Animica

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

With no annotations, the description must disclose behavior. It fails to clarify whether the inference is synchronous or asynchronous, whether it returns a direct answer or a job ID (given the existence of animica_ai_job_status), or what side effects (if any) occur. The phrase 'OpenAI-compatible inference' hints at a direct API call but doesn't confirm response 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?

The description is very short and front-loaded, but 'Cheap general' is vague and adds little value. It is not bloated, but it is under-specified, which prevents it from being considered highly concise in a useful way.

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 AI inference and the presence of related tools like animica_ai_job_status, the description is incomplete. It doesn't explain the expected output, whether a job is created, or how the response is delivered. The output schema exists but is not referenced in the description, leaving the agent to guess at the tool's full behavior.

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 has 0% description coverage, so the description must compensate. It does not explain any of the five parameters (model, prompt, system, max_tokens, temperature). While the parameter names are standard for AI inference, the description adds no semantic meaning beyond the schema itself.

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: 'Ask Animica's ENA AI a question' with the qualifier 'OpenAI-compatible inference'. This identifies the resource (ENA AI) and the action (ask). It is distinct from sibling tools like animica_ai_job_status or animica_ai_models, which focus on other aspects. However, it lacks specifics on the scope of 'question' or output format, so it's not a 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 offers only 'Cheap general' as guidance, implying a low-cost, general-purpose use but provides no explicit when-to-use or when-not-to-use context. There is no mention of alternatives, prerequisites, or situations where another tool (e.g., animica_ai_job_status) would be more appropriate. This leaves the agent without clear selection criteria.

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 transparency burden. It discloses 'read-only' which is a useful behavioral trait, but nothing else—no return format, error behavior, or dependencies. The truncated ending fails to describe the return value, which is a significant gap.

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 incomplete sentence ending with 'Returns the', which is a structural defect. It is not concise in a helpful way—it is simply truncated and fails to deliver a complete thought.

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?

Despite the tool being simple with one parameter and an output schema, the description is incomplete. It cuts off before explaining the return value, offers no usage context, and lacks any behavioral details beyond read-only. This is inadequate for an agent to reliably invoke 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?

The schema only defines job_id as a string with 0% description coverage. The description adds that the tool reads 'by id', clarifying that job_id is the identifier of a request/job, but it does not explain the format, source, or how to obtain it.

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 reads the status of an ENA AI request/job by id, using a specific verb (read) and resource (status). However, the sentence is truncated mid-thought ('Returns the'), which slightly undermines clarity, and the term 'ENA AI' is ambiguous without context.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit guidance on when to use this tool versus alternatives like animica_ai_ask. The read-only hint implies it's for checking status, but no direct comparison or exclusions are 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?

No annotations are provided, so the description carries the full burden. The verb 'List' strongly implies a read-only operation, and the tool is simple (no parameters). However, it does not disclose any additional behavioral details such as authentication requirements, pagination, or whether the list is static or dynamic. The description is 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?

A single, well-formed sentence that conveys the complete purpose. No wasted words, no redundant information. It gets straight to the point.

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 is a simple, parameterless list operation, and the output schema is present (though not shown in detail). The description provides enough context—'Animica's OpenAI-compatible API'—to clarify the scope. For this level of complexity, the description is complete without needing to elaborate on return values or pagination.

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, and the schema coverage is 100% (trivially). Per the rubric, the baseline for 0 params is 4. The description adds context about what the returned list represents, but there is no parameter-specific semantics to clarify. Baseline 4 is appropriate 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 uses the specific verb 'List' and clearly identifies the resource as 'the AI models available on Animica's OpenAI-compatible API.' This is distinct from sibling tools like animica_ai_ask (which asks the model) and animica_ai_job_status (which checks job status). No ambiguity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use the tool: when one needs to see available AI models. It does not explicitly mention alternatives or exclusions, but the context is clear given the distinguishable sibling names. For a simple listing tool, this is sufficient.

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 burden. It explicitly states 'READ-ONLY' and 'snapshot', which honestly discloses the non-mutating nature. It does not cover edge cases like invalid addresses, but the simple read-only nature mitigates the need.

