Superseded listing, not a dead server. This entry was imported from an early Glama registration under com.moltlinestudio.mcp; the same server (the fleet catalog and entry point) is listed under its official MCP Registry name — use com.moltlinestudio/moltline. Endpoint unchanged: https://mcp.moltlinestudio.com/catalog — still live, still free on the free tier. Only this duplicate entry is deprecated.
Search and load from 138 production agent personas and skills. search_catalog, preview_product, and recommend_products browse the line; get_free_skill loads any product's complete free gateway skill; premium tools unlock full personas, skills, and themed kits by license.
Most tools have clearly distinct purposes: listing, searching, recommending, previewing, and fetching different levels of detail. However, get_full_skill and get_free_skill could be confused since both return skill instructions but differ in which skill they target and plan requirements.
The naming is largely consistent with a verb_noun pattern (list_kits, preview_product, search_catalog, etc.). However, there are minor deviations: get_free_skill uses 'free' as a qualifier rather than a verb, and upgrade_info is noun-heavy instead of verb_noun.
With 10 tools covering discovery (search/recommend/list), preview, and detail retrieval across products, skills, personas, and kits, the count is well-scoped for a catalog API. Each tool has a clear role, and there is no noticeable bloat or deficiency.
The tool surface covers the full discovery-to-detail pipeline: browse/search/recommend, preview, then drill into free skill, full product, persona, kit, or specific paid skill. An 'upgrade_info' helper fills a support gap. A minor gap is the lack of a tool to list all products or browse by category without a keyword query.
Load a product's free gateway skill with its complete instructions. FREE.
Typical input {"slug": "inbox-zero-assistant"} returns {"slug": ..., "skill": "", "instructions": ""}.
Returns exactly one skill - the product's free gateway skill - chosen automatically from the slug, with no plan required. Use when the caller wants usable instructions immediately. Not for the product's other skills: those are named and need get_full_skill with a skill_name, which requires a paid plan. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "unknown slug ''"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Product slug exactly as returned by search_catalog, e.g. "inbox-zero-assistant". |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations (readOnlyHint, idempotentHint, destructiveHint), the description discloses that the tool never raises a protocol error but returns an error object on invalid input, and that it is safe to retry after correcting input. It also explains that the skill is chosen automatically with no plan 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?
The description is compact with three short paragraphs, each adding distinct value: purpose, example, usage guidelines and error behavior. Every sentence is informative with no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has one required parameter, no enums, and an output schema exists, the description fully covers purpose, example, error handling, relationship to siblings, and retry safety. It is wholly sufficient 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?
Schema coverage is 100% with the slug parameter already documented with source and example. The description does not add new parameter details beyond what the schema provides, so 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 'Load a product's free gateway skill with its complete instructions', specifying verb (load) and resource (free gateway skill). It distinguishes from sibling get_full_skill by noting that other skills require a skill_name and a paid plan. A typical input/output example reinforces the purpose.
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?
Explicitly says 'Use when the caller wants usable instructions immediately' and contrasts with 'Not for the product's other skills: those are named and need get_full_skill with a skill_name, which requires a paid plan.' This provides direct when-to-use and when-not-to-use guidance, naming the alternative tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Load a themed kit and every member product in full. PREMIUM (paid plan).
Typical input {"slug": "founder-kit"} returns {"slug": ..., "name": ..., "products": [{"slug": ..., "name": ..., "persona": ..., "skills": [...]}, ...]}.
Returns every member product of one bundle in full, so the payload is large. Use only after list_kits has confirmed the kit slug. Not for a single product (get_full_product). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "unknown kit ''"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Kit slug exactly as returned by list_kits, e.g. "founder-kit". |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (readOnlyHint, idempotentHint, destructiveHint), the description adds that the payload is large, error handling returns a structured error object with fix instructions, and that every call is read-only and idempotent. 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 somewhat long but each sentence serves a purpose. It is front-loaded with the main action and clearly separated into usage, return size, and error behavior. Could be slightly more concise, but still 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?
Given the tool has only one parameter, is read-only, idempotent, and has an output schema, the description covers all necessary aspects: purpose, prerequisite steps, payload size, error handling, and retry safety. It is fully 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%, so baseline is 3. The description adds value by showing a typical input/output example and specifying the slug comes from 'list_kits', which provides context beyond the 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 loads 'a themed kit and every member product in full.' It uses a specific verb ('load') and resource ('kit and member products'), and distinguishes from siblings like 'get_full_product' and 'list_kits'.
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?
Explicitly instructs to use only after 'list_kits' confirms the slug, and warns against using for a single product by naming the alternative 'get_full_product'. This gives clear when-to-use and when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Load a product's complete persona definition. PREMIUM (paid plan).
Typical input {"slug": "inbox-zero-assistant"} returns {"slug": ..., "persona": ""}.
