differentiation from sibling 'delete_product_image' regarding whether this appends to a gallery or replaces a primary image.

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

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

Provides explicit XOR constraint ('Provide either image_base64 or image_url (not both)'), which is valuable usage guidance. However, lacks broader workflow context such as when to use base64 vs URL, prerequisite product existence checks, or relationship to 'create_product'.

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