VendorIndex

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

No annotations exist, so the description carries the full burden; it partially meets that by disclosing the shape of the result (weighted totals, per-dimension scores, health trend), which is valuable given there is no output schema. It does not state that this is a read-only operation, whether there is a cap on how many vendors can be compared, or any cost/latency 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?

Two compact sentences with the operation and scope front-loaded, followed by the return contents and a usage cue. No filler or repetition of the tool name.

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 three-required-parameter read tool with no output schema, the definition covers what it does, its scope, and what comes back, which is enough to call it. The remaining gap is identifier format for the vendors array and any maximum list size, neither of which is addressed.

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 clarifies cardinality by framing the call as "one category + use case" against "several vendors", which pins down that a single category and use case apply across the vendor list. It does not say whether vendors are passed as names, IDs, or slugs, nor what form category/use_case values take, leaving a real gap for all three required parameters.

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?

Names a specific operation (side-by-side comparison of several vendors) scoped to one category and one use case, and enumerates the outputs (weighted totals, per-dimension scores, health trend). It stops short of explicitly distinguishing itself from siblings like recommend or get_category_ranking, which also produce ranked vendor opinions.

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?

"Use for head-to-head questions" gives a usable trigger condition but no exclusions, prerequisites, or named alternatives. An agent must infer from sibling names whether recommend or get_category_ranking is the better fit for a given question.

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 discloses deterministic scoring, inputs (verified pricing, agent-friction, health), the all-use-case behavior, and returned weighted and per-dimension scores. It omits auth, rate limits, and pagination, which are less central for a ranking read but still absent.

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?

Three tight sentences, front-loaded with purpose and optional behavior, and no redundant wording. The parenthetical scoring basis is efficient and useful.

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 two-parameter ranking tool with no output schema or annotations, it describes scoring basis, optional use_case behavior, and return shape. It still lacks guidance on valid category/use_case values or sibling routing, but core invocation is covered.

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%, and the description adds meaning for use_case by stating that omitting it returns every use case and that scoring is per use case. It does not explain valid category/use_case values or formats, so it only partially compensates for the schema gap.

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 and resource: get ranked vendors for a category, with per-use-case scoring. It is distinguishable from compare_vendors and get_vendor by being a category-level ranking, though it does not explicitly name 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?

Explains the use_case omission behavior, which guides parameter use, but gives no explicit when-to-use guidance versus compare_vendors, recommend, or search_vendors, and 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?

With no annotations supplied, the description carries the full behavioral burden. It does useful work by disclosing what the response contains — cited rates with source URL, verified date and confidence, plus friction and health signals — which tells an agent this is an information-rich read. However, it never states that the operation is read-only, how it behaves for an unknown vendor (error vs empty), or whether data freshness varies, so key behavioral traits remain undisclosed.

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 dense sentence with the resource declaration front-loaded and the return payload enumerated compactly in parentheses. Nothing is redundant and no sentence is wasted.

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, so the description must convey return content, and it does so with a concrete field list including provenance metadata. For a simple one-parameter lookup, the main remaining gap is identifier format and miss behavior, which is a minor omission rather than a blocking one.

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% and the single 'vendor' parameter carries no description, so the description is the only source of meaning. It implies the parameter scopes to exactly one vendor but does not say whether the value is a name, slug, or ID, nor whether matching is exact or fuzzy — a meaningful gap for what is likely an exact-lookup call. The implicit 'one vendor' scoping is worth some credit.

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 a specific resource and scope: 'full detail for one vendor,' which is clearly distinct from list/search/compare style siblings like compare_vendors and search_vendors. It enumerates the fields returned (pricing, friction, SDKs, capabilities, portability, health), so an agent knows exactly what it gets. It stops short of naming which sibling to use to discover a vendor first, but the core purpose is unambiguous.

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 only implied: 'full detail for one vendor' contrasts with broader listing/comparison tools, suggesting this is the deep-dive call after a vendor is identified. There is no explicit 'use this when...' clause, no statement about when to prefer compare_vendors or search_vendors, and no mention of prerequisites such as needing a resolved vendor identifier.

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. 'List' implies a read-only operation and the second sentence usefully describes the return payload (use cases plus vendor counts), which matters since there is no output schema, but nothing is said about result stability, ordering, size, or whether the catalog is static.

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, zero waste, front-loaded with the resource and its contents, then the actionable directive. The inline examples are compact and do real work by mapping abstract 'category' to concrete slugs.

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 zero-parameter discovery tool with no output schema, the description covers purpose, payload contents, and call ordering, which is enough to invoke it correctly. Minor gaps remain around result size and whether the catalog is static versus changing over time.

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 zero parameters, so there is no parameter meaning to add; baseline for a no-param tool is 4. The description correctly implies no arguments are needed by framing the call as a discovery step.

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 and resource ('List every vendor category VendorIndex covers') and concretely illustrates the domain with examples (transactional-email, llm-models, embeddings-apis, llm-gateways). It also says what each entry contains (use cases, vendor count), which distinguishes it clearly from siblings like get_category_ranking or search_vendors.

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?

Gives an explicit sequencing directive: 'Call this first to discover valid category and use_case ids,' which tells the agent both when to use it and why (to obtain valid ids for downstream calls). It stops short of naming which sibling consumes those ids, so the routing is strong but not fully spelled out.

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 behavioral burden. It usefully says the output includes top vendors plus the reasoning dimensions behind the pick, but it does not state read-only behavior, data freshness, determinism, or rate limits.

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 are front-loaded with purpose, then usage. Every clause adds either return context or a decision cue, with no redundant wording.

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 recommendation tool with no output schema, the description covers the core answer and reasoning. It still omits limit semantics and sibling-tool routing, leaving gaps given 3 parameters and 0% schema description coverage.

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 should compensate. It gives some meaning to 'category' and 'use_case' through 'specific category + use case,' but does not define acceptable formats or values, and the 'limit' parameter is not addressed at all.

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, 'Recommend,' and names the resource, 'top vendors,' scoped by category and use case. The phrase 'single answer' implies a contrast with comparison or ranking siblings, but it does not explicitly name 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?

It gives a clear usage cue: 'Best when an agent needs a single answer to what should I use for X?' However, it does not name when to prefer compare_vendors, get_category_ranking, or search_vendors instead.

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 supplied, so the description carries the full behavioral burden. It does disclose output shape at a high level ("matches with their categories") and the searchable surface, but says nothing about result limits, ranking/ordering, case sensitivity, or empty-result behavior.

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 front-loaded sentence that names the verb, the searchable fields, examples, and the return shape with no filler. Nothing could be removed without losing 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?

With no output schema and no annotations, the description should cover return behavior more fully than a single clause. It adequately conveys what to pass and roughly what comes back, but omits result count limits, ordering, and whether matching is fuzzy or exact — gaps an agent would hit immediately on 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 description coverage is 0% and the single `query` parameter is undocumented in the schema, so the description meaningfully compensates by defining it as free-text spanning name, id, category, and capability. The concrete examples ('embedding', 'soc2', 'fast_inference') give an agent usable signal about expected query values, though matching semantics (AND/OR, partial matching) remain unstated.

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 and resource ("Find vendors") plus the exact fields searched (name, id, category, capability) and example query terms, which makes it clearly distinguishable from get_vendor or list_categories. It stops short of naming a sibling or stating when an exact-id lookup should be used instead.

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: this is the free-text discovery entry point, as opposed to the exact-lookup siblings. There is no explicit when-to-use/when-not guidance or pointer to alternatives like get_vendor or recommend.

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