token-intel

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

With no annotations provided, the description fully discloses important behaviors: the cost ($0.005 USDC) and the comprehensive list of checks performed. It also states the overarching question the tool answers, giving the agent clear expectations about what the tool reveals.

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, dense sentence that efficiently conveys purpose, scope, and cost. It front-loads the core action and lists specific checks without 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?

For a tool with no output schema, the description gives sufficient context about the audit scope and the key question it answers. It could mention output format or return value briefly, but the current level is adequate given the complexity.

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 a single parameter 'mint' described as 'Base58 Solana SPL token mint address to analyze'. The description adds no further parameter-specific details beyond the schema, which is adequate for a simple input.

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 specifies a concrete verb ('audit') and resource ('token authority'), enumerates specific checks (mint/freeze/close/balance-mutable authorities, transfer hooks, metadata mutability, Token-2022 extension risks, malicious-creator checks), and explicitly answers the user's likely question ('can the dev structurally rug this?'). This distinguishes it from sibling tools like check_safety or get_token_intel.

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 implies use when evaluating token authority risks (e.g., before investing or interacting with a token). It does not explicitly state when not to use it or suggest alternatives, but the context is strong enough for an agent to decide.

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 full burden. It discloses cost ($0.005 USDC), speed (FAST), and detailed return fields (verdict, risk score, confidence, safeToTrade, kill-flags). This is transparent, though it doesn't explicitly state read-only behavior or side effects.

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 purpose and cost, then outputs and usage context. No unnecessary words; every sentence adds value.

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, the description fully explains inputs (mint address), outputs (detailed list), and usage context (pre-trade safety gate with cost). No 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% with one parameter described. The description adds no new parameter-level details beyond the schema, so baseline 3 applies. The overall tool context is helpful but not for individual 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 it's a 'FAST pre-trade safety gate' for tokens, listing specific outputs like verdict, risk score, and kill-flags. It explicitly distinguishes its purpose from siblings by mentioning 'built to be called on every candidate token before buying.'

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 says it's for pre-trade safety checks and should be called before buying, providing clear context. It doesn't explicitly exclude alternatives or give when-not-to-use guidance, but the implied usage is strong given the sibling tools.

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 full burden. It discloses the cost ($0.05 flat), output components (verdict, risk score, safeToTrade), and batch limit. However, it does not mention whether the operation is read-only, authentication requirements, rate limits, or error behavior. For a query tool, this is adequate but not comprehensive.

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 long, front-loading the action and key details. Every sentence serves a purpose: the first states what it does and key constraints/outcomes, the second states when to use it. 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?

The description covers the tool's purpose, input batch size, cost, and return fields. Given no output schema or annotations, it provides sufficient context for an AI agent to understand basic behavior and use. Minor gaps: no return structure format or error handling, but these are not essential for a straightforward query 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%, meaning the schema fully describes the single parameter 'mints' (array, pattern, min/max). The description adds context about batch rug-screening and the cost but does not provide new semantic details beyond reinforcing the batch nature and purpose. Baseline 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 'rug-screen' and clearly identifies the resource (Solana mints) and operation (batch screening up to 20). It distinguishes from sibling tools by focusing on rug detection versus authorities, safety, holders, LP, new tokens, or general intel.

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 states the use case: 'For watchlist / portfolio / multi-candidate scans in one paid call.' It implies this tool is for batch screening, contrasting with sibling tools that likely handle single-item or specialized analyses. It does not explicitly list when not to use it or name alternatives, but the 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?

With no annotations provided, the description must reveal behavioral traits. It describes output (percentages, counts, flags) but does not explicitly state that the tool is read-only or discuss side effects, rate limits, or data freshness. The description 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 a single sentence that efficiently lists all key outputs. It is front-loaded with the main purpose and contains no unnecessary words 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?

Given only one parameter and no output schema, the description covers the intended outputs but omits details like response format, error conditions, or prerequisites (e.g., mint must exist). It is sufficient for a simple tool but could be more 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 schema description coverage is 100%, with a single parameter 'mint' well-defined by regex and description. The tool description adds no additional meaning beyond what the schema provides, thus baseline 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's function: analyzing holder concentration and insider signals for a given mint. It lists specific metrics (top-10 %, top-5 %, holder count, insider detection) and distinguishes from sibling tools like check_authorities or check_safety by focusing on holder analysis.

