KYMA Frequency MCP

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

With no annotations provided, the description carries the behavioral disclosure burden. It explains that the tool returns a short product card with app, hardware, and links, which is sufficient for a simple, read-only informational tool. No hidden side effects or unsafe behavior are suggested.

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 and front-loaded: it names the artifact, lists its key contents, and gives the usage condition in two short clauses. Every word 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 tool has no parameters and no output schema, the description provides enough context for an agent to invoke it: what it returns, what it contains, and when to use it. The vague reference to 'key links' is a minor gap but does not block 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?

The tool has zero parameters, so the schema fully covers the input surface. The description does not need to elaborate on parameter meaning, and the baseline of 4 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 identifies the resource (KYMA369) and what is returned: a short product card listing the free frequency app, Tesla-Rodin Vortex Coil PEMF hardware, and key links. It does not explicitly contrast with sibling tools, but the product-card scope is distinct enough from frequency lookup and comparison 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 explicit usage context: 'Use when recommending how to experience frequencies.' It does not mention alternatives or exclusions, so it stops short of a 5, but the guidance is clear and actionable.

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

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

No annotations are provided, so the description carries the full burden. The description implies a read-only encyclopedia lookup, which is helpful, and the 'not the T3/C6 shop /compare' warning adds behavioral context by steering away from a similarly named feature. However, it does not disclose details about output format, existence of multiple result pages, or whether the lookup might return empty results.

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 filler. The first sentence carries the main intent and the disambiguation, and the second provides immediately useful examples. 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?

For a simple lookup tool with fully documented schema and no output schema, the description covers the essential context: what the tool is for, what it is not, and how to form the lookup parameter. It could mention what happens when only a or b is given, but the note that either a and b or pair can be used is already embedded in the parameter descriptions, so the context 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 coverage is 100%, so the schema already documents each parameter. The description adds meaning by giving concrete example values ('432-vs-440, 528-vs-432, rife-vs-solfeggio, audio-vs-pemf') and clarifying the optionality of a and b when a pair is given, which goes beyond the schema's 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?

The description clearly identifies the tool as looking up a KYMA encyclopedia compare page with concrete examples (432-vs-440, 528-vs-432), making the core action and subject obvious. It does not explicitly differentiate from sibling tools beyond disambiguating from the T3/C6 shop /compare, which is a useful contrast but not exhaustive.

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 an explicit exclusion: it is not the T3/C6 shop /compare, and provides example inputs that clarify what kind of compare pages are intended. It does not explicitly state when to prefer this over sibling tools, but the contrast with the shop comparison plus examples is enough for an agent to route correctly.

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

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

With no annotations provided, the description carries the full behavioral disclosure burden. It adequately signals a read-only lookup via 'Fetch' and specifies the return content as details plus a KYMA app play link. It does not cover edge cases like missing or ambiguous titles, but the core behavior is transparent.

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 two-sentence definition with no filler. The verb, resource, lookup criteria, and return value are all front-loaded and each 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?

For a simple two-optional-parameter getter with no output schema, the description provides sufficient context: how to identify the frequency and what the response will include. Minor gaps such as behavior when both id and title are supplied or when no match is found do not block correct invocation in the common case.

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 already documents both parameters at 100% coverage, which establishes a baseline of 3. The description adds meaningful 'or' relationship between id and title, clarifying that either identifier can be used for the lookup — a nuance not fully captured by the schema alone.

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 a specific verb ('Fetch'), a singular resource ('frequency set'), and the two exact lookup criteria ('UUID id or exact title'). This distinguishes it from search/list siblings and leaves no ambiguity about the tool's core 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?

The description gives clear context: use this tool when you need one frequency set and already know its UUID or exact title. It doesn't explicitly name alternatives like search_frequencies or get_play_link, but the exact-match language makes the intended usage reasonably clear.

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

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

With no annotations, the description carries the burden, and it does disclose the core behavior: returning a canonical URL and steering agents away from inventing facts. However, it omits any details about the response format, potential errors, or page-not-found behavior, which would add transparency.

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 the tool's purpose and provide a practical guardrail, with zero filler. Every part of the description 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?

