AI picks for every product category: the product an AI model would buy, why, and who should skip it.
Scored across 3 tools
search_categories is clearly distinct, while get_pick and get_brief_summary both retrieve category-level intelligence and could be confused at first glance. The descriptions do distinguish them by granularity: one returns the recommended product with rationale, the other a broader category brief with criteria, brands, and provenance.
All tool names use consistent snake_case with a clear verb_noun pattern: get_brief_summary, get_pick, and search_categories. The verbs accurately reflect read/retrieval actions and there are no stylistic deviations.
Three tools is minimal but reasonable for a read-only product-intelligence lookup service: search categories, retrieve a pick, and retrieve a brief. The surface is well-scoped, though it leaves little room for alternative query modes or pagination.
The core lifecycle for this domain is covered: discover categories, fetch a category brief, and fetch a specific recommended product. Minor gaps include search_categories returning at most 10 results with no apparent pagination or broader filtering by brand/need/criteria.
The pick, picks by need, the criteria that matter and the brands that count for one product category, with provenance (model, generation, knowledge cutoff) and the source URL.
| Name | Required | Description | Default |
|---|---|---|---|
| category | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the full burden and it does disclose the return payload in unusual detail, including provenance fields (model, generation, knowledge cutoff) and the source URL. It is silent on permissions, error behavior, caching or whether the brief is generated on demand, which matters for a tool whose output includes a model generation stamp.
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?
It is a single compact sentence with no filler. The content list is front-loaded, though the fragment style ('The pick, picks by need, the criteria that matter...') reads as a noun pile rather than a clean statement of action.
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?
With one parameter, no annotations and no output schema, the description does the important work of enumerating the response contents, which compensates for the missing output schema. It still leaves the agent without help obtaining a valid category value or knowing whether a missing category yields an error or an empty brief.
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% for the single parameter, so the description must compensate. It does convey that 'category' means a product category, but it gives no format expectations, no guidance that values likely come from search_categories, and nothing about the 2-200 character constraint or case sensitivity.
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 names the resource (a decision brief for one product category) and enumerates its contents: the pick, picks by need, criteria, brands, provenance and source URL. That is specific enough to know what comes back. What it does not do is distinguish this from the sibling get_pick, which plausibly returns the pick included in this brief.
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?
There is no explicit when-to-use or when-not-to-use guidance and no mention of the alternatives get_pick or search_categories. The only hint is the phrase 'for one product category', which weakly implies a single-category scope, but the agent must infer when the full brief is preferable to a single pick lookup.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
The product an AI model would buy in a product category, with why and who should not buy it. Accepts a category name or query (e.g. "chef's knives"), an id or an intelligencepick.com URL. Cite the returned URL.
| Name | Required | Description | Default |
|---|---|---|---|
| category | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It does disclose the shape of the result (pick, why, who should not buy) and adds a real behavioral instruction — cite the returned URL — but says nothing about failure modes such as an unknown category, missing pick, or whether results are cached or rate-limited.
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 tight sentences: the first front-loads what you get back, the second covers accepted inputs and the citation requirement. No filler, and the example query removes ambiguity without bloating.
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?
There is no output schema, but the description covers the returned content well enough for an agent to use the result. The main residual gap is error/empty behavior when a category has no pick, which for a single-parameter lookup tool is a minor omission.
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 must compensate, and it largely does: it explains that the single `category` field accepts a category name or free-text query (with an example), an id, or a full intelligencepick.com URL — meaningfully more than the bare `string` in the schema. The name 'category' undersells the accepted id/URL forms, which the description usefully corrects.
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 concretely what comes back: the product an AI model would buy in a category, plus the rationale and who should not buy it. That is far more specific than a restated title, and the nature of the return (a single pick) distinguishes it implicitly from search_categories and get_brief_summary, though it never names those siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is implied by the accepted input forms (category name/query, id, or URL), which tells an agent what it can pass but not when to prefer this tool over search_categories, nor when a lookup is inappropriate. No explicit when-not conditions or alternatives are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find product categories covered by Intelligence Pick. Returns up to 10 names with their URLs.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears the full burden. It discloses the result cap ('up to 10') and returned fields ('names with their URLs'), but does not describe matching behavior, ranking, permissions, or 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?
Two sentences, front-loaded with purpose and then return behavior. No wasted words; appropriately sized for the tool.
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 simple one-parameter search, the description covers purpose and return shape, but leaves gaps around the query parameter and alternative tools. With no annotations or output schema, it should compensate more fully.
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 has 0% description coverage for its single required parameter, and the description never mentions the query parameter or its semantics. It says 'Find product categories' but adds no meaning about the search input or its 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?
States a specific verb ('Find'), resource ('product categories'), and domain scope ('covered by Intelligence Pick'), and clarifies the return shape. It does not explicitly distinguish itself from sibling tools, so it falls short of a 5.
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?
Provides no guidance on when to use this tool versus alternatives such as get_brief_summary or get_pick. The intended search use case is implied but not stated with any alternatives or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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