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 entire description is one short sentence, front-loaded with 'READ-ONLY' and immediately conveys the purpose, input, and output. No superfluous 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 tool is simple with one input and has an output schema; the description states the key return fields (balance, nonce). Given the low complexity and existing output schema, the description is adequate. It doesn't mention error handling, but the output schema likely covers that.

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 has a single address parameter with 0% description coverage. The description compensates by specifying the address format as 'anim1...' and clarifying that it is the account to snapshot. This adds meaning beyond the bare 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 states 'READ-ONLY account snapshot for an anim1... address: balance + nonce.' This clearly identifies the tool's function (fetching account state) and the specific output (balance and nonce), distinguishing it from sibling tools like block/head queries.

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 when account balance and nonce are needed but does not explicitly contrast with alternatives like animica_chain_block or animica_chain_head. The context is clear enough that an agent can infer when to use it, but it lacks explicit exclusions or alternative mentions.

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 labels the operation as 'read-only', which is a useful behavioral disclosure given there are no annotations. However, it fails to complete the explanation of what happens when neither parameter is provided, and it does not mention edge cases like invalid hashes or negative heights, leaving behavioral expectations unclear.

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, incomplete sentence that ends abruptly ('With neither, reads'). This is not concise but structurally broken, indicating a likely truncation error. It fails to form a complete thought and therefore does not earn credit for efficiency.

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?

Although an output schema exists (reducing the need to describe return values), the description still needs to clarify the tool's fallback behavior and parameter semantics. The truncated sentence and lack of parameter explanation mean the tool is not fully specified, especially for the case where neither parameter is passed.

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 provides defaults (hash='', height=-1) but zero description coverage. The description names both parameters ('height' and 'hash') and indicates they are alternative selectors, but it does not explain the meaning of the defaults, what occurs if both are provided, or how the parameters are validated. This leaves the agent to rely entirely on the schema without additional context.

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 reads a block by 'height' or 'hash', which distinguishes it from sibling tools like 'animica_chain_head' or 'animica_chain_account'. However, the trailing sentence 'With neither, reads' is incomplete, leaving the fallback behavior ambiguous and slightly diminishing clarity.

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 on when to use this tool versus alternatives such as 'animica_chain_head' for the latest block or 'animica_chain_account' for account info. The truncated fallback sentence hints at a default behavior but does not specify it, so usage context is not fully conveyed.

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 disclosing behavior. It explicitly states 'Read-only', which signals no side effects, and describes the output content (height/hash and chain id). This is sufficient for a simple getter, though it does not mention potential error conditions or rate limits. Overall, it is transparent for the tool's simplicity.

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, focused sentence that front-loads the verb and resource, followed by the output details. Every word earns its place, with no filler or redundancy. It is efficiently concise while remaining informative.

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 (0 parameters, read-only, output schema present), the description is complete. It specifies what the tool returns and that it is read-only. The output schema covers the return structure, so the description does not need to elaborate further. No critical information is missing.

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 description naturally adds no parameter details. The input schema is empty and already communicates that no parameters are required. The baseline for 0 parameters is 4, and the description does not need to compensate for anything.

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 with a specific verb ('Read') and resource ('the chain head'), and explicitly lists the returned data (current height/hash and chain id). This distinguishes it from sibling tools like animica_chain_account and animica_chain_block, which target different aspects of the chain.

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 when to use this tool: whenever the chain head's height/hash or chain id is needed. The 'Read-only' label provides context that this is a safe, non-mutating operation, but no explicit alternatives or exclusions are mentioned. The context is clear, though it could be improved by explicitly contrasting with sibling chain 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 present, and the description discloses no behavioral traits such as read-only status, authentication needs, rate limits, or side effects. The 'FREE:' note is ambiguous and insufficient to convey safety or cost behavior. The description carries the full burden here but fails to provide any transparency.

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 conveys the core purpose. It is front-loaded with the 'FREE:' label, which is not essential but does not waste space. It could be improved by incorporating param guidance, but for its length, it earns its place.

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 low complexity (one optional param, output schema exists), the description is incomplete because it omits explanation of the 'credits' parameter and any behavioral context. The output schema may help with return values, but the input side is left opaque, making effective invocation guesswork.

