Official MCP server for eData4You — 35 tools covering ecommerce outsourcing, Amazon/Shopify/Walmart listing management, catalog validation, SEO generation, marketplace news, case studies, glossary, and lead generation. No auth required for read tools.
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Average 3.5/5 across 35 of 35 tools scored. Lowest: 2.7/5.
Most tools have distinct purposes (e.g., blog_generator vs. get_blog_post), but there is overlap among some content validation tools (csv_cleaner, duplicate_sku_finder, inventory_checker, product_catalog_validator) which could cause confusion. Also, compare_services and search_services serve similar functions.
Tool names mix styles: some use noun_verb (amazon_listing_manager), others verb_noun (compare_services), and there are multiple prefixes like get_, search_, list_. This inconsistency may hinder an agent's ability to predict tool names.
With 35 tools, the set is large and covers many subdomains. This could overwhelm an agent, making selection challenging. A more focused subset would improve coherence.
The tools cover a broad range of ecommerce operations: listing management for multiple platforms, content generation, data validation, research, and reporting. However, missing update/delete capabilities and some platform interactions limit full lifecycle coverage.
amazon_listing_managerBCreate an Amazon listing plan with title, bullets, backend keywords, image checklist, and compliance checks.
| Name | Required | Description | Default |
|---|---|---|---|
| product | No | Product fields such as title, brand, sku, category, features, dimensions. | |
| keywords | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description says 'Create' implying a write operation, but annotations indicate readOnlyHint=true, contradicting the description. Additionally, no behavioral details beyond the annotations (e.g., no mention of what happens to the plan, if it's saved). Annotation contradiction flag triggered.
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, front-loaded with the core action and key elements. No redundant words, efficient and clear.
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?
With no output schema, the description should explain return values or outcomes. The tool creates a 'plan', but there's no info on what is returned or how the plan is used. Also, the read-only annotation contradicts the 'Create' verb, creating confusion.
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 covers parameters with descriptions, but the 'product' object's nested fields lack structure. The description adds value by listing example fields (title, brand, sku, category, features, dimensions), compensating for the schema's lack of detail on nested properties.
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?
Description clearly states the verb 'Create', the resource 'Amazon listing plan', and lists key components (title, bullets, keywords, checklist, compliance). This distinguishes it from siblings like 'walmart_listing_manager' and 'shopify_product_manager'.
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 tool versus alternatives. No mention of prerequisites, contexts, or exclusions. The description is purely declarative.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
blog_generatorAGenerate a blog brief with SEO title, meta description, outline, intro, FAQs, and CTA direction.
| Name | Required | Description | Default |
|---|---|---|---|
| tone | No | Optional tone. | |
| topic | Yes | Blog topic. | |
| audience | No | Target reader. | |
| keywords | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true; the description adds no further behavioral context beyond the generation task, which is consistent.
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 of 16 words, no fluff, clearly states what the tool does.
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?
Provides the output components but omits return format or prerequisites; adequate for a simple tool but could be more complete given no output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers 75% of parameters with descriptions; the tool description does not add meaning beyond listing output components, so baseline 3 is appropriate.
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?
Description specifies 'Generate a blog brief' with clear components (SEO title, meta description, outline, etc.), distinguishing it from siblings like list_blog_posts or seo_generator.
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 tool versus alternatives; usage is implied but not stated, e.g., no mention of when to generate a brief versus retrieving a full post.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
check_site_healthACheck MCP server health, content counts, authorization mode, and uptime.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds what specific health aspects are checked, which is useful context, but does not disclose potential side effects or rate limits.
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 efficiently communicates the tool's purpose with no unnecessary words. Front-loaded with the action verb 'Check'.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter tool without output schema, the description covers the main inputs and outputs (health, counts, auth mode, uptime). It could mention what 'content counts' refers to, but overall adequate.
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?
No parameters exist, so schema coverage is 100%. According to guidelines, zero parameters baseline is 4. The description adds no parameter info, but none is needed.
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?
Description clearly states the tool checks MCP server health, content counts, authorization mode, and uptime. This is specific and distinguishes it from sibling tools like pricing_calculator or blog_generator.
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 tool versus alternatives. The description implies its use for server health checks but does not specify when not to use it or mention any alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
client_reportingBGenerate a client reporting summary from metrics, wins, blockers, tasks completed, and next actions.
| Name | Required | Description | Default |
|---|---|---|---|
| wins | No | ||
| client | No | Client or account name. | |
| period | No | Reporting period. | |
| metrics | No | Key-value metrics. | |
| blockers | No | ||
| nextActions | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnly, idempotent, and non-destructive. The description adds that it 'generates a summary', implying no side effects. However, it does not elaborate on output behavior or system interactions beyond what annotations cover.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single 13-word sentence that is front-loaded and contains no filler. Every word adds value, and the description is optimally concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 6 parameters, nested objects, and no output schema, the description is minimal. It conveys the tool's purpose but lacks details about the generated summary's structure or content. For a generation tool, more context would help.
