trailweights-mcp

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

No annotations provided, so description carries full burden. It states 'returns' implying a read operation but does not explicitly confirm non-destructive behavior, absence of side effects, or authentication requirements. No mention of rate limits or error handling.

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

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

Two sentences, each essential: one states purpose and scope, the other lists output. 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?

Sufficient for a simple tool with one parameter and no output schema. Covers input constraints (2–6 products) and output fields. Minor gaps like ordering or handling invalid IDs, but adequate.

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

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

Schema description coverage is 0%, but description adds context by explaining the purpose of product_ids and the output fields. However, it does not specify the format of product IDs (e.g., numeric, string) or how to obtain them.

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

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

The description clearly states the verb 'compare', the resource 'products', and the scope '2–6 products'. It distinguishes from siblings like 'get_product_specs' (single product) and 'find_lighter_alternative' (weight-focused). Explicitly lists output fields.

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

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

No explicit when-to-use or when-not-to-use guidance. While implied for side-by-side comparison, it does not mention alternatives like 'get_product_specs' for single product details or 'recommend_gear' for recommendations.

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 exist, so the description carries the burden of behavioral disclosure. It correctly states the ordering (by verified weight ascending) and default/ max limit, but fails to mention any constraints like authentication, rate limits, or what happens if no alternatives exist. It does not contradict annotations because there are none.

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 with no redundant words. It front-loads the purpose and critical details (max 5, ordered ascending). Every element is necessary and informative.

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 low complexity (2 params, no nested objects, no output schema), the description is mostly adequate but lacks details on error handling, verification source for weight, and edge cases like no results. Additional context would improve an agent's 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 description adds context for product_id ('given a product') but does not specify required format or source. The limit parameter is explained in the schema with default and bounds, but the description adds nothing beyond that. With 0% schema description coverage, the description should compensate more, but the tool is simple enough.

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 up to 5 lighter alternatives in the same category, ordered by verified weight ascending. This provides a specific verb ('find'), resource ('lighter alternative'), and result format, distinguishing it from sibling tools like compare_gear or search_corpus.

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

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

The description implies usage when a user needs lighter alternatives for a given product in the same category, but it does not explicitly state when not to use this tool or mention alternative tools for similar purposes. Sibling tool names like compare_gear or recommend_gear suggest related functionality, but no comparisons are provided.

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 description carries full burden. It discloses return data (up to 10 mentions with fields) but does not mention limitations like rate limits, authentication, or error handling. Moderate 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?

One sentence, front-loaded with purpose, no redundancy. 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 tool with one required parameter and no output schema, the description is mostly complete. It explains what it does and what it returns. Lacks info on typical usage context or error handling, but adequate.

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

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

Schema description coverage is 100% for product_id, which is well-described in the schema. The description adds no additional meaning beyond the schema, 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 states it fetches creator video reviews for a product, with specific verb 'Fetch' and resource 'creator video reviews'. This distinguishes it from sibling tools like compare_gear, find_lighter_alternative, etc., which have 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 implies usage when video reviews are needed, but lacks explicit when-to-use or when-not-to-use guidance, and no mention of alternatives among siblings. It provides clear context but no exclusions.

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

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

No annotations are provided, so the description must fully disclose behavior. It mentions what is returned but fails to clarify what happens when both parameters are provided, or when neither is provided. It does not state that the tool is read-only or any error conditions.

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 verb 'Return' and includes all essential information without waste. 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 retrieval tool with no output schema, the description covers the core aspects: what it returns, how to identify templates, and the scope. It lacks potential error handling details but is fairly complete given the tool's simplicity.

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

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

Schema coverage is 100% with both parameters documented. The description adds 'by ID or slug' but does not clarify precedence or formatting beyond schema. Baseline of 3 is appropriate as it adds marginal value.

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 a pack template by ID or slug, including full item list and total weight. The scope is specified as one of 21 in-house templates, which distinguishes it from sibling tools like compare_gear or recommend_gear.