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 does not provide any guidance on when to use this tool over alternatives. It implicitly suggests use for holder analysis, but lacks explicit context like 'use this when you need to assess holder concentration or insider control' or 'avoid if looking for authority checks.'

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?

The description discloses the tool's outputs and its read-only nature (analysis). No annotations exist, but the description is honest about what it computes. However, it does not mention potential limitations like rate limits or data freshness, which could be helpful.

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 succinct sentences with no wasted words. The first lists key output metrics, the second summarizes the use case. Ideal structure for quick comprehension.

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 (one parameter, no output schema, no annotations), the description adequately explains what the tool does and what it returns. Minor omission: no mention of error cases or behavior for invalid mints, but overall complete enough for its complexity.

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 single parameter 'mint' is described in the schema as 'Base58 Solana SPL token mint address to analyze'. The description adds context about what the tool does with that parameter but does not elaborate on format or constraints beyond the schema. With 100% schema coverage, baseline 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's function: analyzing LP-lock depth for a specific token. It specifies outputs (TVL, locked %, burn %, top holders) and the core question it answers, distinguishing it from siblings like 'get_holder_analysis' which likely focuses on general holder info.

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 for verifying liquidity locks but does not explicitly guide when to use this tool over alternatives. No direct comparison to siblings or mention of prerequisites, leaving the agent to infer usage context.

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?

Discloses pre-scoring and need for server-side feed, but lacks details on scoring source, rate limits, or behavior when feed is disabled. With no annotations, more transparency would be beneficial.

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 concise sentences with no wasted words. Front-loaded with purpose and key features.

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?

Covers tool purpose, parameters, and prerequisite. Lacks details on return format (since no output schema) but is adequate given the tool's focused scope and sibling context.

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 description briefly lists parameters but adds little beyond the schema definitions. No example values or edge cases provided.

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?

Description clearly states it provides fresh Solana tokens pre-scored with rug risk, distinguishing it from sibling tools like check_safety and get_token_intel that likely analyze existing tokens.

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?

Implies usage for discovering new launches with safety checks, mentions optional filters and a prerequisite (server-side feed enabled), but does not explicitly state when not to use or compare to 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 full burden. It discloses the cost ($0.01 USDC via x402) and data sources (RugCheck, GoPlus, on-chain). However, it lacks details on idempotency, rate limits, or side effects, though not critical for a read-like 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 a single dense sentence that efficiently conveys purpose and outputs. It is well-structured but could be split for readability. 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?

Given no output schema, the description thoroughly explains all return values: verdict enum, risk score range, confidence, LP-lock details, holder concentration, authority flags, and per-source breakdown. It also covers cost, making it fully informative for agent use.

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 covers the mint parameter completely with pattern and description. The description adds the important cost implication ($0.01 USDC) not in the schema, enhancing parameter usage understanding.

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 provides full Solana token rug-risk intelligence and lists specific outputs (verdict, risk score, confidence, LP-lock depth, holder concentration, authority flags, per-source breakdown). It distinguishes itself from siblings like check_authorities and get_holder_analysis by being the comprehensive version.

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 the tool is for comprehensive risk assessment but does not explicitly state when to use it over siblings like check_safety or get_lp_analysis. No exclusions or alternatives are given, only implicit purpose.

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 provided, but description fully discloses deterministic ordering criteria, cost, output (reasons and kill flags), and that no opaque model score is used. This provides complete behavioral insight.

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?

Description is concise and front-loads key information (cost, number of candidates). Slightly dense but effective with minimal 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?

Given the complexity of ranking with deterministic logic, description covers ordering criteria, output format, and cost. No output schema provided, but description sufficiently explains expected results.

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 detailed constraints (min/max items, pattern). Description adds context about base58 Solana addresses but does not significantly enhance meaning beyond 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?

Description clearly states it ranks up to 20 Solana token candidates with a specific cost, distinguishing it from sibling tools like check_safety or get_token_intel which operate on individual tokens.

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?

Implies usage for ranking tokens with a deterministic safest-first order, but does not explicitly state when to use or not use this tool compared to alternatives.

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