trailweights-mcp

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

With no annotations, the description carries the full burden. It discloses the return structure: aligned rows of name, weight, price, category, buy URL, and a trust envelope with verified provenance, conflict flag, and citations. This is good behavioral detail, though it omits whether the tool is read-only or has any side effects.

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

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

The description is two sentences with no wasted words. It front-loads the purpose and quickly lists the return fields, making it efficient for an agent 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 tool has only one parameter and no output schema, the description covers the input constraints and the core output structure. It could mention the format of the return (e.g., JSON array or table) but is sufficiently complete for a straightforward comparison tool.

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

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

Schema coverage is 0%, so the description fully compensates. It explains the single parameter product_ids: it accepts 2–6 items for side-by-side comparison, which adds meaning beyond the schema's minItems/maxItems 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 clearly states the tool does side-by-side spec comparison for 2–6 products, listing specific fields returned. This is a specific verb+resource that distinguishes it from siblings like get_product_specs (single product) or find_lighter_alternative (alternative search).

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

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

The description explicitly states it takes 2–6 products, providing clear context on when to use. However, it does not explicitly mention when not to use it or name alternative tools for different scenarios.

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 full burden. It discloses key behaviors: returns up to 5 results, ordering by verified weight, and includes a trust envelope (provenance, conflict flag, citations). However, it does not mention whether the operation is read-only or any authentication needs.

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 serving a distinct purpose: the first describes core functionality and ordering, the second details the trust envelope. No redundant or 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?

Given no output schema, the description adequately explains the return structure (trust envelope). The tool's role among siblings is clear. However, missing parameter details slightly reduce completeness for a simple 2-parameter tool.

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

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

Schema coverage is 0%—parameters have no descriptions. The tool description mentions 'up to 5 lighter alternatives' but does not explain that 'limit' can be customized (schema allows 1-10). 'product_id' is implicitly clear but not explicitly described.

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 ('return') and resource ('lighter alternatives') with clear constraints (same category, ordered by weight ascending). It distinguishes this tool from siblings like 'compare_gear' and 'recommend_gear' by focusing on lightweight alternatives.

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 seeking lighter versions of a product, but does not explicitly state when to avoid this tool or mention alternatives. The phrase 'Given a product' provides context, but no exclusions or comparisons to siblings are included.

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 the deterministic nature, the 100-point rubric, the source-diversity gate, and the exact returned fields, which is valuable transparency. It does not mention errors, authentication, or handling of missing products, but for a read-only lookup it is quite complete.

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-loads the main purpose, and every clause adds value—return values, rubric details, and parameter usage. There is no waste 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 there is no output schema and no annotations, the description thoroughly explains what the agent will receive: consensus label, score, evidence count, distinct authors, conflicts, and sample evidence with URLs. It also notes the source-diversity gate. This is sufficient for an agent to correctly interpret the tool's result.

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?

Although the schema already describes each parameter, the description adds the key semantic that either the product UUID or slug can be passed, implying they are interchangeable alternatives. This goes beyond the individual field descriptions and clarifies the intended usage.

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

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

The description clearly states the tool computes field consensus for a product using a deterministic rubric, listing specific return values (label, score, evidence count, etc.). It distinguishes itself from raw review retrieval by offering pre-computed conflict resolution, which separates it from sibling tools like get_gear_reviews.

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 strong context: it is for conflict-resolution an agent would otherwise have to reconstruct from raw reviews, implying use when needing pre-computed consensus. However, it does not explicitly name alternative tools or state when not to use it, so it falls short of the most explicit 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, the description must convey behavior. It mentions returning up to 10 verified mentions and specifies the returned fields. However, it does not disclose any potential side effects, authentication needs, or rate limits, which is acceptable for a read-only fetch but leaves some gaps.

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 long, front-loaded with the main action and resource, and every word adds value. 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 the tool has a single required parameter and no output schema, the description adequately covers what is returned (creator name, YouTube ID, etc.) and limits. Minor omissions like pagination or empty results are acceptable for this 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 coverage is 100% with product_id described as 'TrailWeights product UUID.' The description adds context about the return format but does not enhance the parameter meaning beyond the schema. 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 action 'Fetch' and the resource 'creator video reviews for a product,' making the tool's purpose unambiguous. It is distinct from sibling tools like get_product_specs or search_corpus, which focus on other aspects.

