trailweights-mcp

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

No annotations are provided, so the description carries the full burden. It discloses behavioral traits such as returning a 'trust envelope' with verification status and conflict flags, adding significant context beyond a simple comparison. However, it does not mention auth, rate limits, or idempotency.

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

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

The description is a single sentence that is front-loaded with the core action and output summary. Every word adds value, with no filler or redundancy.

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

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

Given one parameter, no output schema, and no annotations, the description sufficiently explains what the tool returns (rows with specific fields and trust envelope). It does not detail how to interpret the trust envelope or potential errors, but it is complete for typical 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 0%, but the description reinforces the parameter constraint ('for 2–6 products') and implies product IDs. It does not provide format examples or additional semantics like accepted ID types, so it meets the minimum without being rich.

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 ('Side-by-side spec comparison') and resource ('2–6 products'), with specific output fields listed. It distinguishes from siblings like get_product_specs (single product) and find_lighter_alternative (comparative but for lighter options).

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

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

The description implies use when a direct spec comparison of multiple products is needed, but it does not explicitly state when to avoid or mention alternative tools like get_product_specs or find_lighter_alternative. The use case is clear but lacks exclusionary 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?

With no annotations provided, the description carries the full burden. It discloses key behaviors: returns up to 5 results, ordered by verified weight ascending, includes a trust envelope (verified weight provenance, conflict flag, top citations). However, it does not cover error handling or performance characteristics.

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 that conveys purpose, return size, ordering, and data structure with no wasted words.

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

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

Given the tool's simplicity (2 parameters, no output schema), the description covers purpose, return structure, and key details. It lacks error cases but is largely complete for typical 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 0%, so the description must compensate. It explains product_id as the input product and limit as the number of alternatives (implied by 'up to 5'). However, it does not mention the limit's minimum/maximum or that product_id is required, which are already in the schema but not reinforced.

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: 'Given a product, return up to 5 lighter alternatives in the same category'. This specific verb+resource combination distinguishes it from sibling tools like compare_gear, recommend_gear, and 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 implicitly states when to use (when lighter alternatives are needed) but provides no explicit guidance on when not to use or comparison with siblings like get_product_specs or recommend_gear.

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 discloses return fields (consensus label, score, evidence count, distinct authors, known conflicts, evidence sample with URLs) and the deterministic rubric and gate. It is transparent about what the tool does and returns.

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 concise sentences, front-loaded with the core purpose, and no superfluous information. 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 only one parameter, no output schema, and the description detailing the return fields, the description is nearly complete. It could optionally mention the threshold for the source-diversity gate, but overall it is sufficient.

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

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

Schema description coverage is 100% with a single required parameter 'product_id' described as 'Product UUID.' The description adds no further parameter semantics beyond what the schema provides, so a baseline score of 3 is appropriate.

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

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

The description clearly states it provides a pre-computed consensus for a product from independent creators and field experts, scored on a deterministic 100-point rubric with a source-diversity gate. This is specific and distinguishes from raw reviews or other 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 implies usage for conflict-resolution but does not explicitly state when to use this tool versus siblings like get_gear_reviews or compare_gear. No alternatives or exclusions are mentioned.

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?

With no annotations provided, the description carries full burden. It discloses behavioral traits like including a trust envelope (verified weight, provenance, conflict flag, citations). This adds significant transparency, though it doesn't explicitly state it's 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 dense and informative but slightly run-on; it could be split into two sentences for clarity. It front-loads key outputs and includes the trust envelope details without unnecessary words.

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

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

The description fully covers the tool's output fields and trust envelope details, which compensates for the lack of an output schema. It addresses the complexity of the data returned and seems complete for a single-item lookup 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 that either parameter can be used, matching the schema. It provides no additional format or constraints beyond what the schema already offers.

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 returns a canonical product spec sheet with specific fields (name, brand, category, etc.). It distinguishes the tool's output but does not explicitly differentiate from siblings like compare_gear or find_lighter_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 tells users to pass either the product UUID or slug, which is clear parameter usage. However, it provides no guidance on when to use this tool versus alternatives like search_corpus or get_gear_reviews.

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. It thoroughly discloses the internal process (query planner, intent classification, brand-alias expansion, compiled constraints) and the output structure (top catalog products with verified weights, provenance, conflict flags, citations, URLs, interpreted intent, and applied filters). No contradictions.

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

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

The description is efficiently structured: it starts with the core purpose, then details the process and outputs, and ends with optional parameters. Every sentence adds useful information 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?

Given the complexity (4 parameters, no output schema, no annotations), the description provides a thorough explanation of inputs, internal processing, and outputs. It covers what the tool does, how it processes, and what it returns. Some minor gaps exist (e.g., limit parameter not explicitly described), but overall it is complete enough for an agent to select and invoke 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 50% (category and weight_cap_oz have descriptions). The description adds meaning by explaining that query is freeform, category is optional, and weight_cap_oz is 'maximum weight in ounces'. It doesn't explicitly mention limit but implies it controls the number of top products. This adds value beyond the schema.

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

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

The description clearly states it provides 'structured gear recommendations from a freeform natural-language query' and lists specific processing steps (intent classification, brand-alias expansion, etc.). It distinguishes from siblings like compare_gear or find_lighter_alternative by focusing on recommendations from a natural-language query.

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 to use it for natural-language queries about gear and gives an example. It mentions optional filters but does not explicitly say when not to use it or compare to siblings. However, the context of sibling tools implies alternatives exist, so some guidance is present.

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. It discloses the search returns top chunks with source attribution and supports hard predicates. It does not mention pagination or ranking details, but covers the essential behavior for a read operation.

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

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

The description is three sentences, each adding essential information: action/scope, return format, and filter capabilities. No wasted words, and the key purpose is front-loaded.

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

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

Given 7 params and no output schema, the description covers the main functionality but omits match_count parameter and scoring details. However, the high schema coverage compensates, making it adequately complete for 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 coverage is high (86%), so baseline is 3. The description adds value by explaining the 'trust floor' concept for min_creator_count and grouping filters by type, enhancing parameter understanding beyond the schema.

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

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

The description clearly states it performs semantic search over a specific corpus, returns matching chunks with attribution, and supports hybrid queries. It distinguishes from sibling tools by focusing on the corpus search with filter capabilities.

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 hybrid semantic+spec queries are needed, but does not explicitly state when not to use or provide alternatives. It gives clear context for filters reducing need for multiple calls.

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