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 coverage is 50%, with descriptions for client, period, and metrics. The description reiterates 'metrics, wins, blockers, tasks completed, and next actions', partially mapping to parameters. It adds no syntax or format details beyond the schema, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates a client reporting summary and lists the input components. It is specific enough to distinguish from sibling tools like blog_generator or proposal_generator, as it focuses on client reporting.
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 is provided on when to use this tool versus alternatives, nor are there any prerequisites or exclusions mentioned. The description is purely functional with no usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
compare_servicesACompare two or more eData4You service pages by path, slug, or name.
| Name | Required | Description | Default |
|---|---|---|---|
| services | Yes | Service paths, slugs, or names to compare. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the description adds minimal behavioral context beyond the parameter method. It does not contradict annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that immediately communicates the tool's purpose. No extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is adequate for a simple tool with one parameter and annotations covering safety. However, it lacks information on the output format (e.g., comparison result structure) which could help the agent use the result correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for the one parameter. The description mostly repeats the schema's description of 'services' as paths, slugs, or names, adding no new semantic detail.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'compare', the resource 'eData4You service pages', and the method 'by path, slug, or name'. It distinguishes from sibling tools like search_services or get_service_page which perform different actions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for comparing service pages but does not provide explicit guidance on when to use it versus alternatives like get_service_page or search_services. No exclusions or conditions are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
create_project_briefBCreate a structured project brief from business requirements for handoff to the eData4You team.
| Name | Required | Description | Default |
|---|---|---|---|
| goals | No | ||
| scope | No | Scope, volume, timeline, and constraints. | |
| budget | No | Optional budget range. | |
| company | No | Company or brand name. | |
| timeline | No | Desired timeline. | |
| platforms | No | ||
| projectType | Yes | Project/service type. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description says 'Create', suggesting a write operation, but annotations declare readOnlyHint=true. This is a contradiction, severely misleading an AI agent about the tool's behavior. No additional behavioral context is provided.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with no redundancy. However, it is overly terse and could benefit from additional context without being verbose.
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?
The tool has 7 parameters and no output schema, yet the description gives no information about return values, the structure of the brief, or post-creation steps. With a contradiction in annotations, the completeness is inadequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 71% (5/7 params described). The description adds no parameter-level information beyond what's in the schema, failing to compensate for the 2 undocumented 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?
The description clearly states the verb 'Create' and the output 'structured project brief' from 'business requirements' for handoff to 'eData4You team'. It distinguishes itself from sibling tools like proposal_generator or generate_service_recommendation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use (when you have business requirements for handoff), but provides no explicit guidance on when not to use or alternatives among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
csv_cleanerBClean CSV-like row objects by trimming text, normalizing headers, removing empty rows, and reporting changed fields.
| Name | Required | Description | Default |
|---|---|---|---|
| rows | Yes | ||
| normalizeHeaders | No | Convert headers to snake_case. Default true. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, indicating safe, non-mutating behavior. The description adds useful behavioral context: it trims text, normalizes headers, removes empty rows, and reports changed fields—none of which are captured by annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that efficiently conveys the tool's purpose and actions. It is front-loaded with the verb and resource, and every clause adds specific detail. However, 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.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description should explain what the tool returns. It only mentions 'reporting changed fields' without specifying the structure. Given the complexity (2 params, cleaning operations), this is insufficient for an agent to fully understand the tool's behavior.
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 coverage is 50%: the 'rows' parameter lacks a description, and the tool description does not clarify what keys or format those objects should have. The description mentions 'normalizing headers' which aligns with the 'normalizeHeaders' parameter, but otherwise adds no value beyond 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?
The description uses a specific verb ('Clean') and resource ('CSV-like row objects'), and lists concrete actions (trimming, normalizing, removing empty rows, reporting changes). This clearly distinguishes it from sibling tools like image_audit or inventory_checker.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not provide any guidance on when to use this tool versus alternatives, nor does it specify prerequisites or typical context. It only states what the tool does without usage boundaries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
duplicate_sku_finderBFind duplicate SKUs, duplicate titles, and possible variant conflicts in supplied catalog rows.
| Name | Required | Description | Default |
|---|---|---|---|
| rows | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds the finding of duplicates and conflicts but lacks details on output format, variant conflict definition, 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?
Single sentence with no wasted words, front-loading the core function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Adequate for a simple read-only tool with good annotations, but lacks details about output format and interpretation of results, which could be important for an agent.
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 coverage is 0%, and the description provides no additional meaning about the 'rows' parameter beyond its name. It does not specify the expected structure or properties of the array items.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the verb 'Find' and specifies the resources (duplicate SKUs, titles, variant conflicts) in catalog rows, clearly distinguishing it from siblings like csv_cleaner or product_catalog_validator.