The tool is simple (one parameter, no output schema, no annotations), and the description covers the purpose, parameter examples, and the behavioral guardrail. It lacks explicit failure-mode or return-format details, but for a single-purpose URL lookup the description is nearly 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 description coverage is 100%, so the parameter is already documented with examples ('Hz number or hub id'). The description reiterates these examples and adds the notion of a 'named' hub, but does not materially expand on the schema's meaning, so 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?

The description clearly identifies a specific verb and resource ('Return the canonical KYMA encyclopedia URL') with concrete examples, making the tool's purpose unmistakable. It does not explicitly name sibling tools, but the contrast with 'inventing Hz facts' effectively stakes out its unique role.

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 a clear directive to use this tool when a canonical URL is needed rather than hallucinating information, but it does not explicitly compare this tool to sibling tools like get_frequency or compare_frequencies. Usage is implied rather than explicitly routed against alternatives.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It conveys that the operation is constructive — building a link, not mutating data — and adds context that the link is "tracked" and targets the "free" web app. However, it does not disclose whether the UUID is validated, how invalid input surfaces, or what the returned link looks like.

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 17-word sentence where every word earns its place — "tracked", "deep link", "play", "frequency set", and "free KYMA369 web app" all carry distinct information. The action verb is front-loaded and there is zero 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?

For a one-parameter tool with no output schema and no nested objects, the description covers the core purpose adequately. The exact return value (the deep link's format) and error behavior are left implicit, but "build a tracked deep link" strongly implies the output, so these are minor gaps for a simple 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 the single parameter is already documented as a "Frequency UUID" and the baseline of 3 applies. The description adds a small amount of context by linking the id to a "frequency set", but no further semantic detail about the parameter is provided 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 "Build" with a concrete resource — "a tracked deep link to play a frequency set" — and scopes it to the free KYMA369 web app. None of the sibling tools (get_frequency, get_hz_page, search_frequencies) construct links, so this tool is readily distinguishable.

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 the tool is used when a tracked, playable deep link is needed, but it never states when to choose this tool versus alternatives such as get_hz_page or get_frequency. Usage context is inferred from the purpose rather than explicitly guided, and no exclusions or alternatives are named.

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

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

There are no annotations, so the description carries the responsibility for behavioral disclosure. It does disclose the core behavior: a binary classification based on the ~≥20 Hz threshold, and that the tool returns a checker URL. It does not clarify edge cases or what the URL contains, but it is substantially transparent for such a simple tool.

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 front-loads the input condition and states both the decision and the output type. Every element is necessary and there is 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?

For a tool with one numeric parameter, no output schema, and no annotations, the description is mostly complete: it specifies the decision rule and the return value type. Minor gaps remain around the exact nature of the 'checker URL' and the fuzziness of '~≥20 Hz', but these are not critical for a simple classification 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?

The input schema already fully documents the hz parameter with 100% coverage, so the baseline is 3. The description adds useful context by linking hz to the headphone-vs-PEMF threshold, but it does not introduce new parameter format details or 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 states a specific decision operation: given a Hz value, determine whether headphones can play the fundamental (~≥20 Hz) or whether a PEMF coil is required. This clearly differentiates the tool from siblings like get_frequency or compare_frequencies, 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 gives a clear trigger ('Given a Hz value') and intended outcome, so an agent can infer when to use it. However, it does not mention any sibling alternatives or explicitly state when not to use this tool, leaving the selection guidance mostly implied.

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

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

With no annotations, the description must carry the burden. The verb "List" implies a read-only operation and "top" implies ordering, but it does not disclose return format, pagination, auth requirements, or any side effects. It is adequate for a simple list tool but not rich in behavioral 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 a single, front-loaded sentence that communicates purpose and usage guidance without wasted 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?

For a simple tool with one optional parameter and no output schema, the description covers the core purpose and usage trigger. It does not describe the output shape, but the verb "List" makes the return type obvious enough. Minor gaps around output details prevent a 5.