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 implicitly indicates use when retrieving a specific pack template, but does not explicitly exclude alternatives or mention when-not-to-use. It provides clear context but lacks comparative guidance.

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 must cover behavior. It lists returned fields but does not disclose any behavioral traits such as read-only nature, authorization needs, rate limits, or side effects. The description lacks detail on what happens if both parameters are provided or if neither is provided.

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

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

The description is a single, well-structured sentence that front-loads the purpose ('Canonical product spec sheet') and packs information efficiently without fluff. Every word earns its place.

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

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

Despite no output schema, the description lists the key returned fields (name, brand, category, etc.), providing reasonable completeness for a simple retrieval tool. The context signals (low complexity, zero nested objects) make the description adequate, though it could briefly mention error cases or data freshness.

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?

Both parameters have schema descriptions (100% coverage), and the description adds value by clarifying that either UUID or slug can be passed, implying mutual exclusivity and optionality. This goes beyond the schema's 'optional' flag.

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 a canonical product spec sheet with specific fields like name, brand, category, etc. It distinguishes this tool from siblings like 'search_corpus' or 'recommend_gear' by implying it is for retrieving detailed specs, but does not explicitly differentiate.

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

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

The description provides explicit instructions for invocation: 'Pass either the product UUID or the slug.' However, it does not specify when to use this tool versus alternatives like 'compare_gear' or 'get_gear_reviews', nor does it mention any prerequisites or constraints.

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 fully disclose behavior. It reveals that the tool returns top catalog products with verified weights and cottage-first buy URLs, and mentions optional filters. However, it omits details like authentication needs, rate limits, ordering/ranking criteria, error handling, or side effects. The description is adequate but not comprehensive.

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 at two sentences, with the primary purpose in the first sentence and additional details in the second. Every word adds value, and there is no redundancy or fluff. It is well-structured and easy to parse.

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 absence of output schema and annotations, the description should provide more context about the return format, ranking logic, and potential errors. It states the output includes top products with weights and URLs, but lacks details on how results are ordered, pagination, or structure. It is adequate for basic understanding but not 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 description coverage is 50% (category and weight_cap_oz have descriptions). The description adds value by reiterating these optional filters and clarifying that query is a freeform search. However, it does not explain the `limit` parameter or add new meaning beyond the schema for the covered parameters. The description partially compensates for incomplete schema metadata.

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's purpose: providing structured gear recommendations based on a freeform query. It specifies the return value (top catalog products with verified weights and cottage-first buy URLs) and distinguishes itself from siblings like compare_gear and find_lighter_alternative by focusing on recommendations rather than comparison or weight reduction.

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

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

The description offers no explicit guidance on when to use this tool versus its siblings (e.g., compare_gear, find_lighter_alternative). While it implies usage for gear recommendations, it does not clarify when to prefer this over other tools or when not to use it, leaving the agent without context for tool selection.

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 bears the full burden. It notes that results are 'top matching chunks with source attribution', indicating a read-only operation, but does not mention side effects, authentication needs, or limitations. This is adequate but not rich.

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, front-loading the core action and resource, with no extraneous words. 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 medium complexity (3 params, no output schema), the description covers the basic purpose and return type but does not detail the output structure (e.g., fields in each chunk). It is functionally adequate but leaves some ambiguity about what 'chunks' contain.

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 67% (2 of 3 params have descriptions). The description adds context about the corpus but does not explain individual parameters beyond what the schema provides. The match_count parameter, lacking a schema description, is also not elaborated in the 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 'Semantic search' as the verb over the 'TrailWeights knowledge corpus', listing specific content types. This distinguishes it from sibling tools like compare_gear or recommend_gear, which are different 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 explains the scope of the corpus but does not explicitly state when to use this tool versus others, such as when to search versus using get_product_specs or recommend_gear. The context is implied but no exclusions or alternatives are provided.

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