Returns the persona text alone, with no skill bodies. Use when the caller needs the product's voice and operating rules only. Not when skills are also wanted - get_full_product returns persona and every skill in one call. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "unknown slug ''"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Product slug exactly as returned by search_catalog. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint; the description reinforces this and adds valuable behavioral detail about returning error objects instead of protocol errors for invalid input, which is 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 is concise (4-5 sentences) and well-structured: purpose/cost first, then example, content clarification, usage guidance, and error handling. Every sentence earns its place without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one required parameter and an output schema, the description covers purpose, usage guidance, error behavior, and output content (no skills). No gaps remain; annotations and output schema handle the rest.
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 100% schema coverage, the baseline is 3. The description adds value by providing an example input ('inbox-zero-assistant') and explaining the error response on invalid slugs, which aids correct invocation.
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 loads a product's complete persona definition, provides an example input/output, and explicitly distinguishes from the sibling tool get_full_product by noting that no skill bodies are returned.
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 says when to use (needs voice/operating rules only) and when not to use (if skills are wanted), names the alternative get_full_product, and explains that errors return error objects for safe retry.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Load one product in full: persona plus every skill's instructions. PREMIUM (paid plan).
Typical input {"slug": "inbox-zero-assistant"} returns {"slug": ..., "name": ..., "persona": ..., "skills": [{"name": ..., "instructions": ...}], "free_skill": {...}}.
Returns persona plus every skill for one product. Use when the caller wants the whole product. Not for a single skill (get_full_skill) and not for a bundle of products (get_full_kit). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "unknown slug ''"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Product slug exactly as returned by search_catalog. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds valuable behavioral detail: 'on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ...}' and 'Every call is read-only and idempotent, so after correcting the input it is always safe to retry.' This goes beyond annotations by explaining error handling and retry safety.
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 well-structured: first sentence states purpose, then PREMIUM note, then typical input/output, usage guidance, error handling, and idempotent note. It is front-loaded with the most critical information. While slightly verbose, every sentence adds value without redundancy. Could be trimmed slightly, but it remains clear and organized.
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 and an output schema exists, the description covers the expected return structure ('persona plus every skill' with a typical output example), error handling, and sibling references. It is complete enough for an agent to use effectively. The only minor gap is that the full output schema is not detailed, but the presence of an output schema compensates.
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%, so baseline is 3. The description reinforces the parameter meaning by showing a typical input example: '{"slug": "inbox-zero-assistant"}' and restates that the slug is exactly as returned by search_catalog. However, it does not add new semantic information beyond what the schema already 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?
The description explicitly states 'Load one product in full: persona plus every skill's instructions.' It also provides a typical input/output example and distinguishes from siblings by noting 'Not for a single skill (get_full_skill) and not for a bundle of products (get_full_kit).' This leaves no ambiguity about what the tool does.
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 gives clear guidance: 'Use when the caller wants the whole product.' It also explicitly tells when not to use it by naming alternatives: 'Not for a single skill (get_full_skill) and not for a bundle of products (get_full_kit).' Additionally, it notes the tool is PREMIUM (paid plan), providing context for accessibility.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Load one paid skill's complete instructions from a product. PREMIUM (paid plan).
Typical input {"slug": "inbox-zero-assistant", "skill_name": "Deep Triage"} returns {"slug": ..., "skill": ..., "instructions": ""}.
Returns one named skill, selected by skill_name. Use after preview_product has shown the skill list and the caller wants one specific paid skill. Not for the free gateway skill, which get_free_skill returns with no plan, and not for every skill at once (get_full_product). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "unknown slug ''"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Product slug exactly as returned by search_catalog. | |
| skill_name | Yes | Exact skill name as shown in preview_product's "skills" list for that product. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (readOnlyHint, idempotentHint), the description discloses the error handling behavior: it never raises a protocol error but returns an error object. This adds valuable behavioral context not captured in structured 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 moderately long but well-structured: starts with core purpose, then provides an example, usage notes, and error behavior. Every sentence adds value. A slight trim could make it more concise, but it is still efficient.
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 provides an example return shape and covers error returns. It also explicitly states when to use and what it does. No gaps remain for an agent to effectively invoke the 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 100% with clear descriptions for both parameters. The description adds a example input and repeats that skill_name selects the skill, but does not significantly extend the parameter meaning. 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 loads one paid skill's complete instructions from a product, distinguishes it from get_free_skill and get_full_product, and provides a typical input/output example. This makes the purpose specific and distinct 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?
Explicitly says 'Use after preview_product has shown the skill list and the caller wants one specific paid skill.' It also tells when not to use: 'Not for the free gateway skill... and not for every skill at once.' Names alternative tools (get_free_skill, get_full_product). This is excellent guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List the 12 themed kits with pricing and member products. FREE.