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 tool vs alternatives, no prerequisites, and no mention of limitations or exclusions. The description assumes the agent already knows the context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
generate_service_recommendationARecommend eData4You services based on business type, platforms, challenges, and goals.
| Name | Required | Description | Default |
|---|---|---|---|
| goals | No | Desired outcomes. | |
| platforms | No | Platforms such as Amazon, Shopify, Walmart, WooCommerce, or eBay. | |
| challenges | No | Current operational or growth challenges. | |
| businessType | Yes | Business type or industry. | |
| monthlyOrderVolume | No | Optional monthly order volume. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare it read-only, idempotent, non-destructive. The description adds context that the recommendation is based on business inputs, which is useful beyond annotations. Could mention output format but not required.
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 of 16 words, no filler, perfectly front-loaded with verb and resource. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity, no output schema, and robust annotations, the description sufficiently covers the purpose and inputs. The agent can confidently invoke it knowing what to provide and what to expect (a recommendation).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description restates the parameters (business type, platforms, challenges, goals) as the basis for recommendation, adding minimal extra meaning beyond what the schema provides.
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 verb 'recommend' and the resource 'eData4You services', and lists the specific inputs (business type, platforms, challenges, goals). This distinguishes it from sibling tools like search_services which only search.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage: when you need service recommendations based on those parameters. However, it does not explicitly state when not to use it or mention alternatives, leaving the agent to infer.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_blog_postARead one eData4You blog post by slug.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Blog post slug. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds minimal behavioral context beyond reading by slug; no mention of return format or side effects. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no redundant words. It efficiently conveys the tool's purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description omits details about the return structure of the blog post. While the tool's name implies a post object, additional context on fields would improve completeness for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for the single parameter 'slug', which already has a description. The tool description adds no extra meaning beyond what the schema provides, earning the baseline score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Read'), the resource ('one eData4You blog post'), and the method ('by slug'), effectively distinguishing it from sibling tools like list_blog_posts.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when a specific slug is known, but lacks explicit guidance on when to use versus alternatives like list_blog_posts or search_resources. No exclusions or when-not cases are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_case_studyARead one ecommerce case study by slug.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Case study slug. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already provide readOnlyHint, idempotentHint, and destructiveHint, indicating a safe read operation. The description adds the 'ecommerce' domain but does not disclose any additional behavioral traits beyond what the annotations already convey. Thus, it meets the baseline but adds minimal extra context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is one short, clear sentence with no extraneous words. It is front-loaded with the verb and resource, and each word adds value. No waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, no output schema), the description covers the core functionality. It specifies the action and the identifier. The annotations provide safety context. Missing are details on return value format, but for a straightforward read-by-slug tool, this is acceptable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a single parameter 'slug' described as 'Case study slug.' The description reinforces that the slug identifies a case study and adds the 'ecommerce' qualifier, but it does not provide substantial additional meaning beyond the schema. Baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Read one ecommerce case study by slug,' specifying the verb (read), resource (ecommerce case study), and the retrieval method (by slug). It distinguishes itself from the sibling tool 'search_case_studies' which is for searching multiple studies.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when a specific slug is available, contrasting with the sibling 'search_case_studies' which is for querying. However, it lacks explicit guidance on when not to use this tool or mention of alternatives, relying on implicit understanding from the sibling list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_glossary_termARead one ecommerce glossary term by slug.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Glossary term slug. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds minimal behavioral context beyond the basic read operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single 6-word sentence, concise and front-loaded with the core information. No unnecessary words or sentences.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read tool with full schema coverage, clear annotations, and no output schema, the description provides adequate context to understand the tool's purpose and usage.
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 coverage is 100% with the slug parameter described as 'Glossary term slug.' The description repeats 'by slug,' adding no new semantic value. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (read), resource (one ecommerce glossary term), and method (by slug). It distinguishes itself from sibling tools like search_glossary, which likely returns multiple results.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use this tool (retrieve a single term by slug) but does not explicitly state when not to use it or mention alternatives. The context of sibling tools provides some differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_marketplace_newsARead one marketplace news item by slug.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Marketplace news slug. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the description's job is to add extra context beyond safety. It adds no additional behavioral traits such as rate limits or authentication needs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
One sentence, front-loaded, no redundant words. Perfectly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read tool with full schema coverage and good annotations, the description is complete. It states the purpose and retrieval mechanism, with no missing information.
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 coverage is 100%, with the schema already describing the 'slug' parameter. The description merely repeats 'by slug' without adding new meaning or format details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Read', the resource 'marketplace news item', and the method 'by slug'. It distinguishes itself from sibling tools like 'latest_marketplace_news' which likely lists or retrieves the latest items.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when you have a known slug, but does not explicitly state when not to use it or mention alternatives. However, the context is clear for a single-item retrieval tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_pricing_guidanceBReturn practical pricing guidance and quote factors for a service need.
| Name | Required | Description | Default |
|---|---|---|---|
| scope | No | Known scope, volume, channels, or urgency. | |
| service | No | Service or project type. | |
| skuCount | No | Optional SKU count. | |
| monthlyOrderVolume | No | Optional monthly order volume. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the description's role is to add context. It adds minimal behavioral detail (e.g., 'practical guidance' implies non-binding), but does not contradict 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?
