Skincare Question and Answer with cited products, ingredients, sources, and comparison context.
Fetch one public SkinKnowledgeBase resource. Required input: id, which may be a canonical SKB URL, public comparison URL, entity ULID, slug, or accepted comparison-page identifier. Returns public read-only text and structured data such as title, canonical URL, entity type, evidence sources, product fact sources, updated_at, and public-safe comparison metadata where applicable.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes |
| Name | Required | Description |
|---|---|---|
| ok | Yes | Whether the SKB MCP tool call succeeded. |
| data | No | Tool-specific public SkinKnowledgeBase response data. |
| meta | No | Response metadata for successful calls. |
| error | No | Error details when ok is false. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint, idempotentHint, destructiveHint. Description adds context about returning public read-only text and structured data, and lists specific fields. No contradictions; 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?
Two sentences: first states purpose, second details return type. No wasted words; front-loaded with key action and required input.
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 presence of output schema, description adequately lists return fields. Might benefit from mentioning behavior when id not found, but overall sufficient for a straightforward fetch tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but description thoroughly explains the id parameter: accepts canonical SKB URL, public comparison URL, entity ULID, slug, or accepted comparison-page identifier. Adds significant meaning beyond schema constraints.
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 fetching one public SkinKnowledgeBase resource, specifies verb and resource type, and lists returned elements. Differentiates from siblings like search or list by focusing on single resource retrieval.
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?
Mentions required input id and acceptable formats, but does not explicitly say when to use this tool versus alternatives like get_entity or search. Implied use for known identifiers.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch one public SkinKnowledgeBase entity. Required inputs: type (Question, Concern, Ingredient, Product, SideEffect, or Source) and slug_or_id (entity slug or ULID). Returns public entity details, canonical URL, text, and linked source/context fields where available.
| Name | Required | Description | Default |
|---|---|---|---|
| type | Yes | ||
| slug_or_id | Yes |
| Name | Required | Description |
|---|---|---|
| ok | Yes | Whether the SKB MCP tool call succeeded. |
| data | No | Tool-specific public SkinKnowledgeBase response data. |
| meta | No | Response metadata for successful calls. |
| error | No | Error details when ok is false. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only and safe behavior. Description adds that entity must be public and details what is returned (canonical URL, text, linked fields), beyond annotation cues.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with purpose, then required inputs, then outputs. No redundant 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?
Given low complexity (2 params, output schema exists), description covers purpose, inputs, and output fields sufficiently. No missing critical 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 0%, but description compensates by explaining type enum values and slug_or_id as slug or ULID, and notes they are required. Adds clarity beyond the raw 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 uses specific verb 'Fetch' and resource 'one public SkinKnowledgeBase entity', clearly distinguishes from siblings like list_entities and search_entities by specifying entity type and identifier.
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?
Implies usage for fetching a single entity by type and slug/ID, but no explicit when-to-use vs alternatives like search or get_question_bundle.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch a public SkinKnowledgeBase Question bundle. Required input: slug_or_id, accepting a question slug or ULID. Optional booleans include include_sources, include_products, and include_validation_summary. Returns the public question answer structure and requested linked public context.
| Name | Required | Description | Default |
|---|---|---|---|
| slug_or_id | Yes | ||
| include_sources | No | ||
| include_products | No | ||
| include_validation_summary | No |
| Name | Required | Description |
|---|---|---|
| ok | Yes | Whether the SKB MCP tool call succeeded. |
| data | No | Tool-specific public SkinKnowledgeBase response data. |
| meta | No | Response metadata for successful calls. |
| error | No | Error details when ok is false. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations (readOnlyHint, idempotentHint, destructiveHint) already indicate read-only, idempotent behavior. The description adds that the tool is for 'public' data and returns 'public question answer structure', providing some 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?
The description is two sentences with no wasted words. It front-loads the purpose and efficiently lists inputs and output.
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 mentions the output ('public question answer structure and requested linked public context'), and there is an output schema. For a straightforward fetch tool, this is nearly complete; however, the term 'bundle' could be clarified.
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%, so the description must compensate. It explains that 'slug_or_id' accepts a slug or ULID, adding meaning. However, the three boolean parameters are only listed as 'Optional booleans' without explaining their effect, leaving 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 states the tool fetches a 'public SkinKnowledgeBase Question bundle', using a specific verb and resource. It distinguishes itself from siblings like get_sources_for_question or get_ranked_products_for_question by focusing on the bundle concept.
