Revnuvo Company Intelligence

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

With no annotations, the description carries full responsibility for behavior disclosure. It declares the read-only nature by saying 'Retrieve an overview', specifies a representative result set (current technologies with confidence, DNS records, timestamps, observation count, latest state hash), and calls out the special 'not_observed (404-equivalent)' case when no history exists. This is meaningful behavioral information.

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 two sentences: the first states the purpose and contents, the second adds the canonical use case and the error condition. No filler words; it is front-loaded with the substantive purpose.

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 tool has a single parameter, no output schema, and no annotations, so the description needed to explain both input and expected output. It enumerates the main fields returned and the not_observed behavior, which gives an agent enough to decide and invoke correctly. All necessary information is present.

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 only parameter, 'domain', is fully documented in the input schema (100% coverage) with an example. The description mentions 'company domain' but adds no new semantics beyond what the schema already provides, so a baseline score of 3 is appropriate.

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 ('Retrieve') and resource ('overview of a company domain from Revnuvo's observation history'), enumerates the exact fields returned, and names the typical use case. The sibling tool names indicate distinct narrower scopes (technologies, changes, timeline), and this 'overview' aggregates multiple aspects, making it distinguishable.

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 explicitly states when to use it: 'Use it to answer "what do we know about this company?"' This gives clear context for the agent. It doesn't explicitly name alternatives or exclusions, but the overview framing implies when a summary rather than a deep-dive into one aspect is wanted.

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 and does well: it discloses that this is a retrieval operation and details the returned attributes (before/after values, detection timestamp, confidence, evidence). It does not discuss sorting, pagination, or empty-result behavior, but the read-only nature is clear from 'retrieve.'

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 packs the full behavioral scope and returned fields, and the second provides a crisp user-oriented summary. Every phrase contributes, with no filler or 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?

The definition is complete enough for a simple read tool: input parameters are fully covered by the schemawatermark, and the description explains what results contain. Minor gaps remain around result ordering/pagination and behavior when no changes exist, so it is not a perfect 5.

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 description need not repeat parameter details. It adds no new meaning about limit or since beyond the schema, keeping this at the baseline appropriate for fully documented 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 names a specific verb and resource ('Retrieve observed changes at a company domain') and enumerates the exact types of changes returned. It also answers the intended user question, making it easy to distinguish from sibling tools like get_company_state or get_company_technologies.

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 usage by framing the tool as answering 'what changed at this company and when?', but it does not explicitly say when to prefer this over siblings such as get_company_timeline or get_company_state, nor does it state exclusions or prerequisites.

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 provided, the description carries the behavioral burden. It discloses that this is a point-in-time measurement, that it is timestamped and hashed, and that it returns a read-style snapshot of multiple data categories. This goes beyond a bare 'Retrieve' claim and gives the agent meaningful expectations about the data's temporal nature.

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: one opening sentence defining scope and contents, one sentence establishing temporal/hash behavior, and one usage sentence. Every sentence adds value and the most important scoping information 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 single-parameter read tool with no output schema, the description is nearly complete. It enumerates the main return categories and clarifies that this is the latest observed state. It does not detail exact field structures or error/rate-limit behavior, but those are not essential for this simple parameter surface.

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 input schema already fully documents the single 'domain' parameter with an example. The description does not add new parameter-level meaning beyond what the schema provides, so the baseline score of 3 is appropriate.

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 ('Retrieve') and resource ('company's latest full observed state') and enumerates exactly what is included: HTTP status/headers, technologies with evidence and confidence, scripts, and DNS records. This clearly distinguishes it from summary or historical sibling tools like revnuvo_get_company_changes and revnuvo_get_company_technologies.

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 context: use it when the complete current picture is needed rather than a summary. It does not explicitly name alternatives or state when not to use it, but the 'rather than a summary' contrast and point-in-time framing give adequate situational guidance.

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 does the disclosure work itself and goes beyond the obvious read operation: it specifies that detection is 'currently detected,' confidence is a 0-1 score, version is included only when detectable, and evidence strings accompany detections. The caveat that absence means 'not-detected, never proven-absent' is a genuinely non-obvious behavior that helps an agent avoid over-interpreting negative results.