Single sentence with no wasted words. Front-loaded with action verb 'Return', making it immediately clear what the tool does.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple input schema and annotations, the description adequately conveys the tool's purpose but lacks details on output structure or examples, making it 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 coverage is 100% with clear parameter descriptions. The tool description does not add further meaning beyond the schema, meeting the baseline expectation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns 'practical pricing guidance and quote factors' for a service need, using a specific verb and resource. It distinguishes from siblings like pricing_calculator and generate_service_recommendation, but does not explicitly differentiate.
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 tool versus alternatives like pricing_calculator or generate_service_recommendation. The description only states what it does, missing context about appropriate scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_resourceARead one resource guide by slug.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Resource guide slug. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds no behavioral context beyond the verb 'Read', which aligns well.
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?
A single sentence of five words, directly front-loading the core action and resource. 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?
For a minimal read tool with one parameter and full annotation coverage, the description is adequate. It does not describe the return format but 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?
Schema coverage is 100% with a clear description for the 'slug' parameter. The description restates 'by slug' but adds no extra semantics beyond 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?
Description clearly states the verb 'Read' and the resource 'one resource guide by slug', differentiating from sibling getters that target different content types.
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 tool versus alternatives like search_resources, but the purpose is straightforward for a simple getter with a known slug.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_service_pageAGet one service page summary by path or slug, plus related blog posts, resources, and glossary terms.
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | Service page path or slug, for example /amazon-ppc-management-services. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds behavioral context by specifying it returns a summary plus related items (blog posts, resources, glossary terms), which is valuable information beyond what annotations provide.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that efficiently conveys purpose and outputs without redundancy. Every word adds value, making it highly concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has only one parameter, no nested objects, and annotations covering safety, the description provides sufficient context. It clearly states what is returned (summary plus related content) even without an output schema, making it complete for agent 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?
Schema description coverage is 100%, with the single 'path' parameter well-documented in the schema itself. The description repeats 'by path or slug' but does not add new meaning or constraints beyond the schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves a service page summary along with related blog posts, resources, and glossary terms. It uses specific verb 'Get' and identifies distinguishing elements (path/slug lookup) that differentiate it from siblings like get_blog_post or search_services.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context (by path or slug) but does not explicitly state when to use this vs alternatives like search_services or get_blog_post. No when-not or exclusion criteria provided, though the listed outputs hint at differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
google_sheets_managerCCreate a Google Sheets structure, tabs, columns, formulas, validation rules, and automation notes for an operations workflow.
| Name | Required | Description | Default |
|---|---|---|---|
| columns | No | ||
| useCase | Yes | Workflow, for example inventory tracker or PPC report. | |
| rowsEstimate | No | Optional expected row count. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description claims the tool creates structures, implying a write operation, but annotations declare readOnlyHint: true. This direct contradiction severely misleads the agent about the tool's side effects. The description fails to disclose this inconsistency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, actionable sentence that front-loads the main action. However, it lists many details in a run-on manner, slightly reducing readability. It is efficient but could benefit from bullet points or clearer structure.
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?
The description lacks critical context: no output schema, no error handling, no mention of permissions or consequences. The contradiction with annotations is unaddressed, and the tool's behavior under failure is unknown. For a tool with moderate complexity, this is severely incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description does not clarify the schema parameters. It mentions 'columns' but provides no additional meaning beyond the schema's definitions. With 67% schema description coverage, the tool description should compensate but does not. The meaning of 'useCase' and 'rowsEstimate' remains implicit.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool creates a Google Sheets structure with specific elements like tabs, columns, formulas, and validation rules for an operations workflow. The verb 'Create' and resource 'Google Sheets structure' are precise, and the context of 'operations workflow' differentiates it from 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?
No guidance is provided on when to use this tool versus alternatives. It does not mention exclusions, prerequisites, or contexts where the tool is inappropriate. The description solely defines the tool's purpose without usage conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
image_auditAAudit product image metadata for missing URLs, weak alt text, low dimensions, duplicate URLs, and main-image readiness.
| Name | Required | Description | Default |
|---|---|---|---|
| images | Yes | ||
| platform | No | Optional platform: Amazon, Shopify, Walmart, or Generic. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, so the agent knows this is a safe, read-only operation. The description adds specific behavioral context by detailing the exact checks performed (missing URLs, weak alt text, etc.), which enhances transparency beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that immediately states the purpose and lists the audit criteria. It is front-loaded with all key information and contains no unnecessary words.
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?
The description lists the checks performed, which is adequate for a simple audit tool. However, the input schema for 'images' is underdefined (array of objects with no schema), and there is no output schema. This leaves gaps about expected input structure and return format.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 50%: the 'platform' parameter has a brief description, but 'images' is an array of objects with no description in schema or tool description. The tool description does not clarify the expected structure of the image objects, so it adds no meaning beyond 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?