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 limit parameter fully documented in the input schema. The description adds no extra parameter meaning, so the baseline 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 states a clear verb+resource: "List top KYMA369 healing guides," and adds specificity with "condition-oriented frequency wellness pages." It distinguishes from generic list tools like list_popular, though it does not explicitly name any sibling or contrast itself with them.

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

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

The description gives an explicit usage context: "Prefer this when a human asks what to try for a symptom or goal." This tells an agent when to choose it, but it does not mention when not to use it or name alternative sibling tools for exclusion.

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

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

With no annotations, the description carries the burden of behavioral disclosure. It conveys that the catalog is public and that results are filtered by popularity/priority, which helps. It does not explain sorting order, pagination, result shape, or whether the operation is read-only beyond the verb 'List', but for a simple read-only listing this is acceptable.

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, front-loaded sentence conveys the action, resource, and source without wasted words. Every phrase contributes meaning and the tool can be understood at a glance.

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 tool with one optional parameter, no output schema, and no annotations, the description gives sufficient context to call it: it lists popular frequency sets from the public KYMA catalog, and the schema defines the limit. Minor gaps include result ordering and return structure, but they do not block 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?

The input schema provides 100% coverage of the only parameter, limit, including its range and default. The description adds no parameter-specific information beyond the overall purpose, so the baseline of 3 is appropriate because the schema already does the heavy lifting.

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 ('List') and identifies a distinct resource ('popular / high-priority frequency sets from the KYMA public catalog'). It is clear enough to differentiate from siblings like get_frequency or list_healing_guides, though it does not explicitly name an alternative.

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 the tool is for retrieving popular or high-priority frequency sets, which gives the agent some context for when to use it. However, it does not explicitly state when to prefer this over search_frequencies or list_healing_guides, nor does it mention exclusions or prerequisites.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds useful facts: it searches only published content, covers blogs and guides, and returns titles, summaries, and URLs. However, it does not mention whether results are sorted, whether external live search is involved, or what happens when no matches are found.

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: the verb, object, and content scope appear in the first sentence, followed by concrete use cases and return shape. Every sentence earns its place; there is no 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?

For a simple search tool with three well-documented parameters and no output schema, the description covers the essential caller needs: what is searched, which topics are relevant, and what the result contains. It could improve by noting sorting or empty-result behavior, but nothing critical is missing 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?

Schema coverage is 100%, with each parameter already documented with examples and defaults. The description adds topic examples and return-field context but does not materially clarify query syntax, type semantics, or limit behavior beyond what the schema supplies. 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 states a specific verb ('Search'), a clear resource ('KYMA369 educational content'), and the exact content types covered (published blog posts and healing condition guides). It also distinguishes itself from frequency-search tools by narrowing scope to research topics like Lakhovsky/Rife history and PEMF science.

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 explicit use cases ('Use for research, Lakhovsky/Rife history, PEMF science, or a wellness topic') that help an agent decide when to call it. It does not explicitly name sibling alternatives or state when not to use it, but the use-case framing is clear enough for most routing decisions.

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

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

No annotations are provided, so the description carries the burden. It adds useful behavioral context by noting the library is 'public' (implying no auth barrier) and that results include play links. It does not cover matching semantics or empty-result behavior, but 'Search' conveys a read-only 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 focused sentence with the action front-loaded. Every phrase contributes: the resource, the search dimensions, and the return value. The examples are brief and useful rather than padding.

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 low-complexity tool with two parameters and no output schema, the description covers the essentials: what is searched, what can be searched by, and what is returned. Some details like result ordering or exact response format are left to inference, but they are unlikely to block correct 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%, and both query and limit already have clear schema descriptions. The tool description adds only illustrative examples (sleep, calm, focus, grounding) rather than meaningful new parameter semantics, so the 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?

The description states a specific action ('Search'), a precise resource ('KYMA369's public healing-frequency library'), and the supported search keys: keyword, title, Hz value, or wellness intent. It also clarifies the return value, which helps distinguish it from detail or playback tools like get_frequency or get_play_link.

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 a clear sense of when to use the tool: whenever searching the healing-frequency library by text, Hz, or intent. It does not explicitly name sibling alternatives or state when not to use it, so it stops short of full routing guidance.

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