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, all informative: the first sentence front-loads the key behavior and return fields, the second gives a user-friendly paraphrase, and the third adds a high-value interpretive caveat. No sentence is filler or redundant with 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 simple one-parameter tool with no output schema or annotations, the description covers the input, return contents, and the critical absence semantics. The only notable gap is domain formatting and error behavior, which is a minor omission for this low-complexity 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 100%, so the parameter is already documented; the description only reinforces that technologies are scoped to 'a company domain' without adding format details such as whether protocols or subdomains are accepted. Baseline 3 is appropriate because the schema carries the semantic weight.

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 first sentence names a specific verb (retrieve), resource (technologies currently detected at a company domain), and enumerates the returned fields, so the core purpose is unmistakable. It does not explicitly contrast with sibling tools such as revnuvo_get_company or revnuvo_get_company_timeline, though the resource name itself makes the distinction fairly obvious.

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 'Answers ...' sentence gives the agent a clear user query this tool is meant to satisfy, which is concrete usage guidance. It does not provide exclusions or name alternatives, but for a single-resource lookup the implied context is clear enough.

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 must carry the full burden. It discloses a key limitation: 'History depth depends on your plan (free: 7 days).' It also specifies the output contents. However, it does not mention read-only status (implied by 'Retrieve'), rate limits, pagination behavior beyond the limit parameter, or any potential side effects. For a read tool, this is adequate but not exhaustive.

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 two sentences, front-loaded with the main purpose and contents, then a use case, then a plan caveat. There is no redundant phrasing; every sentence adds value. It is concise and well-structured.

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 (returns both observations and change events) and the absence of an output schema, the description gives a solid overview of the data returned and a caveat about plan-dependent depth. It doesn't detail the exact output structure or pagination beyond the limit parameter, but the schema covers the limit. Overall, it is sufficiently complete for an agent to call it 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 100% for both parameters (domain and limit). The description adds no additional parameter guidance beyond what the schema already provides, so it meets the baseline of 3.

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 states the tool retrieves the chronological observation history for a company, listing the specific data points (timestamp, source, confidence, state hash, change events). It is unambiguous about the resource and action, but does not explicitly differentiate from sibling tools like revnuvo_get_company_changes, which might overlap in returning change events.

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 a clear use case: 'Use it to answer how did this company's stack evolve?' This gives context for when to use the tool. However, it does not mention when not to use it or name alternative tools, so it lacks exclusions but still offers a clear contextual trigger.

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, and it does substantial work: scan intervals, plan restrictions, observation recording, change detection, result delivery, and the domain-limit side effect. This is strong disclosure for a monitoring tool, though it omits details like idempotency or authentication requirements.

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, front-loaded with the core action, and each clause adds useful information about scheduling, delivery, or plan impact. The second sentence is somewhat dense with lists, but it avoids unnecessary filler and reads cleanly.

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 tool with only two parameters, no output schema, and no annotations, the description is largely complete: it explains what the tool does, how results are obtained, and the important domain-limit consequence. It does not specify whether re-monitoring an already-watched domain updates or fails, but this is a minor gap for initial 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 100%, with both parameters already well documented: `domain` is self-explanatory and `interval_hours` includes its enum values and plan constraints. The description restates the interval/plan relationship but adds no new parameter-level meaning, so the baseline of 3 is appropriate.

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 and resource: "Start monitoring a company domain," and explains the ongoing, change-detection behavior. This clearly distinguishes it from sibling tools like revnuvo_get_company or revnuvo_search_company, which are one-time lookup or search operations.

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 an explicit trigger: "Use it to answer 'alert me when something changes at this company'." It also explains where results surface (search_signals or webhook/email), giving an agent clear context. It does not explicitly say when NOT to use one-off alternatives like get_company or get_company_changes, so it is not a full 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses the return fields, the constraint that only observed companies are returned, and explicitly states 'nothing is invented'—a strong guarantee against hallucinated data. However, it does not explicitly state that the operation is read-only (though 'search' implies it), nor does it mention ordering or potential rate limits. These are minor gaps given the tool's simplicity, but the description does a good job overall.