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 has one parameter 'credits' (string, optional, default '') with 0% description coverage. The description does not explain what this parameter represents (e.g., a token identifier). The agent cannot determine how to use it, and the description adds no meaning 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 states 'balance, expiry and spend history for a prepaid credit token,' clearly identifying the resource (credit token) and the data provided. This distinguishes it from sibling tools that deal with AI, blockchain, or other services. A small improvement would be an explicit verb like 'get' or 'retrieve,' but the intent is clear enough.

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 guidance is provided on when to use this tool versus alternatives, or whether there are prerequisites or restrictions. The prefix 'FREE:' hints at cost behavior but does not constitute usage advice. The agent receives no help with tool selection beyond the 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?

The description discloses the pricing model (PAID, ~$0.005 per BATCH) and the batch limit (up to 256 texts), which are behavioral traits beyond parameter or annotation constraints. Since annotations are absent, the description carries the full burden and addresses key operational constraints.

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 a single sentence with 10 words, yet conveys essential purpose, cost, limit, and output dimension. Every element 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?

Given the tool has an output schema, the description does not need to explain return values. The description covers purpose, cost, batch limit, and output dimension, which is sufficient for an embedding tool with moderate complexity. However, it could add more context about the typical use case versus alternative embedding methods among siblings.

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 has 2 parameters with 0% schema description coverage, placing the burden on the description. The description clarifies that 'texts_json' accepts multiple texts (up to 256) in a single call and produces 384-dimensional embeddings, but it does not describe the format, structure, or constraints of the other parameter ('credits'). A score of 3 is appropriate as the description compensates partially.

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 clear verb-resource combination ('embed up to 256 texts in one call') with specific technical detail (384-dim embeddings). Among siblings like animica_qdna_verify_gene, animica_web_fetch, and animica_ai_ask, this uniquely identifies the embedding service, distinguishing it effectively from other tools.

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 states it is a paid tool and quantifies the cost per batch ($0.005 per BATCH), which guides the agent on when to use it (embedding needs) and when to avoid (cost-sensitive scenarios). However, it does not explicitly contrast with non-embedding tools among siblings.

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 should disclose behavioral traits. It does not mention whether the tool makes external calls, returns static content, requires authentication, or what kind of output to expect. The truncated sentence leaves much undisclosed.

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 fragment, apparently cut off, and lacks the completeness of a well-formed sentence. It is under-specified rather than concise, and the structure is poor.

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?

Despite having an output schema and no parameters, the description is incomplete due to truncation. It does not explain what the output contains or how to use the tool, so the agent cannot fully understand its purpose or expected 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 tool has zero parameters, so there is nothing for the description to explain. Baseline for zero-param tools is 4, which is appropriate 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 begins to indicate that the tool provides general information about Animica and its OpenAI-compatible API, but it is truncated ('...the') and does not clearly state the action or the resource returned. It is not a tautology, but it lacks specificity.

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 learning about Animica, but gives no explicit context on when to use it versus sibling tools. There are no alternatives mentioned or exclusion criteria, leaving the agent to infer usage.

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 of disclosing side effects. It explicitly states 'Read-only,' which is a key behavioral trait. It also clarifies the data source ('from the chain'). More could be said about return format, but the output schema handles that.

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 front-loads the action and resource. It is concise with no filler, perfectly sized for a simple read-only tool.

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 parameters, an output schema, and a straightforward read-only purpose, the description is fully sufficient. It covers the essential behavior and leaves return details to the schema. No gaps remain.

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 does not need to explain parameter semantics, and the input schema is trivially complete. No additional param information is required.

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 ('Read') with a clear resource ('the current network hashrate from the chain'). This unambiguously states what the tool does and distinguishes it from sibling tools like chain block or account readers.

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 when the current network hashrate is needed, but it does not explicitly mention when to use this tool over alternatives or any exclusions. Sibling tools have different purposes, so the intent is clear, but no explicit guidance is given.

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 full burden for behavioral transparency. It discloses that the tool is paid (~$0.006) and anchors a digest on-chain, implying a mutation/transaction. It does not reveal what happens on failure, whether the proof is returned immediately, or any side effects. The cost disclosure is useful, but more detail on behavioral traits (e.g., idempotency, latency) is needed.

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 with no wasted words. It front-loads the cost indicator ('PAID') and clearly states the core action. Every word 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?