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 lists required and optional inputs but does not explicitly state when to use this tool over siblings. Usage context is implied (fetching a bundle), but no alternatives or when-not-to-use guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Deprecated read-only alias for get_ranked_products_for_question. Required input: slug_or_id, accepting a question slug or ULID. Returns the same public supporting product context, capped at two products, without modifying anything.
| Name | Required | Description | Default |
|---|---|---|---|
| slug_or_id | Yes |
| Name | Required | Description |
|---|---|---|
| ok | Yes | Whether the SKB MCP tool call succeeded. |
| data | No | Tool-specific public SkinKnowledgeBase response data. |
| meta | No | Response metadata for successful calls. |
| error | No | Error details when ok is false. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations that already declare readOnlyHint, idempotentHint, and destructiveHint, the description adds key behaviors: it returns public supporting product context, is capped at two products, and is read-only (without modifying anything). The deprecation note is also behavioral.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: two sentences cover deprecation, input, and behavior. Every sentence adds value, and the most critical information (deprecation, alias) is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter, existing annotations, and an output schema, the description is complete. It covers the deprecation status, input requirements, return behavior, and non-destructive nature, leaving no major gaps for 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?
Despite 0% schema description coverage, the description adds meaning by explaining that the single required parameter 'slug_or_id' accepts a question slug or ULID. This provides the agent with crucial semantic information beyond the schema's basic string type constraints.
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 states it is a deprecated read-only alias for get_ranked_products_for_question, clearly identifying the tool's purpose and distinguishing it from its sibling. It specifies the action (get), the resource (ranked product for question), and constraints (capped at two products).
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 directly guides the agent by labeling the tool as deprecated and directing to the alternative get_ranked_products_for_question. It also specifies the required input (slug_or_id) and its accepted formats (question slug or ULID).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return public supporting product context for one eligible SkinKnowledgeBase Question. Required input: slug_or_id, accepting a question slug or ULID. Returns at most two relevant products with public fit/caveat context; it does not purchase, recommend as medical advice, or modify anything.
| Name | Required | Description | Default |
|---|---|---|---|
| slug_or_id | Yes |
| Name | Required | Description |
|---|---|---|
| ok | Yes | Whether the SKB MCP tool call succeeded. |
| data | No | Tool-specific public SkinKnowledgeBase response data. |
| meta | No | Response metadata for successful calls. |
| error | No | Error details when ok is false. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnly, idempotent, non-destructive hints. The description adds that it returns at most two products and does not purchase, recommend as medical advice, or modify anything, enhancing 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?
Two sentences: first states purpose, second details required input and constraints. No wasted words, front-loaded with key 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?
Given one parameter, clear annotations, and output schema present, the description sufficiently covers return bounds, non-modification, and non-medical scope. No gaps identified.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description explains the required parameter 'slug_or_id' accepts a question slug or ULID, adding meaning beyond the schema's plain string type.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns public supporting product context for one eligible SkinKnowledgeBase Question, using specific verb and resource. It distinguishes from siblings like 'get_ranked_product_for_question' (singular) and list-oriented 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 indicates the tool returns at most two products and does not purchase or recommend, but does not explicitly compare to alternative tools like 'get_ranked_product_for_question' or 'get_question_bundle'. Lacks when-not guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return public Source entities cited by one SkinKnowledgeBase Question. Required input: slug_or_id, accepting a question slug or ULID. Returns source records and canonical URLs used to support the public question page.
| Name | Required | Description | Default |
|---|---|---|---|
| slug_or_id | Yes |
| Name | Required | Description |
|---|---|---|
| ok | Yes | Whether the SKB MCP tool call succeeded. |
| data | No | Tool-specific public SkinKnowledgeBase response data. |
| meta | No | Response metadata for successful calls. |
| error | No | Error details when ok is false. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only and idempotent behavior. The description adds useful context: the tool returns only public sources and is used to support the public question page. This clarifies the scope and purpose beyond the 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 consists of two concise sentences. The first states the purpose, the second clarifies input and output. No unnecessary words or repetition. Every sentence adds value.
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 presence of an output schema, the description adequately covers the tool's behavior: input format, output content, and read-only nature. Annotations cover safety. No additional details are needed for an agent to use this tool 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?
