present-of-ai

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 fully covered elsewhere. The description adds the facet set and that place accepts 'Remote', but says nothing about result shape, pagination beyond the schema's cursor, 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?

A single front-loaded sentence naming the resource first, then the search dimensions. No filler, no repetition of schema or annotation content.

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 should hint at what a result contains, but it only says 'open roles' without describing fields (title, company, location, link). Pagination and limits are covered by the schema, so the remaining gap is the return shape.

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 already high (83%), so a 3 is the baseline. The description goes beyond it by supplying example function values (Engineering, Product, Sales, AI & Data) where the schema offers only one example and no enum, and by clarifying that place accepts a city, country, or the literal 'Remote'.

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 resource (open roles at AI companies) and enumerates the searchable facets, so an agent can tell it apart from search_ai_companies or get_company. It lacks an explicit contrast with those siblings, but the resource noun 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 implied by 'searchable by keyword, company, function and place' — an agent can infer this is the tool for locating job listings. However, no when-to-use/when-not guidance or alternatives are named, so an agent must infer the routing itself.

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. The description adds 'public profile' and lists returned sections, but does not disclose auth needs, rate limits, or error behavior beyond what the annotations cover.

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 no wasted words. The purpose is immediately clear and the content list is compact.

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?

No output schema exists, so the description carries the return-value burden, and it does so by enumerating description, founders, funding facts, open roles, recent news, and source links. For a read-only single-record tool with rich annotations and full schema coverage, this is complete enough.

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%, so the slug parameter is already well documented with an example. The description adds meaningful provenance by stating the slug comes from search_ai_companies, which goes 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?

States a specific verb and resource ('Full public profile of one company') and then enumerates the profile's contents. It also names search_ai_companies as the source of the slug, distinguishing this retrieval tool from the search sibling.

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 parenthetical '(from search_ai_companies)' gives a clear prerequisite and usage context: obtain a slug from the search tool first. It does not explicitly state when not to use it or name alternative tools for news or jobs-only queries.

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, openWorldHint=false, and destructiveHint=false, so the safety profile is fully covered without the description. The description adds ordering (newest first) and item shape (links to primary source and their article), which is useful but modest; it says nothing about rate limits, freshness windows, or how pagination terminates.

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 tightly packed sentences with no filler. The defining characteristics (dated, source-linked, newest first) and the filter option are front-loaded before the return-value detail.

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 carries the return-value burden and does so adequately: dated items, newest first, each with source and article links. It omits pagination semantics and total-count behavior, but for a simple 3-param read tool this is close to 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 coverage is 67%: limit and cursor are documented in-schema, but query has no description. The description compensates by explaining that query filters by keyword or company, which is the missing semantic for that parameter. It does not add detail on matching behavior (exact vs. fuzzy) or cursor interaction.

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 the resource precisely (dated, source-linked AI news events), the ordering (newest first), and the optional filtering axis (keyword or company). This clearly separates it from siblings like find_ai_jobs, get_company, or whats_trending. It stops short of an explicit retrieval verb, but the resource and scope leave no ambiguity.

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: call it when you want AI news, optionally narrowed by keyword or company. There is no explicit when-not guidance and no alternative sibling is named (e.g., nothing pointing to whats_trending for trending topics), so the agent must infer the boundary itself.

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, openWorldHint=false and destructiveHint=false, so the safety profile is covered. The description adds that results carry company counts and that this is a lookup step, but says nothing about list size, pagination, or completeness of the enumeration – modest added value over 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 short sentences: the first states what is returned, the second states when to call it. Zero filler and the output content 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 parameterless-in-practice, read-only lookup with no output schema, the description covers purpose, return content, and usage trigger. The only real gap is the undocumented `kind` filter, which is minor but not zero.

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 description never mentions the single `kind` parameter, so the agent must infer from the enum (both/sectors/countries) that it can narrow the response to one axis. With no schema documentation, the description should have compensated here and does not.

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 (sectors and countries) and states what the call returns (company counts), plus its role as a filter-value reference. It implies a list verb clearly enough to distinguish from find_ai_jobs/search_ai_companies, though it never explicitly contrasts itself with 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?

"Call this first if unsure how a sector or country is spelled" gives an explicit triggering condition and ordering advice. It does not name which sibling consumes these filter values, so the routing guidance stops just short of a 5.

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 and idempotentHint=true, so the safe, repeatable read behavior is covered. The description adds what fields are returned (name, one-liner, sector, etc.), which is helpful, but doesn't mention pagination behavior beyond the cursor schema or rate limits. With annotations covering safety, a 3 is appropriate as the description adds moderate value.

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 that are front-loaded with the core purpose and then an example. The example adds practical value. Minor verbosity in enumerating filters that are already in the schema, but otherwise efficient.

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 complexity (8 parameters, no output schema) and the presence of annotations and high schema coverage, the description covers the essential purpose and provides a concrete example. It doesn't discuss pagination (though cursor schema hints at it) or default limit, but these are documented in the schema. Overall complete enough for an agent to use 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 description coverage is 88%, so the schema already documents most parameters thoroughly. The description's mention of filter dimensions (topic, sector, country, funding stage, founding year, hiring) maps to query, sector, country, stage, founded, and hiring_only, but adds no syntax or format details beyond what the schema provides. Baseline 3 is correct when the schema does the heavy lifting.

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'') and resource (''AI startups and companies'') and enumerates filter dimensions (topic, sector, country, funding stage, founding year, hiring). This clearly distinguishes it from siblings like find_ai_jobs (jobs) and get_company (single lookup).

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 an example query ('clinical documentation', country 'Australia') which implies usage context, but doesn't explicitly state when to use this tool versus get_company or list_sectors_countries. The sector parameter references list_sectors_countries, which is a useful cross-reference, but overall guidance is implicit 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, destructiveHint=false, and openWorldHint=false, so the safety profile is fully covered. The description adds useful return-content context (example companies, latest news, hiring companies), which matters since there is no output schema, but it says nothing about freshness window, result limits, or update cadence.

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 primary payload (trending topics) front-loaded and the secondary payload (top hiring companies) trailing. No filler, no repetition 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 zero-parameter, read-only aggregate with no output schema, the description covers both halves of the return content and the annotations cover safety. The only gap is the lack of any guidance on recency or result scope, which an agent might reasonably need.

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 the baseline is 4. There is nothing for the description to clarify or compensate for, and it introduces no phantom parameter expectations.

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 two concrete deliverables: trending AI topics (with example companies and latest news) and the companies hiring the most. That is a specific resource statement an agent can act on. It stops short of naming the overlapping siblings (latest_ai_news, find_ai_jobs) it must be chosen over.

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 guidance at all. The tool clearly overlaps with siblings latest_ai_news (news) and find_ai_jobs (hiring), yet the description gives no condition for picking this aggregate over either of them, leaving the agent to guess.

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