Given the tool complexity (3 parameters, paid mutation, no annotations), the description is minimally adequate but incomplete. It lacks details on parameter semantics, return values (despite output schema existing, the description doesn’t clarify what the proof contains or how to use it), and error conditions. The output schema may partially compensate, but the description should still contextualize 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?

Schema description coverage is 0%, so the description must compensate for the three parameters. It only mentions 'SHA-256 digest' in the purpose, which loosely relates to the required 'digest' parameter, but does not explain format (hex, base64?), constraints, or how 'memo' and 'credits' affect behavior. This leaves agents guessing about parameter usage.

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 purpose: 'anchor a SHA-256 digest on-chain with a verifiable proof.' It uses a specific verb ('anchor') and resource ('SHA-256 digest on-chain'). However, it does not differentiate itself from sibling tools like animica_chain_block or animica_chain_head, which also involve on-chain operations, missing an opportunity to clarify scope.

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 notarizing a SHA-256 digest with a verifiable proof, and the 'PAID' marker warns about cost. However, it does not specify when to use this versus alternatives (e.g., other notarization or on-chain tools), nor does it mention any prerequisites or exclusions. The context is clear but lacks explicit 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 provided, the description carries the full burden. It explicitly states 'Read-only,' a critical safety trait, and specifies the exact data returned (pool status and miner count). It does not cover rate limits or latency, but for a simple zero-parameter read, this is sufficient.

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: two phrases that include the main purpose, specific data points, and safety trait. No wasted words; every element earns its place.

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?

This is a simple, zero-parameter read-only tool. The description fully captures what it does and what it returns. An output schema exists, so return details need not be explained. The description is complete for the tool's 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 tool has zero parameters, and the input schema is empty with 100% coverage. Per the rubric, the baseline is 4 for 0-param tools. The description adds no parameter information, which is appropriate because there are none.

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 'Live mining-pool stats' with specific fields ('pool status plus active miner count'), using a specific verb ('stats') and resource. It is distinguished from siblings like animica_network_hashrate by explicitly focusing on mining-pool level 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 context is clear: this tool is for mining-pool stats, not network-level stats. However, it does not explicitly name alternatives or state when not to use it. It provides a clear context without formal 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It only reveals the paid nature and algorithms. It does not describe what happens on successful or failed verification, whether credits are consumed, or any side effects. The return value is not mentioned even though an output schema exists (not shown). This is insufficient for a paid tool with potentially irreversible credit usage.

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 (one short sentence), which is good for brevity but severely under-specifies critical details for a tool with 5 undocumented parameters and no annotations. The cost is front-loaded, but essential usage and parameter information is missing, making it inadequate for an AI agent.

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 (5 parameters, no schema descriptions, no annotations, sibling tool confusion), the description is vastly incomplete. It fails to explain parameter values, algorithm selection, credit consumption, output format, or how it differs from similar verify tools. An agent would struggle to invoke this tool correctly based solely on this description.

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 has 5 parameters with 0% description coverage, and the tool description provides almost no parameter guidance. It implies the signature uses message, signature, and public key hex strings, but does not explain formats, the meaning of 'alg_id' (integer), how to obtain or use 'credits', or constraints (e.g., hex encoding length). The agent cannot correctly populate parameters based solely on the description.

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: verify a post-quantum signature, and specifies the supported algorithms (ML-DSA-65, Dilithium3, SPHINCS+). However, it does not differentiate from the sibling tool 'animica_quantum_verify', which could cause confusion about which verification tool to use for different quantum algorithms.

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 mentions the cost (~$0.005) but provides no guidance on when to use this tool versus alternatives like 'animica_quantum_verify' or 'animica_qdna_verify_gene'. It lacks context about prerequisites (e.g., need credits) or situations where this tool is not appropriate.

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 carry the full burden of behavioral disclosure. It explains the mechanism ('recompute its content-address') but does not state whether the operation is read-only, what happens on failure, or the format of the result. It does not disclose any side effects or error behavior, which is a significant gap for a verification tool with zero annotation coverage.

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, front-loaded sentence that efficiently captures the tool's purpose. Every word contributes value, with no wasted phrases or redundant details. It is appropriately concise for a simple verification tool.

