BeevR

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

Annotations already declare readOnlyHint=true, idempotentHint=true, openWorldHint=false and destructiveHint=false, so the safety profile is fully covered without the description. The description adds that the payload is descriptive company/profile content and includes contact routing, which is modest but real added context.

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 with the subject first and the three content categories packed into one clause. No filler, no restatement of the tool name, nothing that could be cut.

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, read-only informational tool with no output schema, the description adequately signals what the agent will get back. It is complete enough to invoke correctly, with only a minor gap in not indicating depth or format of the returned profile.

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 tool takes zero parameters, so there is no parameter semantics to explain and the baseline of 4 applies. The empty schema matches the description's implication of a parameterless, fetch-everything call.

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 (BeevR) and enumerates the content it returns: identity, AI capabilities, and contact info. It distinguishes itself from data-fetching siblings like get_article or get_pricing by being an orientation/about tool, though it never explicitly contrasts itself with 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?

Usage is implied by the content list – an agent can infer this is for general background questions about the vendor and for finding contact channels. However, there is no explicit 'use this when' trigger and no stated alternative or exclusion, so guidance remains inferential.

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?

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds the Markdown return format, which is beyond the annotations, but says nothing about behavior on an unknown slug or locale fallback.

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 sentence with the action, resource, output format, key parameter and slug source, all front-loaded with zero filler.

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, the description correctly supplies the return format (Markdown), and both parameters are accounted for between description and schema. Only edge-case behavior (invalid slug, locale fallback) is unstated.

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 only 50%: locale is fully documented in the schema (enum plus default), but slug has no schema description. The description compensates by defining slug as a BeevR article identifier and naming the tools that produce it, covering the 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 (Get), a precisely scoped resource (one BeevR blog article), the return format (Markdown), and the keying parameter (slug). It clearly distinguishes itself from list_articles and search_beevr, which it names as the sources of the slug.

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 the upstream workflow explicitly — obtain the slug from list_articles or search_beevr before calling this. It does not state when not to use it or what to do on a missing slug, but the routing context for a single-item fetch is clear.

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?

Annotations already declare readOnlyHint=true, idempotentHint=true and destructiveHint=false, so the safety profile is covered. The description adds non-obvious value beyond that by disclosing the return format (Markdown) for a single full write-up, which matters since no output schema exists.

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 tight sentence with no filler. The essential information—what is returned, in what format, and how it is keyed—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?

For a two-parameter read tool with an enum already documented in the schema and no output schema, the description covers purpose, format, and the slug's provenance well. Only the default-locale behavior and edge cases (e.g., unknown slug) are left unstated, which is minor.

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%: locale is documented in the schema, but slug has no description. The description compensates by explaining that the slug is the identifier and where to obtain it (from list_case_studies), which is real semantic value beyond the bare string type.

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 a specific verb (Get), a specific resource (the full write-up of one BeevR case study), the output format (Markdown), and the lookup key (slug). It clearly distinguishes itself from the sibling list_case_studies by returning a single full document rather than a list.

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 clear context for when to use this tool and explicitly points to list_case_studies as the source of the slug, establishing the correct call order. It does not state exclusions or when-not-to-use cases, but for a simple lookup the context is sufficient.

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?

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false and openWorldHint=false, so the safety and determinism profile is fully covered structurally. The description adds the content scope of the response (price, duration, inclusions), which is mildly useful, but discloses no further behavioral traits such as caching, currency, or whether packages can change.

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 carries the resource, the concrete package names, and the returned fields with no filler. Nothing needs trimming and nothing is buried.

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-argument, read-only lookup with no output schema, the description covers what the agent needs to decide and call it, including the shape of the return. It stops just short of completeness by not noting the data is static/fixed-price or whether pricing may be region- or currency-dependent.

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 tool takes zero parameters, so there are no parameter semantics to explain; the baseline for a 0-param tool is 4. The description correctly implies no inputs are needed to retrieve the package list.

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 ('Get') and resource ('BeevR fixed-price packages'), and enumerates the concrete offerings (Pitch Demo, Investor MVP, Flagship Sprint) plus the returned attributes (price, duration, inclusions). This lets an agent distinguish it from list_services or about_beevr without opening any schema.