The description clearly states the tool audits product image metadata for specific issues: missing URLs, weak alt text, low dimensions, duplicate URLs, and main-image readiness. The verb 'audit' and resource 'product image metadata' are specific, and the listed checks distinguish it from sibling tools like 'csv_cleaner' or 'duplicate_sku_finder'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool vs alternatives. It does not mention contexts where the tool is appropriate, prerequisites, or situations where other tools would be better suited.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
inventory_checkerBCheck inventory rows for low stock, stockout risk, reorder quantities, and stale inventory signals.
| Name | Required | Description | Default |
|---|---|---|---|
| rows | Yes | ||
| reorderDays | No | Target days of stock to keep. Default 30. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the agent knows the tool is safe and non-modifying. The description adds that it checks for specific stock conditions, but does not elaborate on behavioral aspects like return format, pagination (if any), or data freshness. It does not contradict annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that efficiently conveys core functionality. It is front-loaded with the purpose. No fluff, but could be slightly more structured (e.g., bullet points) for readability. Still, it earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description should clarify what the tool returns (e.g., a list of problematic rows, a summary). It does not. Annotations are rich, but the description lacks coverage of output behavior. With 2 parameters and moderate complexity, the description is incomplete in conveying the tool's full interface.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 50% description coverage (reorderDays has a description, rows does not). The tool's description gives context by listing the conditions checked, which implies what the rows input should contain (stock data). However, the 'rows' parameter remains under-described: agents need to know required fields (e.g., product ID, current stock, lead time). The description adds some value but leaves significant gaps.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the resource (inventory rows) and the actions (checking for low stock, stockout risk, reorder quantities, stale signals). However, the verb 'check' is generic; a more specific verb like 'analyze' or 'evaluate' would enhance clarity. The purpose is distinct from siblings since no other tools explicitly deal with inventory health checks.
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 tool versus alternatives. The description does not mention prerequisites, typical scenarios (e.g., before reordering), or when not to use it. Sibling tools exist for product management and catalog validation, but no differentiation is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
latest_marketplace_newsBList latest marketplace news items for Amazon, eBay, Shopify, Walmart, Etsy, and TikTok Shop.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Default 8, max 20. | |
| platform | No | Optional platform filter. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds the context of listing the latest news for specific platforms, which is helpful but not extensive behavioral disclosure beyond what annotations imply.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that immediately conveys the tool's purpose. It is concise with no extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple listing tool with two optional parameters and robust annotations, the description provides adequate context. However, it could mention the output format or note the relationship to 'get_marketplace_news' for completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for both 'limit' and 'platform'. The description adds no additional meaning beyond the schema, so the baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it lists latest marketplace news items for specific platforms, using a precise verb and resource. It distinguishes implicitly from the sibling 'get_marketplace_news' (which likely retrieves a single item), but does not explicitly mention this difference.
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 is provided on when to use this tool versus alternatives like 'get_marketplace_news'. There is no mention of prerequisites or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_available_toolsAList available MCP tools with descriptions and protection level.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, destructiveHint, and idempotentHint. The description only adds that results include 'descriptions and protection level', offering minimal behavioral context beyond 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?
Single sentence with clear purpose. Efficient but could benefit from additional structure or details about return format. Still avoids waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (no parameters, no output schema), the description is adequate but lacks specifics about what a 'protection level' is or how the output list is structured.
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?
No parameters defined, so schema coverage is 100%. With zero parameters, the description does not need to add parameter info; baseline score of 4 is appropriate.
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 verb 'List', resource 'available MCP tools', and includes the information returned ('descriptions and protection level'). Distinguishes from all sibling tools, as this is the only tool that lists tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Description implies usage context (listing tools) but provides no explicit guidance on when to use or not use this tool, nor alternatives among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_blog_postsAList recent eData4You blog posts with titles, excerpts, dates, categories, and URLs.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Default 8, max 20. | |
| query | No | Optional title, excerpt, or category filter. | |
| category | No | Optional exact category filter. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds that it returns specific fields but does not disclose default ordering, pagination details, or any edge-case behaviors. With annotations covering safety, the description adds limited extra value.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence with verb and resource front-loaded. No fluff; every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description covers what the tool returns. However, it omits ordering (e.g., by date descending) and pagination behavior. For a simple list tool, it is mostly 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 coverage is 100%, with adequate parameter descriptions. The description does not add meaningful semantics beyond what the schema already provides (limit default 8, max 20; optional query and category filters). Baseline 3 applies.
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?
Description clearly states the verb 'List', the resource 'recent eData4You blog posts', and specifies the returned fields (titles, excerpts, dates, categories, URLs). It distinguishes itself from sibling tools like get_blog_post (single post retrieval) and search_resources (broader search).