Takes no arguments. Returns a list of kit objects, each {"slug": ..., "name": ..., "price_usd": N, "tagline": ..., "members": ["product-slug", ...], "availability": ...}. Use a kit's slug with get_full_kit (premium). Kits are not sold standalone on any marketplace: price_usd is the bundle's reference value, and All-Access is how a caller actually unlocks one.
Use when the caller asks about bundles or bundle pricing. Not for individual products (search_catalog) and not for a kit's full contents (get_full_kit). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description goes well beyond the annotations by specifying the exact return object shape, stating it takes no arguments, disclosing the error-handling pattern (returns an error object instead of protocol errors), and confirming read-only and idempotent behavior with retry safety. No contradictions 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 front-loaded with the core purpose and then layers additional context (pricing semantics, usage routing, error behavior, and retry safety) in a logically ordered way. Every sentence adds non-redundant value for an agent deciding how to call it.
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 paramterless, read-only list tool with an output schema and rich annotations, the description is complete: it covers return format, error handling, pracing semantics, How All-Access unlocks the bundle, and when touse it. No critical information is missing.
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 tool has zero parameters and the schema is empty with additionalProperties:false, so there is no parameter detail to add. The description still explicitly states 'Takes no arguments,' which is a clarifying confirmation. Basline 4 for 0 params 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 states a specific verb and resource: 'List the 12 themed kits with pricing and member products.' It clearly distinguishes itself from sibling tools by naming search_catalog as the tool for individual products and get_full_kit for full kit contents.
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?
Explicit 'Use when...' guidance is provided ('when the caller asks about bundles or bundle pricing') along with direct exclusions: 'Not for individual products (search_catalog) and not for a kit's full contents (get_full_kit).' It also tells how to proceed after using a slug with get_full_kit, covering follow-up routing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Preview one product: metadata, persona teaser, and paid-skill teasers. FREE.
Typical input {"slug": "inbox-zero-assistant"} returns {"slug": ..., "name": ..., "tagline": ..., "persona_preview": ..., "skills": [{"name": ..., "preview": ...}], "free_skill": ..., "note": ...}.
Returns teasers only, never full text. Use to judge a product before committing. Not for the complete persona or skill bodies (get_full_persona, get_full_product), and not when the caller wants instructions they can act on now (get_free_skill). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "unknown slug ''"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Product slug exactly as returned by search_catalog or recommend_products, e.g. "inbox-zero-assistant". |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds value by stating it returns only teasers (never full text), and details the error handling behavior (no protocol error, returns error object). This goes beyond annotations without contradicting them.
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 concise and well-structured: purpose is front-loaded, followed by example, then exclusions, and error behavior. Every sentence adds essential information 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?
Given the tool's simplicity (single parameter, has output schema, rich annotations), the description is fully complete. It covers input format, output shape, limitations, error handling, retry safety, and when to use versus alternatives.
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 of the 'slug' parameter. The description repeats the source of the slug (search_catalog or recommend_products) and provides an example, but does not add new semantic constraints. Baseline 3 is appropriate since the schema already fully documents the parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool previews one product, providing metadata, persona teaser, and paid-skill teasers. It differentiates from sibling tools by explicitly stating what it does not provide (complete persona/skill bodies from get_full_persona and get_full_product) and what it does (teasers only).
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 gives explicit usage guidance: use to judge a product before committing. It also states when not to use: for complete persona/skill bodies (directing to siblings) or when actionable instructions are needed (get_free_skill). It further advises that errors return an object with a fix suggestion, and that retrying is safe after correction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recommend catalog products for a need described in plain language. FREE.
Typical input {"need": "help answering customer support emails"} returns {"need": ..., "recommendations": [{"slug": ..., "name": ..., "why": ..., "area": ..., "try_free": ""}], "next": "..."}.
Use when the caller describes a problem instead of naming a product; ranking is by fit to the described need. Not for exact keyword or slug lookups (search_catalog). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| need | Yes | The job or problem in plain language, a few words or a sentence, e.g. "help answering customer support emails". | |
| limit | No | Maximum recommendations; values outside 1-10 are clamped. Default 5. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (readOnlyHint, idempotentHint), the description adds: 'Every call is read-only and idempotent, so after correcting the input it is always safe to retry' and describes the error response format, providing behavioral context not in annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is a single paragraph but well-organized: purpose, FREE note, example, usage guidelines, error behavior. Every sentence adds value, though it could be slightly more terse.
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 good annotations, the description covers all necessary context: purpose, usage scope, error handling, and retry safety. No gaps remain for a generic recommendation 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 100%, baseline 3. The description adds a detailed example of input and output, which clarifies the 'need' parameter's purpose and the structure of the response, providing additional context beyond the schema descriptions.