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 purpose implies when to use it (when a user asks about pricing/packages), but there is no explicit when-to-use framing, no exclusions, and no mention of alternatives such as list_services. Usage is inferable rather than 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false and openWorldHint=false, so safety behavior is fully covered without the description. The description adds the useful ordering trait ('newest first') and confirms filtering is optional, but says nothing about pagination or truncation behavior when limit is omitted.

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 compact sentence that front-loads the verb and resource before the optional filter clause. No wasted words or redundant restatement of the title.

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 no-required-parameter read-only list tool with no output schema, the description plus annotations cover what an agent needs. The main gap is behavior around the undocumented limit parameter and whether results are paginated.

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 only 33% (only locale is described in the schema), so the description must carry more weight. It adds meaning for query ('filtered by a keyword query'), but 'limit' is never mentioned and there is no hint about its 1-50 range or default, leaving one of three parameters unexplained in both places.

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?

Gives a specific verb ('List') and resource ('BeevR blog articles') with a scope note ('newest first'), which is enough to distinguish it from get_article (singular) and list_case_studies (different resource). It stops short of explicitly contrasting itself with siblings like search_beevr, so it is clear but not maximally differentiating.

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 phrase 'optionally filtered by a keyword query' implies when the query parameter is relevant, but there is no guidance on when to prefer this tool over search_beevr or get_article, and no exclusions or prerequisites are 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is fully covered. The description adds content scope (results and stack per case study) but says nothing about pagination, result limits, or ordering.

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 the verb and resource, with the domain list as useful scoping detail. 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?

For a read-only, zero-required-param list tool with full annotation coverage and no output schema, the description gives enough to invoke correctly. It stops short of noting whether the list is exhaustive or truncated, which is the only real gap.

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% and the single locale enum is fully documented in the schema, so the baseline of 3 applies. The description adds no extra locale or formatting semantics beyond what the schema states.

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 (List) and resource (case studies / production systems BeevR has shipped) and enumerates the domains covered, so an agent knows exactly what comes back. It is clearly distinguishable from the singular sibling get_case_study.

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 plural 'List' verb implies this is the browse-all entry point versus get_case_study for one item, but the description never names that alternative or states when to prefer it. Usage is inferable from the name/siblings rather than explicit.

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?

Annotations already declare readOnlyHint=true, idempotentHint=true and destructiveHint=false, so the safety profile is covered. The description adds that results include summaries and URLs, which is useful output context, but says nothing about result size, ordering, or pagination.

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 with the verb and resource front-loaded. The long parenthetical category list is somewhat expendable for tool selection but does convey scope, and there is no filler prose.

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-required-parameter, read-only listing tool with full schema coverage and no output schema, the description adequately says what is returned (summaries and URLs). Only the lack of guidance on result scope or ordering keeps it from being 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 100% and the single locale parameter carries its own description with enum values and default, so the schema does the work. The description adds no parameter meaning beyond that, which is the expected baseline.

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 (List) and resource (BeevR services) and enumerates the service categories, so the agent knows what comes back. It is clearly a catalog-listing tool distinct from get_pricing or list_articles, though it never explicitly contrasts itself with siblings like about_beevr or search_beevr.

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 when-to-use or when-not-to-use guidance and no mention of alternatives. The agent must infer that this is the entry point for discovering service offerings, and nothing tells it how to choose this over search_beevr or about_beevr.

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?

Annotations already carry the safety profile (readOnly, idempotent, non-destructive, closed-world), so the bar is lower. The description adds genuine value by disclosing the corpus scope and the return shape ('ranked matches with URLs'), which the agent cannot read off the annotations.

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, front-loaded with the action and resource, with the topical examples parenthetically packed in rather than given their own sentences. Nothing 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?

With no output schema, the description usefully states the return shape (ranked matches with URLs) and the searchable corpus. It stops short of covering pagination or how 'limit' bounds results, which is the one gap for a 3-parameter search 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 67% and the description mentions no parameters at all, so it adds no meaning beyond the schema. The undocumented 'limit' (1-25) and the locale switch remain unexplained here, leaving this at the baseline for partial coverage.

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 (Search) and resource (BeevR services, case studies, articles) and even enumerates the topical corpus, which tells an agent what content is reachable. It is clearly distinguishable from the get_*/list_* siblings by the ranked-search framing, though it never names an alternative to route against.

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: an agent can infer this is for query-driven retrieval versus the list_* tools for enumeration, but the description never states when to prefer search over list_articles/list_case_studies/list_services or when it is the wrong tool.

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