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 tool versus alternatives. Sibling tools like get_blog_post and search_resources exist, but the description does not contrast them or provide usage context beyond 'recent'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
pricing_calculatorBCalculate target selling price, margin, break-even price, ad allowance, and marketplace-fee impact.
| Name | Required | Description | Default |
|---|---|---|---|
| cost | Yes | Product cost. | |
| shipping | No | Shipping/fulfillment cost. | |
| adCostPercent | No | Expected ad cost percentage. | |
| targetMarginPercent | No | Target net margin percentage. | |
| marketplaceFeePercent | No | Marketplace fee percentage. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds no additional behavioral context (e.g., no pricing tiers, no rate limits, no authentication requirements). It is adequate but does not enhance understanding beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence listing outputs. It is concise and to the point, but lacks structural elements like bullet points or sections that could improve readability for an agent.
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?
With no output schema and five parameters, the description fails to clarify the return format, default values, or edge cases (e.g., what happens if cost is missing? negative?). As a calculator tool, more completeness is needed to guide correct 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?
Schema coverage is 100% with clear parameter descriptions. The tool description does not add new meaning or examples beyond what the schema provides. Baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly lists the outputs (target selling price, margin, break-even price, ad allowance, marketplace-fee impact), making it clear what the tool calculates. It distinguishes itself from sibling tools like get_pricing_guidance by focusing on computation rather than advice or listing management.
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 is given on when to use this tool versus alternatives (e.g., get_pricing_guidance). There is no mention of prerequisites, context, or scenarios where this calculation is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
product_catalog_validatorAValidate supplied product rows for missing SKU/title/price/category/image fields, invalid prices, duplicate SKUs, and platform readiness.
| Name | Required | Description | Default |
|---|---|---|---|
| rows | Yes | ||
| platform | No | Optional platform: Shopify, Amazon, Walmart, or Generic. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds meaningful behavioral context beyond the annotations. It specifies the types of validation performed (missing fields, prices, duplicates, platform readiness), which aligns with the non-destructive, idempotent nature declared by annotations. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the action and key details. Every element contributes to understanding, 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?
The description covers the tool's purpose and validations, but it omits what the output or return value looks like. Since there is no output schema, an agent cannot know if the tool returns a report, errors, or a simple pass/fail. This is a gap for a tool performing complex validations.
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 50% schema description coverage, the description enriches the meaning of the 'rows' parameter by listing expected fields (SKU, title, price, category, image) and validation dimensions. For the 'platform' parameter, the schema description is already clear. This adds significant value beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Validate' and the resource 'product rows', listing specific validation checks (missing fields, invalid prices, duplicate SKUs, platform readiness). However, it does not explicitly distinguish itself from sibling tools like 'duplicate_sku_finder' or 'csv_cleaner', which have overlapping functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. There is no mention of when-not-to-use or which sibling tools might be more appropriate for specific subtasks, such as duplicate SKU only validation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
proposal_generatorAGenerate a client proposal structure with scope, deliverables, timeline, assumptions, and next steps.
| Name | Required | Description | Default |
|---|---|---|---|
| scope | No | Known project scope. | |
| client | No | Client or company name. | |
| service | Yes | Requested service. | |
| timeline | No | Desired timeline. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description uses 'Generate' which is consistent with a read-only operation (producing output without side effects). It does not add behavioral context beyond what annotations provide, but does not contradict them either.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that is front-loaded and contains no redundant words. Every phrase earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 4 parameters and no output schema, the description provides a high-level overview but lacks details about the output format, usage examples, or any prerequisites. It is adequate but not comprehensive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with all parameters described in the schema. The description adds no additional meaning for the parameters themselves; it only lists output components not in the schema. Since the schema already documents parameters, the description's value for parameter semantics is minimal.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates a client proposal structure, specifying the included components (scope, deliverables, timeline, assumptions, next steps). This is a specific verb-resource combination that distinguishes it from sibling tools like blog_generator or pricing_calculator.
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 tool versus alternatives. There is no mention of prerequisites, contexts, or exclusions. The description solely states what it does without indicating appropriate use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_case_studiesASearch ecommerce case studies by title, vertical, tags, challenge, solution, or results.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Default 6, max 12. | |
| query | No | Optional case study search text. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds that the search can use multiple query fields, but this does not contradict annotations or provide significant new behavioral insight beyond what annotations imply.