Even though schema coverage is 0%, the description fully explains the sole parameter slug_or_id: it accepts a question slug or ULID and is required. This adds critical semantic meaning not present in the schema definition, fully compensating for the lack of schema description.
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 public Source entities cited by one SkinKnowledgeBase Question. It specifies the input (slug_or_id accepting slug or ULID) and output (source records and canonical URLs). This distinguishes it from sibling tools like search_entities or get_question_bundle, which serve different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states the required input and what the tool returns, making its usage clear. However, it does not provide guidance on when not to use this tool or mention alternatives among siblings like get_question_bundle, which might also return sources. This lack of exclusion criteria prevents a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List public SkinKnowledgeBase entities. Optional input: entity_type (Question, Concern, Ingredient, Product, SideEffect, or Source). Returns published MCP-eligible entity identifiers and metadata for discovery; it does not expose drafts or private/internal data.
| Name | Required | Description | Default |
|---|---|---|---|
| entity_type | No |
| Name | Required | Description |
|---|---|---|
| ok | Yes | Whether the SKB MCP tool call succeeded. |
| data | No | Tool-specific public SkinKnowledgeBase response data. |
| meta | No | Response metadata for successful calls. |
| error | No | Error details when ok is false. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description reinforces this by stating it 'does not expose drafts or private/internal data' and aligns with the read-only nature. 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?
Two concise sentences with no wasted words. The first sentence states the core purpose, the second adds essential constraints and output description. Perfectly 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 list tool with one optional parameter and an output schema, the description fully explains what it returns (identifiers and metadata for discovery) and what it excludes (drafts/private), leaving no ambiguities.
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 only parameter (entity_type) is described in the description as optional and listing possible enum values, adding meaning that the schema (which lacks descriptions) does not provide. Could specify default behavior when omitted, but overall adequate.
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 tool lists public SkinKnowledgeBase entities, specifying the resource and action. The description distinguishes it from siblings by noting it returns published MCP-eligible identifiers and metadata for discovery, and explicitly excludes private/draft data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides context for use (discovery of public entities) and notes that it does not expose private data, implying when not to use it. However, it does not explicitly contrast with siblings like search_entities or search_questions to guide agent choice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search public SkinKnowledgeBase content. Required input: query string. Optional inputs: entity_type (Question, Concern, Ingredient, Product, SideEffect, Source), deprecated type alias, limit 1-50, and cursor. Returns compact ranked public results with canonical URLs, snippets, matched fields, scores, and public-safe retrieval metadata. Use for skincare questions, ingredient/product lookup, cited source lookup, and comparison-page discovery.
| Name | Required | Description | Default |
|---|---|---|---|
| type | No | Deprecated alias for entity_type; kept for compatibility. | |
| limit | No | ||
| query | Yes | ||
| cursor | No | ||
| entity_type | No | Preferred entity type filter. |
| Name | Required | Description |
|---|---|---|
| ok | Yes | Whether the SKB MCP tool call succeeded. |
| data | No | Tool-specific public SkinKnowledgeBase response data. |
| meta | No | Response metadata for successful calls. |
| error | No | Error details when ok is false. |
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 context that results are compact, ranked, public-safe, and include metadata like snippets and scores, enhancing 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?
Three sentences efficiently cover action, resource, inputs, outputs, and use cases with no redundant phrasing, front-loading the most critical 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?
Description covers purpose, parameters, output structure, and use cases. Given an existing output schema, it is mostly complete, though it omits details on pagination (how to use cursor) and query semantics (fuzzy matching, case sensitivity).
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 only 40% schema description coverage, the description should add meaning for undocumented parameters (e.g., query, cursor, limit). It merely lists them without explaining query syntax, cursor usage, or limit behavior, failing to compensate for the gap.
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 searches public SkinKnowledgeBase content, lists optional filters, and specifies use cases covering multiple entity types, distinguishing it from sibling tools like search_entities and search_questions that are entity-specific.
