Venture Atlas

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

No annotations are provided, so the description must carry behavioral information. It mentions that every field carries source URLs, which is useful, but it omits other traits like read-only nature, auth requirements, 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 sentences, front-loaded with the core purpose, then a list of content, and a final note about source URLs. Every word adds value, no 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 no output schema, the description lists many returned fields, giving good context. Minor gaps: no mention of response structure or error handling, but the enumeration is thorough.

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 descriptions for both parameters. The description adds value by telling users to use 'search_companies' for the slug and noting that includeOverview is 'Large; off by default', which goes beyond schema definitions.

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 specifies 'Full Venture Atlas profile for one company' and enumerates all included components (summary, metrics, milestones, etc.). It implicitly distinguishes from siblings like 'search_companies' (search) and 'latest_news' (news only).

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 use for retrieving a comprehensive profile but does not explicitly state when to use this vs siblings like 'list_upcoming_milestones' or 'latest_news'. No when-not or alternative guidance is provided.

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 behavioral burden and does add useful context by naming three data series and noting that chip counts appear on a separate series. However, it does not disclose whether operator/metric are optional, what the default returned record looks like, or any response-format details beyond the series names.

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 compact: two sentences with no filler. The core subject ('Comparable AI data-center capacity') is front-loaded, and every clause contributes either a metric detail or a filtering instruction. It avoids repeating schema examples and stays focused.

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 only two optional parameters and no output schema, the description supplies the essential context: what data is returned (power, H100-equivalents, chips) and the operator filter. The schema fills in the remaining parameter semantics, such as the 'full operator record is always returned' behavior, so an agent can select and invoke the tool without major gaps.

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% and both parameters already have descriptions, so the baseline is 3. The tool description adds genuine meaning by mapping the metric enum values to concrete concepts (power→MW, compute→H100-equivalents, chips→raw chip counts) and explaining the filtering behavior via operator slug, going beyond the schema's generic parameter 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 clearly identifies the resource as AI data-center capacity and enumerates concrete metrics (IT power in MW, H100-equivalents, raw chip counts), which readily distinguishes it from sibling tools like get_robotaxi_tracker and get_company. However, the main clause is a noun phrase ('Comparable AI data-center capacity') rather than an explicit action verb such as 'returns' or 'retrieves', so purpose is clear but not maximally explicit.

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 use when an agent needs comparable AI data-center capacity, and it explains that filtering by operator slug is supported. It does not explicitly state when to prefer this tool over alternatives like search_companies or get_company, nor does it mention any exclusions or when-not-to-use cases.

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 of behavioral disclosure. It usefully explains the distance metric variants (`distanceKm` vs `distanceAllKm`) and notes that each point is sourced. However, it does not disclose behavior such as the default when no operator is provided, what the full time-series response looks like, or update/freshness semantics.

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 compact and front-loaded, immediately stating the tool's purpose and key metrics. Each sentence adds value: the first defines scope, the second clarifies important distance semantics, and the third explains filtering. No filler or repetition.

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?

The description covers the main output aspects and parameter semantics, but with no output schema and no annotations, it leaves some operational gaps. An agent may not know what happens when no operator is supplied, whether multiple operators are returned, or the exact structure of the time-series data. It is adequate but not fully complete for a tool with no structured behavioral metadata.

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 a baseline of 3 applies. The description goes beyond the schema by clarifying the meaning of the `distance` metric, distinguishing paid robotaxi distance from broader autonomy distance, and explaining that filtering is by operator slug or metric name. This adds meaningful semantic context to the 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?

The description clearly identifies the tool as a robotaxi operations time-series tracker covering distance, fleet size, coverage area, and city count. It also distinguishes itself from the sibling tools by focusing on comparable operational metrics rather than company profiles, news, milestones, or 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 provides clear usage context: it is for comparable robotaxi operations time series and can be filtered by operator slug or metric name. It does not explicitly exclude alternatives, but the framing is specific enough for an agent to know when to select this tool.

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 adequately discloses the behavior: it returns a digest of sourced summaries, newest first, and is read-only. No destructive or side effects are implied, and the input parameters for filtering are transparent.

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 three sentences, each serving a purpose: naming the tool, describing the content, and explaining filters. No unnecessary words; front-loaded with identity.

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 sufficiently explains what the tool returns (summaries with companies, dates). It covers filtering and ordering. Minor gap: no mention of pagination or how to handle multiple pages, but 'limit' parameter implies truncation.

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 descriptions for all parameters. The description adds context about the digest's content and ordering ('newest first'), which goes beyond the schema. This additional context helps the agent understand the tool's behavior.

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 identifies the tool as a news digest ('Venture Atlas daily news digest'), specifies ordering ('newest first'), and describes the content of each item. It is distinct from sibling tools like 'get_company' and 'list_upcoming_milestones', which serve different purposes.

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 how to filter by company or date, implying when to use the tool (to get news). It does not explicitly state when not to use it or mention alternatives, but the context is clear enough for typical use.

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, so the description carries the burden and does disclose ordering (soonest first, undated last) and what the result includes (source of commitment plus the company's track record). It stops short of stating read-only semantics, pagination behavior, or any 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?

Three tight sentences, with the primary scope front-loaded and the explanatory 'forward-looking view' framing kept short. Slight redundancy in re-describing the return payload, but nothing wasteful.

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 list tool with full schema coverage and no output schema, the description covers what the tool returns and how results are ordered. Pagination/cap behavior beyond the schema's limit field is not mentioned, a minor 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%, so the schema fully documents limit, company, industry, and includeUndated; the baseline is 3. The description only restates that company/industry filtering exists and that undated items sort last.

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+resource with scope ('announced, still-upcoming milestones with target dates across all tracked companies, soonest first') and even previews the payload (source of commitment, company track record). It does not name or distinguish itself against any sibling tool, so it falls short of 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?

The description frames it as 'the forward-looking view,' which implies usage relative to a past/historical view, and notes filtering options. But there is no explicit when-to-use vs. latest_news or the tracker tools, 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?

No annotations are provided, so the description carries the burden; it discloses the searched field set and the response shape (slim records with a trimmed brief), which is real behavioral context for a search tool. It does not cover pagination, case sensitivity, or match ranking, which are the remaining gaps for a no-annotation read 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?

Three sentences, front-loaded with purpose, then return shape and routing, then the query-omission rule. Every sentence earns its place with no 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 compensates by describing the return shape and steering to get_company for detail, which is sufficient to call the tool correctly. Pagination and total-count behavior remain unspecified, keeping it just short of 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% – limit, query, and the industry enum all carry their own descriptions, so the schema does the heavy lifting. The description reinforces the query-omission semantics but adds no syntax or format detail beyond the schema; baseline 3 applies.

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 (companies tracked by Venture Atlas via free text and/or industry), plus the exact fields matched: name, summary, product names, CEO, headquarters, industry. The routing sentence 'call get_company for the full profile' clearly separates it from its 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?

Names the alternative (get_company) and the condition that selects it ('full profile' vs the slim records returned here), and explains the query-omission case ('Omit `query` to list every company in an industry'). An agent can decide between this and the sibling without opening either schema.

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