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, front-loaded with verb and resource, no wasted words. Every part earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple search tool with 2 parameters, the description covers the purpose but does not explain the return format, pagination, or default behavior (though default limit is in schema). Annotations cover safety adequately. Slightly more detail about output would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for both 'limit' and 'query'. The description adds meaning by specifying the fields that can be searched (title, vertical, tags, etc.), which is more specific than the schema's 'Optional case study search text'. This helps the agent understand how to use the 'query' parameter effectively.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Search', the resource 'ecommerce case studies', and the searchable fields. It distinguishes from sibling tools like 'get_case_study' (retrieval of a single case study) and 'search_resources' (broader resource search).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for searching case studies but does not explicitly state when to use this tool versus alternatives. Siblings exist for specific case studies ('get_case_study') and broader searches ('search_resources'), but no guidance on when not to use this tool is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_glossaryASearch the ecommerce glossary for definitions and related terms.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Default 10, max 25. | |
| query | Yes | Term, category, or definition search text. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds that the tool returns definitions and related terms, but no further behavioral traits (e.g., auth, rate limits) are disclosed. The description adds some value beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that efficiently conveys the tool's purpose with no unnecessary words. It is well-structured and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple search tool with rich annotations and no output schema, the description is adequate. It could be more complete by explicitly stating the return format, but it is sufficient for an agent to understand its use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with both parameters documented. The tool description does not add any additional meaning or constraints beyond what the schema already provides, so baseline score of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Search', the resource 'ecommerce glossary', and the outcome 'definitions and related terms'. It distinguishes itself from sibling tools like get_glossary_term which likely retrieves a specific term, and other search tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no explicit guidance on when to use this tool versus alternatives like get_glossary_term or other search tools. No when-not-to-use or context is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_resourcesBSearch eData4You resource guides by title, excerpt, category, tags, or content.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Default 8, max 20. | |
| query | No | Optional resource search text. | |
| category | No | Optional exact category filter. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations (readOnlyHint, idempotentHint, destructiveHint) already indicate a safe, read-only operation. The description adds that it searches by specific fields but does not disclose pagination, rate limits, or response behavior. It meets a baseline but adds minimal extra value.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence and front-loaded, but it includes unsupported search fields (tags, content) that are not in the schema. While concise, it sacrifices accuracy, earning an average score.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description should clarify the return format (e.g., list of resources). It mentions searchable fields but omits what the tool returns. For a simple read search, it is somewhat complete but has a clear gap.
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 coverage is 100% with descriptions for limit, query, and category. However, the description mentions searching by 'tags, or content' which are not reflected in the schema as separate parameters, potentially misleading an agent. This inconsistency reduces clarity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (Search), the target (eData4You resource guides), and the searchable fields (title, excerpt, category, tags, or content). This distinguishes it from sibling tools like search_case_studies or search_glossary, which target different content types.
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 is provided on when to use this tool versus alternatives (e.g., get_resource for a specific resource, or other search tools). The description only states what it does, not the context or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_servicesBSearch eData4You service pages and return matching URLs for ecommerce, marketplace, marketing, data, and web services.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Default 8, max 20. | |
| query | Yes | Search text, for example Amazon PPC, Shopify support, data entry, or website maintenance. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, which describe safety. The description adds that it returns URLs but does not disclose any additional behavioral traits such as pagination, result ordering, or network dependencies, providing minimal extra value beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that efficiently states the core functionality. It is front-loaded with the action and outcome, but lacks any structural separation (e.g., bullet points) that could improve readability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (2 parameters, no output schema, clear annotations), the description provides enough context: what it searches, for what results. It is complete enough for an agent to understand the tool's basic purpose.
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 coverage is 100% (both parameters described). The description does not add meaning beyond the schema; it simply restates the purpose. Baseline 3 is appropriate as the schema already documents parameter names and their types/ranges.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Search', the resource 'eData4You service pages', and the return value 'matching URLs'. It specifies domains like ecommerce, marketplace, marketing, data, and web services, distinguishing it from sibling search tools for case studies, glossary, and resources.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description offers no explicit guidance on when to use this tool versus alternatives (e.g., search_case_studies). It does not mention exclusions or context for optimal use, leaving the agent to infer based on sibling names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
seo_generatorBGenerate SEO metadata, heading structure, keyword clusters, FAQ ideas, and internal-link targets for a page.
| Name | Required | Description | Default |
|---|---|---|---|
| topic | Yes | Page or article topic. | |
| audience | No | Optional target audience. | |
| pageType | No | service, blog, resource, category, product, or landing. | |
| primaryKeyword | No | Primary SEO keyword. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety and idempotency. The description adds no behavioral traits beyond these, which is acceptable but does not enhance the agent's understanding of side effects or permissions.
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, no filler. Every word adds value, and the description is front-loaded with the action and outputs.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a read-only, idempotent tool with 4 well-documented parameters and no output schema, the description covers the main outputs. It could mention the return format (e.g., structured JSON), but overall it is sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with all 4 parameters documented. The description does not add new meaning beyond the schema (e.g., it does not explain format or constraints). Baseline 3 is appropriate as schema handles the semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates SEO metadata, heading structure, keyword clusters, FAQ ideas, and internal-link targets for a page. It uses a specific verb and lists the outputs, but does not differentiate from sibling tools like blog_generator that may also produce SEO content.
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 tool versus alternatives. It does not specify context, prerequisites, or when not to use it. Sibling tools like blog_generator or generate_service_recommendation could overlap, but no exclusions are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
shopify_product_managerBCreate a Shopify product setup plan, required fields, SEO fields, collection logic, and launch QA checklist.
| Name | Required | Description | Default |
|---|---|---|---|
| product | No | Product fields such as title, sku, price, vendor, type, tags, description. | |
| keywords | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, indicating a safe, non-mutating operation. The description adds little beyond stating it 'Creates a plan', which aligns with read-only behavior but does not disclose any additional traits like auth requirements or that no actual Shopify mutation occurs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, front-loading the key purpose. It is concise and contains no extraneous information. However, it could be slightly more structured by separating the output elements.