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 'Recommend catalog products for a need described in plain language' with specific verb and resource. Distinguishes from sibling tool 'search_catalog' by mentioning 'not for exact keyword or slug lookups'.
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?
Explicitly says 'Use when the caller describes a problem instead of naming a product' and 'Not for exact keyword or slug lookups (search_catalog)', providing an alternative. Also explains error behavior: 'never raises a protocol error — it returns an error object'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search all 138 catalog products by keyword and/or focus area. FREE.
Typical input {"query": "email inbox", "limit": 5} returns a ranked list of product objects [{"slug": ..., "name": ..., "area": ..., "segment": ..., "tagline": ...}]. An empty query with an area set browses that area.
Use when the caller names a keyword, product, or focus area to look up. Not for bundles (list_kits) and not for plain-language problem statements, which recommend_products ranks by fit rather than keyword match. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| area | No | Optional focus-area filter; one of "productivity", "developertools", "personalassistant", "creative", "industryspecific", "educational". Empty means all areas. | |
| limit | No | Maximum results to return; values outside 1-100 are clamped. Default 15. | |
| query | No | Space-separated keywords matched against name, tagline, slug, and segment, e.g. "email inbox". |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description goes well beyond annotations by stating the tool never raises protocol errors but returns an error object on invalid input, that every call is read-only and idempotent, that it's free, and that browsing is possible with empty query. Annotations already cover readOnlyHint, idempotentHint, and destructiveHint, but the description adds critical edge-case behavior and error handling. 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 compact and front-loaded with key purpose in the first sentence, followed by examples, usage rules, and error behavior. Every sentence adds value with no redundancy. Structure flows logically from purpose to usage to edge cases.
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 3 optional parameters with 100% schema coverage, annotations, output schema, and 9 sibling tools, the description covers purpose, scope, typical input/output, alternative tools, error behavior, safety (read-only/idempotent), and browsing use case. No gaps remain for an agent to safely invoke this 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 description coverage is 100%, so baseline is 3. The description adds value by explaining how parameters work in context (e.g., 'empty query with an area set browses that area', 'values outside 1-100 are clamped'), which clarifies behavior beyond schema types. It doesn't detail all parameter semantics exhaustively, but adds meaningful usage context.
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 all 138 catalog products by keyword and/or focus area, with specific verb 'search' and resource 'catalog products'. It distinguishes itself from siblings by explicitly naming list_kits and recommend_products, making the scope unambiguous.
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 gives explicit guidance on when to use it (when caller names a keyword, product, or focus area) and when not to (for bundles use list_kits, for problem statements use recommend_products). This leaves no ambiguity about alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Explain how to unlock full personas, paid skills, and kits. FREE.
Takes no arguments. Returns {"message": ..., "how": ..., "free_tier": ...} describing the upgrade path and what stays free.
Use when a premium tool has been refused or the caller asks what a plan covers. Not for per-bundle or per-product pricing (list_kits, preview_product). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that the tool takes no arguments, returns a specific JSON structure, and explicitly states it is read-only and idempotent. It also details error behavior: 'it never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"}, and assures that after correcting input it is safe to retry. This adds significant context beyond the annotations (which already mark readOnlyHint, idempotentHint, destructiveHint=false) and fully characterizes the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise, with three short paragraphs covering purpose, usage, and behavior/error handling. It front-loads the core purpose in the first sentence, then adds return structure and usage guidelines without any filler. Every sentence 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 zero parameters, detailed annotations, and an output schema, the description is complete. It explains the purpose, when to use, error handling, and behavioral guarantees. There are no gaps left for the agent to infer. The output schema already documents the return format, so the description need not repeat it in full detail.
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 tool has no parameters (input schema is empty), so the description does not need to add parameter semantics. The baseline is 4 for zero-parameter tools, and the description adds value by confirming that 'Takes no arguments' and specifying the exact return structure, which helps the agent understand the tool's complete interface.
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 explains how to unlock full personas, paid skills, and kits, and that it's FREE. The verb 'explain' combined with specific resources (full personas, paid skills, kits) and scope (upgrade path) distinguishes it from sibling tools like list_kits, preview_product, or get_full_persona which focus on listing or getting details, not explaining the upgrade process.
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 says 'Use when a premium tool has been refused or the caller asks what a plan covers.' It also specifies when not to use it: 'Not for per-bundle or per-product pricing' and names sibling alternatives list_kits, preview_product. This provides clear context and exclusions, making it easy for an agent to decide when to invoke this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
get_free_skillget_full_kitget_full_personaget_full_productget_full_skilllist_kitspreview_productrecommend_productssearch_catalogupgrade_infoClaiming 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, then choose Claim with GitHub. An organization namespace such as io.github.acme/server also needs that organization to have installed the Glama AI GitHub App and approved its permissions, because GitHub discloses organization membership only to apps it has installed. Use HTTP or DNS when it has not.
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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