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 simple schema (one parameter) and the presence of an output schema, the description need not explain return values. However, it is incomplete regarding the required input format ('gene_json') and does not offer any behavioral or usage context. For a tool with zero annotations, the description fails to cover the essential details a user needs to invoke 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 0%, and the description does not mention the 'gene_json' parameter at all. It refers to a 'gene seal' but does not explain that gene_json should be the seal to verify, nor does it provide format, structure, or examples. The description adds no semantic value beyond the parameter name itself.

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: 'Verify a qDNA training-genome gene seal: recompute its content-address'. It uses a specific verb ('Verify'), names the resource ('qDNA training-genome gene seal'), and explains the method ('recompute its content-address'). This distinguishes it from sibling tools like animica_quantum_verify, which is more generic.

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, nor any exclusions or preconditions. The only implied usage is that it verifies a gene seal, but there is no mention of when this is appropriate or how it differs from other verification tools. No explicit context or alternative naming.

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?

There are no annotations, so the description carries the full burden of behavioral disclosure. It does not mention side effects, authentication, rate limits, or what constitutes a 'round'. The word 'verifiable' describes the beacon's property, not the tool's behavior, offering no additional insight into the tool's operation.

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 with no filler words. It is front-loaded and directly states the tool's purpose, earning its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter getter, this is minimally adequate, but it fails to provide context on how it relates to sibling quantum tools or what the output entails. The lack of usage guidance and behavioral transparency creates notable gaps, despite having an output schema.

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 there is no ambiguity in the input. The baseline of 4 for 0 parameters applies, and the description does not need to add parameter meaning.

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 action ('Get') and the resource ('Animica's verifiable quantum randomness beacon') with a specific scope ('latest round'). This distinguishes it from sibling tools like animica_quantum_draw, which likely generate or draw randomness, by focusing on the beacon's latest round.

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 such as animica_quantum_draw or animica_quantum_verify. It only states what the tool does, not when it should be selected or any 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?

With no annotations, the description bears full responsibility for disclosing behavior. It only mentions 'compute' and 'verifiable', but does not explain whether the operation is synchronous, whether it creates any side effects, how round_id and beacon_hex interact, or what permissions/rate limits apply. Significant behavioral ambiguity remains.

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 consists of a single, front-loaded sentence: 'Compute a verifiable quantum-random draw off a beacon round.' Every word contributes to the core purpose, with zero 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 tool has 5 parameters, 2 required, and no field descriptions in the schema, yet the description is only one sentence. While an output schema exists, the absence of input semantics and usage context leaves the agent under-equipped to construct a correct 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 input schema has 5 parameters with 0% description coverage, and the tool description adds no parameter information. Parameters like 'kind', 'request_id', and 'params_json' remain cryptic, and the relationship between round_id and beacon_hex is entirely unexplained.

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 'Compute' and a specific resource 'a verifiable quantum-random draw off a beacon round', which clearly distinguishes this tool from siblings like animica_quantum_beacon_latest and animica_quantum_verify. It is not fully explicit about whether it consumes an existing beacon round or initiates a new one, but the core purpose is clear.

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 guidance is provided on when to use this tool versus alternives such as animica_quantum_beacon_latest or animica_quantum_verify. There are no stated prerequisites, exclusions, or context cues beyond the innate implication that you would use it to compute a draw.

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?

There are no annotations, so the description carries the full burden of behavioral disclosure. The incomplete sentence reveals almost nothing about side effects, permissions, error behavior, or what 'recompute it from its declared' implies. This is a significant transparency gap that makes the tool risky to invoke.

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 but reads as a fragment, not a complete sentence. It lacks proper structure and ends abruptly, which is under-specification rather than effective brevity. Key information is missing.

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 parameter and no output schema visible, the description should fully explain the verification process and input requirements. It fails to do so, leaving the tool's purpose and usage unclear. The truncation makes it incomplete even for a simple 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?

The schema has one parameter, result_json, with no description (0% schema description coverage). The description fails to explain what result_json should contain or how it relates to the verification logic. The tool name implies a quantum draw result, but the truncated description does not clarify expected format or provenance.

