zero-core-intel

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

The description lists data categories but says nothing about side effects, permissions, data freshness, or whether the operation is read-only. With no annotations provided, the description carries the full burden of behavioral disclosure, and it falls short.

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 a single, concise sentence that is easy to parse. The hyphenated list is efficient, though the leading 'Synthia' adds marginal value and could be omitted. Overall, it is appropriately sized with no redundant fluff.

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 one optional parameter and no output schema, the description offers a reasonable high-level overview. However, it omits the optional filter behavior and does not clarify what the response will contain, making it slightly incomplete for an agent that needs to know whether a competitor name is required and what to expect in the result.

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 fully documents the single optional parameter 'competitor' with a clear description ('Filter by competitor name (partial match)'). Schema description coverage is 100%, so the description adds no additional parameter meaning, warranting the baseline score 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 identifies the tool as competitor tracking and enumerates the types of information it covers (feature launches, pricing changes, strategic moves). This distinguishes it from sibling tools like get_market_regime and get_social_trends, though it lacks a strong imperative verb like 'retrieve' or '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?

The description provides no guidance on when to use this tool versus alternatives, no mention of prerequisites, and no exclusions. It is a purely descriptive sentence without any contextual or comparative usage information.

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. It discloses the output (regime labels and metrics) but does not explain behavioral aspects like data recency, interpretation of 'graduation velocity', or side effects. Some useful context is provided, but gaps remain.

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, well-structured sentence that immediately conveys the tool's purpose and outputs. No wasted words, and the key 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?

Given the tool's simplicity (no params) and no output schema, the description provides a reasonable overview of what is returned. It lists the regime categories and mentions additional metrics, which is sufficient for basic understanding, though 'graduation velocity' remains undefined.

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 has zero parameters, so the baseline is 4. The description adds meaning by detailing the output categories (HOT/NORMAL/COLD) and the inclusion of metrics, which effectively compensates for the empty 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 identifies the tool's function with a specific verb ('detection') and resource ('market regime'), and distinguishes it from siblings by naming the output categories HOT/NORMAL/COLD and additional metrics. It is immediately clear what this tool does.

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?

No explicit guidance is given on when to use this tool versus siblings like get_trading_signals or get_social_trends. The description only implies its purpose but does not state use cases, exclusions, or alternatives.

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 mentions the return types (terms, frequency, sources) but does not disclose rate limits, data freshness, output format, or any side effects. This is a minimal disclosure.

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 a single concise sentence that front-loads the core purpose. It is efficient, but could be slightly improved by using a verb and clearer structure, though not to a degree that hurts usability.

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?

Without an output schema, the description should clarify the return value structure and behavior, but it only lists high-level data types. It also lacks usage guidance, making it incomplete for an agent to fully understand the tool's output and applicability.

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 documents the single parameter 'hours' with a description, so schema coverage is 100%. The description adds no additional meaning beyond the schema, yielding the baseline score 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 that the tool provides trending terms, frequency, and sources from social media monitoring. This is specific and distinguishes it from siblings like get_competitor_intel or get_trading_signals, which focus on other domains.

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?

No usage guidance is provided. The description does not indicate when to use this tool over alternatives, nor does it mention any context or exclusions. The usage is only implied from the tool name and brief description.

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 bears the full responsibility for behavioral disclosure. It states that the output includes AI-generated direction, confidence, and win rate, which is useful context, but it does not mention side effects, latency, authentication, or the behavior when no token filter is provided. The read-only nature is only implied by the name.

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 a single, concise phrase that is front-loaded with the tool's purpose. It contains no wasted words and directly communicates the core value proposition.

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 only one optional parameter and no output schema, the description should clarify the default return behavior and response shape. It names the output fields but does not explain what happens when the token filter is omitted or whether the result is a list or single object. This makes it minimally adequate but with clear 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?

The input schema already describes the 'token' parameter as a filter by mint address with 100% coverage. The description adds no extra 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 clearly identifies the resource (trading signals) and specifies the key attributes (direction, confidence score, win rate) for Solana tokens. The verb is implied by the tool name 'get', and the content distinguishes it from siblings like competitor intel or market regime.

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?

No explicit guidance is provided on when to use this tool versus alternatives. The description is purely definitional and does not mention any exclusions, prerequisites, or when one might prefer a sibling 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?

No annotations are provided, so the description bears the burden of disclosure. It lists what is checked (status, uptime, payment network configuration), which suggests a read-only operation, but it doesn't explicitly state side effects, permission requirements, or return behavior. Adequate but minimal.

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 a single, clear sentence with no redundant or filler words. It earns its place by concisely stating the tool's 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?