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 concise—three sentences with zero waste. It front-loads the core purpose and return fields, then provides usage guidance and a boundary constraint. Each sentence earns its place, and the structure is easy to scan. This is exemplary conciseness.

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 search tool with three well-documented parameters and no output schema, the description is nearly complete. It covers what the tool returns, when to use it, and its scope limitation. It does not mention result ordering (e.g., by first-seen date) or that empty results mean no history, but these are implied and not critical. The description is sufficient for an agent to call the tool 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?

The input schema already provides 100% description coverage for all three parameters (limit, query, offset), including defaults and examples. The description does not add any additional meaning or context about the parameters beyond what the schema already states. Since the schema does the heavy lifting, a baseline of 3 is appropriate; the description adds no extra value here.

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 states the tool's function: searching companies previously observed by Revnuvo. It lists the exact fields returned (domain, first seen, observation count, tech stack, last scan time), which makes its purpose concrete and distinguishable from sibling tools like revnuvo_get_company (which likely fetches a single company) and revnuvo_search_signals (which searches signals). The phrase 'before querying deeper' explicitly positions it as a preliminary 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?

The description provides explicit guidance: 'Use this to check whether Revnuvo already has history for a company before querying deeper.' This tells the agent when to invoke this tool versus alternatives. It also clarifies the boundary condition: only observed companies are returned, so the agent can interpret empty results as no prior history. This is clear, actionable usage guidance.

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 and does useful work: it defines signals as observed changes, not purchase-intent claims, and states that every signal includes before/after state, evidence, and confidence. It could additionally disclose ordering or pagination behavior, but the core semantics are well covered.

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 deliver scope, filters, a concrete use case, result contents, and a caveat. It is front-loaded with the core purpose and every sentence adds value without repeating the title or schema verbatim.

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 and no annotations, the description compensates by explaining what a signal is and what each result contains. The schema covers parameter limits. It stops just short of full completeness by not explicitly stating how filters combine or how results are ordered.

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 baseline is 3. The description adds helpful technology examples like 'vue' and 'hubspot', and restates the event types and score range, but it does not materially extend the schema's meaning for limit, since, or offset.

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 opens with a specific verb and resource: 'Search Revnuvo's signal feed across all observed companies.' It enumerates filter dimensions and gives a concrete question the tool answers ('which companies recently adopted technology X?'), making it easy to distinguish from per-company siblings like revnuvo_get_company_changes.

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 clearly frames when to use this tool: for cross-company signal search and technology adoption questions, and it cautions that signals are observed changes rather than purchase-intent claims. It does not explicitly name alternative tools or state when not to use it, so it falls just short of full routing guidance.

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 does disclose a key behavioral trait: the trust score is only provided 'when the Trust service is reachable,' indicating conditional availability. However, it does not mention whether the operation is read-only, requires authentication, or has side effects. The conditional nature is helpful, but other behavioral aspects remain implicit.

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 two concise sentences, front-loaded with the purpose and listing specific return elements. No unnecessary words; each phrase contributes. The conditional trust service note is efficiently integrated.

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 single-parameter tool with no output schema and no annotations, the description covers the main points: what it does, what it returns, and when to use it. It mentions the conditional trust score. However, it does not explicitly state what it does not do (e.g., does not provide historical changes, which might be expected from the 'changes' sibling). This is a minor gap given the simplicity of the 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 description coverage is 100% (the domain property has a description: 'Company domain.'), so the baseline is 3. The tool description references the domain parameter but adds no additional semantic detail (e.g., format, examples, constraints). It provides minimal extra value 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?

The description clearly states the tool's purpose: verifying what Revnuvo can substantiate about a company domain, listing specific outputs (observation history, count, state hash, trust score). The verb 'Verify' and the resource 'company domain' are explicit, and the focus on evidence-backed verification distinguishes it from sibling tools like revnuvo_get_company (retrieval) or revnuvo_get_company_state (specific state).

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 a clear usage context: 'Use it for diligence flows that need evidence-backed company verification rather than assumptions.' This tells the agent when to use it, but does not explicitly name alternatives or say when NOT to use it (e.g., 'for raw company data use revnuvo_get_company'). It gives a clear context without explicit exclusions.

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