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 guidance is provided on when to use this tool versus alternatives such as get_product_specs or search_corpus. There is no mention of prerequisites or scenarios where this tool is preferred.

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 convey behavioral traits. It describes a read operation returning data, but lacks details on authentication, rate limits, or any potential side effects. The description does not contradict annotations but offers minimal transparency beyond the obvious.

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 with no fluff. Every word adds necessary context, making it efficient for an AI agent 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?

For a simple tool with 2 optional parameters and no output schema, the description adequately conveys the return data (item list and weight). However, some structured details about the return format could further enhance completeness.

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 covers both parameters with descriptions, achieving 100% coverage. The description adds value by clarifying that either slug or template_id can be used to retrieve the template, and it specifies the return includes the item list and weight, which is not in 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 the verb 'Return', the resource 'pack templates', and specifies the scope ('21 in-house TrailWeights'), identifiers (ID or slug), and included data ('full item list and total weight'). This distinguishes it from sibling tools like compare_gear or get_product_specs.

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 retrieving a specific pack template but provides no explicit guidance on when to use this tool versus alternatives like find_lighter_alternative or recommend_gear. No exclusion criteria or prerequsites 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?

With no annotations provided, the description carries the full burden. It details the output but does not disclose whether the tool is read-only, required permissions, error handling, or rate limits. The 'trust envelope' description adds some behavioral context but lacks essential safety and side-effect information.

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

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

Two sentences: the first lists output fields, the second adds trust envelope details and usage. Front-loaded with key information, no unnecessary words. Excellent conciseness.

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?

Without an output schema, the description thoroughly covers the return data (fields, trust envelope, provenance, citations). It is complete for a product spec tool, though missing error cases or conditions like missing products.

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 covers both parameters (slug and product_id) with descriptions. The tool description adds value by clarifying that only one parameter is needed ('Pass either'), which is not explicit in the schema. This helps the agent choose correctly.

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 a detailed list of fields (name, brand, category, verified weight, MSRP, image, affiliate URL, trust envelope). It is specific and distinguishes from siblings like get_gear_reviews or compare_gear by focusing on core specs.

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 that you can pass either a product UUID or slug, but provides no explicit guidance on when to use this tool versus alternatives like compare_gear or find_lighter_alternative. Usage context is implied but not detailed.

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 full burden. It richly discloses behavior: deterministic, fast (<500ms), rule-based planner, no LLM invocation, and details the return contents (verified weights, provenance, conflict flags, trust envelopes, citations, cottage-first URLs, applied filters). This is exemplary 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?

The description is information-dense but not bloated. Two sentences front-load key aspects (determinism, latency, planner) and each clause adds value—no filler. Slightly long, but every part 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 covers return values well, including verified weights, conflict flags, trust envelopes, citations, and buy URLs. It omits limit semantics and potential error conditions, but for a recommendation tool it is largely 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 50% (category and weight_cap_oz are documented, query and limit are not). The description compensates by explaining query as 'freeform query' with an example, and explicitly mentioning optional weight cap and category filter. It does not explain limit behavior, but the ranking context helps.

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 defines a deterministic gear lookup tool with a specific verb ('lookup'), resource ('gear'), and scope ('freeform query'). It distinguishes itself from sibling tools by emphasizing the rule-based deterministic planner and explicitly noting that no server-side LLM is involved, making its niche clear.

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?

Usage is implied through examples like 'best quilt for a PCT thru hike' and the phrase 'structured gear lookup', but there is no explicit statement of when not to use it or how it compares to alternatives like search_corpus or compare_gear. The boundary is inferable but not stated.

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 provided, but description discloses return of 'top matching chunks with source attribution' and filtering behavior. Does not discuss pagination or side effects, which are minimal for a search 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?

Two sentences with front-loaded purpose and detailed capabilities; 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?

Given no output schema and 7 parameters, description adequately covers key aspects, though could mention match_count default/max explicitly (though in schema).

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 86%; description adds semantic context like 'trust floor' and 'verified weight', making filter purposes clear beyond 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?

The description states 'Semantic search over the TrailWeights knowledge corpus' with clear verb and resource, and distinguishes from siblings like compare_gear by highlighting hybrid semantic+spec queries.

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 implicitly guides when to use via 'hybrid semantic+spec queries resolve in one call', but lacks explicit when-not-to-use or direct alternative references.

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