For a simple health-check tool with no parameters and no output schema, the description provides sufficient context about what is checked. It could mention output format or what 'status' entails, but the low complexity makes the description reasonably 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?

The tool has zero parameters, so the schema is trivially complete. The description doesn't need to explain parameters or add parameter details, and with no parameters, the baseline of 4 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 action ('Check') and a specific resource ('Harvey Intel server status, uptime, and payment network configuration'). It clearly distinguishes itself from sibling tools that retrieve market or token data, as this is about system health.

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 needing server health information but provides no explicit guidance on when to choose this tool over alternatives or any exclusions. Since it's a simple health check, the implied usage is moderately 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?

With no annotations, the description must fully convey behavioral traits. It states the tool lists tools with pricing and input requirements, which suggests a read-only discovery operation, but it does not explicitly mention absence of side effects, authentication requirements, or output structure. Since 'list' implies a read operation, this is minimally transparent but lacks extra context like pagination or response format.

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 short sentences with no filler. The primary action and purpose are front-loaded in the first sentence, and the usage guidance is in the second. Every word earns its place, making it exceptionally concise.

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 tool with no parameters and no output schema, the description adequately conveys what it does and when to use it. The output is described conceptually ('list with pricing and input requirements'), but the absence of a detailed output schema means the description could have added more specifics about the returned fields or structure, though it remains sufficient for basic discovery purposes.

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 has zero parameters, so per the baseline rule, a score of 4 is appropriate. The description does not need to explain parameter semantics, and it does not attempt to. The mention of 'input requirements' refers to the contents of the output (other tools' requirements), not this tool's own 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 states the tool's function: 'List all available Harvey Intel tools with pricing and input requirements.' The verb 'List' and the resource 'available Harvey Intel tools' are specific, and the additional detail about pricing/input requirements distinguishes this discovery tool from sibling tools that provide actual intelligence data.

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 'Use this for discovery' provides an explicit use case, implying this tool is for discovering what tools exist rather than invoking them. It does not explicitly state when not to use it, but given the sibling tools are all data retrieval functions, the context effectively differentiates this tool from alternatives.

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 burden and does disclose the return payload, the 5-model ensemble methodology, and the analytical scope. It does not mention latency, failure modes, or safety guarantees, but the analysis framing reasonably implies read-only 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 sentence with high information density, placing the primary purpose first and then listing concrete outputs. No wasted words or redundant restatement of the schema.

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 a single well-documented parameter and no output schema, the description sufficiently explains what the tool returns. It could add guidance on error handling or when to prefer other tools, but it is functionally complete for 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?

The schema already documents the only parameter (mint) with a clear description ('Solana token mint address (base58)'), achieving 100% coverage. The tool description adds no additional parameter semantics, 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?

Clearly identifies a full risk-analysis function for a Solana token and explicitly lists the output fields (score, risk level, rug stage, honeypot detection, risk flags, temporal prediction). This distinguishes it from sibling tools such as scan_token_preview.

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 word 'Full' implies a comprehensive scan relative to scan_token_preview, but there is no explicit instruction on when to use this tool instead of alternatives or what context warrants the full analysis. Usage is implied 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses that the tool returns a risk level classification (LOW/MEDIUM/HIGH/CRITICAL) and implies it is a lightweight preview by calling itself 'Quick' and 'Free preview'. However, it does not mention whether the call is read-only, error behavior for invalid mint addresses, or any rate limits, which would be useful but are not critical for a simple preview 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?

The description is extremely concise, using two sentences to state the purpose, output values, and usage guidance. It is front-loaded with the core action ('Quick risk level check') and wastes no words, earning a perfect score.

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 low complexity (one parameter, no output schema), the description adequately covers what it does, what it returns, and how it differs from the full analysis tool scan_token. It does not explain what each risk level means or how it is calculated, but for a free preview, this is acceptable. The description is complete enough for an agent to invoke it correctly in many contexts.

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 fully describes the single parameter 'mint' with 'Solana token mint address (base58)'. The description does not add any additional meaning beyond the schema, so with 100% schema description coverage, 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 clearly states the tool performs a 'Quick risk level check' for a Solana token, with a specific output of LOW/MEDIUM/HIGH/CRITICAL. It distinguishes itself from the sibling tool scan_token by explicitly saying 'use scan_token for full analysis', making its scope and resource clearly defined.

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 explicitly provides usage guidance: it is a 'Free preview' intended for a quick check, with the alternative 'use scan_token for full analysis' when more comprehensive results are needed. This tells the agent when to select this tool versus its sibling scan_token.

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