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 use-case examples (skincare questions, ingredient lookup, etc.) but does not explicitly contrast with alternatives or state when not to use this tool, leaving some ambiguity for an agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search public SkinKnowledgeBase entities. Required input: query string. Optional inputs: type (Question, Concern, Ingredient, Product, SideEffect, Source), limit 1-25, and cursor. Returns matching public entities with slugs/IDs and canonical URLs.
| Name | Required | Description | Default |
|---|---|---|---|
| type | No | ||
| limit | No | ||
| query | Yes | ||
| cursor | No |
| Name | Required | Description |
|---|---|---|
| ok | Yes | Whether the SKB MCP tool call succeeded. |
| data | No | Tool-specific public SkinKnowledgeBase response data. |
| meta | No | Response metadata for successful calls. |
| error | No | Error details when ok is false. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnly, idempotent, and non-destructive behavior. The description adds value by specifying the output format (slugs/IDs, URLs) and input constraints (limit 1-25), providing context beyond annotations without contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences front-load critical information: purpose, required/optional inputs, and output description. No fluff or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the output schema exists (not shown), the description appropriately omits return value details but provides slug/ID/URL hints. Inputs are well-covered, though cursor explanation is minimal. Sibling context aids tool selection.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description compensates well by listing the query as required, explaining the type enum and limit range, and mentioning cursor. However, it does not fully explain cursor semantics (e.g., pagination usage), leaving a minor gap.
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 searches public SkinKnowledgeBase entities, lists all inputs (required query, optional type, limit, cursor), and describes the output (matching entities with slugs/IDs and canonical URLs). It distinguishes itself from siblings like get_entity or search_questions by being a general multi-type 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?
It explicitly specifies required and optional inputs, implying usage for broad entity search. While it doesn't explicitly state when not to use it, the sibling tool list (e.g., get_entity, search_questions) provides enough context for the agent to infer alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search published public SkinKnowledgeBase Question pages. Required input: query string. Optional inputs: filters for concern, ingredient, product, or side_effect; limit 1-25; cursor. Returns public question search results with identifiers and canonical URLs for follow-up retrieval.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes | ||
| cursor | No | ||
| filters | No |
| Name | Required | Description |
|---|---|---|
| ok | Yes | Whether the SKB MCP tool call succeeded. |
| data | No | Tool-specific public SkinKnowledgeBase response data. |
| meta | No | Response metadata for successful calls. |
| error | No | Error details when ok is false. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover safety (readOnly, idempotent, non-destructive). The description adds value by detailing what the tool returns (identifiers and canonical URLs) and the input constraints (pagination via cursor, limit range). It does not repeat annotation information.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences effectively convey purpose, inputs, and outputs. No extraneous information. The description is front-loaded with the primary action.
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 presence of an output schema and the tool's complexity (4 parameters, nested object), the description adequately covers inputs and output summary. It provides enough context for an agent to use the tool 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?
With 0% schema description coverage, the description fully compensates by listing all parameters: required 'query', optional 'limit' (with min/max), 'cursor', and 'filters' object with four valid fields. This clearly explains their meaning and usage.
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 searches 'published public SkinKnowledgeBase Question pages,' specifying the verb 'search,' the resource 'Question pages,' and the scope 'published public.' This distinguishes it from siblings like 'get_entity' or 'fetch' which are retrieval operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on required and optional inputs, and mentions the output purpose for follow-up retrieval. However, it does not explicitly state when not to use this tool or mention alternatives like the generic 'search' tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
GitHub identity — fastest for official registry listings. For a namespace such as io.github.alice/server, link the matching GitHub user or an account that owns the GitHub organization, then choose Claim with GitHub.
HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at /.well-known/glama.json on the same origin as the connector, then choose Check HTTP challenge.
DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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Neutral, payment-blind reference of 1,682 dermacosmetic products: lookup, search, alternatives.
Answers questions about a business using its indexed website content, with cited sources.
Condition-aware ingredient and product checks for agents, with evidence tiers and citations.
AI product expert answering from live source code, with sources and a verification status.
Add one secure layer between your agents and this server.
Most tools have distinct purposes, but the deprecated alias get_ranked_product_for_question and the overlap between fetch (generic ID) and get_entity (type+slug) create slight ambiguity.
Naming mixes patterns: 'fetch' stands alone, while others use 'get_', 'search_', or 'list_'; the deprecated alias also deviates from the plural form of the newer tool.
10 tools cover querying, searching, and listing without being too many or too few; appropriate for a read-only knowledge base.
The set provides full read coverage: generic fetch, typed entity retrieval, question bundles with optional includes, product rankings, sources, lists, and multiple search variants. No write operations are expected for this use case.