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?
The description informs that the tool outputs a setup plan with multiple components, which is adequate for a generative tool with no output schema. However, it lacks details on output format (e.g., text, structured data) and fails to specify any prerequisites or edge cases.
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?
Input schema has two parameters: 'product' (object) and 'keywords' (array). Schema description coverage is 50% (only 'product' has a description). The tool description broadly mentions required fields, SEO fields, etc., but does not elaborate on parameter usage, format, or semantics beyond what the schema provides.
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?
Description clearly states it creates a Shopify product setup plan with specific elements (required fields, SEO, collection logic, QA checklist). The verb 'Create' and resource 'plan' are specific, distinguishing it from siblings that manage listings on other platforms. However, the name 'shopify_product_manager' could imply direct product management, creating slight ambiguity.
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?
Usage is implied: use when setting up a Shopify product. There is no explicit guidance on when not to use this tool or alternatives. Among siblings, there are similar listing managers for Amazon and Walmart, but no direct comparison is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
sop_searchASearch eData4You content for SOP-like guidance and return workflow steps for the requested operation.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Default 8, max 20. | |
| query | Yes | Operation or SOP topic. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, so the description adds little new behavioral context. It confirms the tool searches and returns workflow steps but does not disclose additional behaviors like pagination, rate limits, or search scope boundaries.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with no redundant words. It front-loads the action and resource, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (2 params, no output schema, clear annotations), the description covers the core purpose and output. However, it omits details about what 'eData4You content' is and whether search is across all such content, but overall it is sufficiently complete for an agent to understand basic usage.
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 coverage is 100% with both parameters having descriptions ('Operation or SOP topic' for query, default and max for limit). The description does not add any additional semantic meaning beyond the schema, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Search', the resource 'eData4You content', the scope 'SOP-like guidance', and the output 'workflow steps'. It distinguishes from sibling search tools like search_case_studies or search_services by focusing on SOP content.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use when seeking SOP guidance or workflow steps but does not explicitly state when to use this tool versus alternatives. No exclusions or when-not-to-use guidance is provided, which is a gap given the many sibling search tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
submit_lead_requestAInspectProtected tool. Submit a sanitized lead request into server logs for follow-up. Requires MCP write authorization.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Contact name. | |
| Yes | Contact email. | ||
| phone | No | Optional phone or WhatsApp. | |
| company | No | Company or brand name. | |
| message | Yes | Project details. | |
| service | Yes | Requested service. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate the tool is not read-only and not destructive. The description adds value by clarifying the write operation ('submit'), the sanitization step, and the authorization requirement, providing behavioral context beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description consists of two short, front-loaded sentences with no superfluous words. Every phrase ('Protected tool', 'sanitized', 'server logs for follow-up') contributes essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple submission tool, the description provides clear purpose, authorization needs, and behavior. No output schema is expected, and the annotations cover safety. The description is complete enough for an agent to use correctly, though it omits details like error handling or success response.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and includes descriptions for all 6 parameters (e.g., 'name', 'email', 'phone'). The description does not add new semantic meaning beyond stating which parameters are required (name, email, service, message), which is already captured in the schema's required array.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('submit'), the resource ('sanitized lead request'), and the destination ('server logs for follow-up'). This is a specific verb+resource combination that distinguishes it from the sibling tools, none of which handle lead submission.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description indicates it is a 'Protected tool' and 'Requires MCP write authorization', setting clear access requirements. While it does not explicitly contrast with alternatives, the sibling list contains no similar lead submission tool, making the usage context self-evident.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
walmart_listing_managerCCreate a Walmart Marketplace listing plan with item setup fields, content checks, taxonomy needs, and QA steps.
| Name | Required | Description | Default |
|---|---|---|---|
| product | No | Product fields such as title, sku, category, price, attributes, images. | |
| keywords | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate readOnlyHint=true, but description uses 'create', implying a mutation. This is a contradiction per evaluation rules. Additionally, no extra behavioral context is provided (e.g., no mention that the plan is generated without side effects).
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, 17 words, front-loaded with purpose. Efficient but packs multiple concepts; could be broken into two sentences for clarity.
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?
No output schema provided; description does not explain return format or how the plan is delivered. Given both parameters are optional, missing guidance on expected inputs. Annotations provide safety but description lacks sufficient behavioral depth.
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 coverage is 50% (product described, keywords not). Description adds no parameter-level detail; keywords are not mentioned. The description's 'item setup fields' loosely maps to product but adds no specific 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?
Description clearly states the tool creates a Walmart Marketplace listing plan, listing included aspects like item setup, content checks, taxonomy needs, and QA steps. This distinguishes it from siblings like amazon_listing_manager.
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 tool versus alternatives (e.g., product_catalog_validator). No mention of prerequisites, context, or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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