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 begins with 'Verify a quantum-random draw client-side' which gives a clear verb and resource, but the sentence is cut off after 'from its declared', leaving the intended meaning incomplete. This makes the purpose ambiguous and fails to distinguish it from sibling tools like animica_quantum_draw or animica_quantum_beacon_latest.

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 state when to use this tool versus the related quantum tools, nor any prerequisites or context for verification. The truncated sentence offers no actionable direction.

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 carry the behavioral burden. It implies that the tool is a pre-payment estimate, but it does not clearly state that no payment or run is triggered, nor does it mention any side effects, limitations, or what the estimate represents. Minimal behavioral disclosure beyond the phrase 'BEFORE paying'.

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, well-structured sentence that gets straight to the point. It is concise, front-loaded, and contains 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?

While the tool is simple and has an output schema, the description lacks any guidance on input parameters, defaults, or what the estimate encompasses. The lack of parameter semantics and minimal behavioral context makes it incomplete for an agent to correctly invoke the tool, despite the presence of an output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description provides no information about the parameters (gpu, mem_gb, seconds). It does not compensate for the schema's lack of descriptions, leaving the agent to guess the meaning of each parameter.

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 ('Estimate') and a distinct resource ('ANM cost of an Animica Studio run'), while the phrase 'BEFORE paying' differentiates it from execution tools like animica_studio_functions. This provides a strong contrast with siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'BEFORE paying' gives clear contextual usage (use this tool before committing to payment), and it implicitly distinguishes from an actual studio run. However, it does not explicitly mention alternatives or exclusions, so it falls short of a full guideline.

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 takes on the burden of disclosing behavioral traits. It explicitly states 'Read-only,' which is a key safety characteristic, and the function name 'list' reinforces the non-mutating nature. However, it doesn't add details about return format or any side effects, though for a list operation this is relatively satisfactory.

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 is front-loaded with the action, includes the underlying function name for precision, and contains no redundancy. Every word earns its place.

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 that the tool has no parameters and an output schema is present, the description provides complete context: what it does, its read-only nature, and its identity. Nothing additional is necessary for an agent to select and invoke 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?

The tool has zero parameters, so the description does not need to explain parameter semantics. The baseline for zero-parameter tools is 4, and the description appropriately avoids adding unnecessary parameter information.

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 action ('List functions') and the resource ('deployed to Animica Studio'), and also includes the underlying function call 'aicf.fn.list' for additional disambiguation. This is distinct from sibling tools which cover AI, chain, quantum, and pool functionality.

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—it's a listing tool for deployed functions—but provides no explicit guidance on when to use it versus alternatives, nor any exclusions. For a simple list operation, the context is fairly clear, but the guidance is minimal.

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 fully disclose behavior. It mentions cost (~$0.007) and that it provides sources, which is helpful. However, it does not disclose destructive or mutating behavior, rate limits, or required permissions. The description adds moderate value beyond schema.

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 is front-loaded with the key information (cost, purpose, single page, sources). Every word is meaningful.

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 core purpose, cost, and output feature (sources). Given the presence of an output schema (which likely documents return structure), it does not need to explain return values. It is complete enough for a simple question-answering tool with only three parameters.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It explains that 'url' and 'question' are used to answer about a web page, and implies 'credits' is optional for payment (based on cost mention). This adds meaning beyond the bare schema, especially clarifying 'credits' role.

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 verb ('answer a question'), a resource ('a web page'), and a key differentiator ('WITH its sources') that distinguishes it from similar tools like animica_web_fetch or animica_ai_ask. It is clear and specific.

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 suitable for answering a question about one web page with sources, but does not explicitly state when to use it versus alternatives (e.g., animica_web_fetch for raw content, or animica_ai_ask for general knowledge). No exclusion criteria or prerequisites are 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 provided, the description must fully disclose behavioral traits. It states that the output is 'clean text' from a public web page, but it fails to mention critical details like rate limits, error handling (e.g., for unreachable pages, redirects, or large pages), authentication needs, or any side effects. The cost mention is a positive, but for a tool that fetches external content, more behavioral context is needed.

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 very concise at 10 words (excluding the cost hint in parentheses), and it front-loads the key action and resource. The cost information is a slight digression but is short and relevant. Every word earns its place, though the clarity of 'clean text' could be improved with a brief qualifier.

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 required param, no nested objects), the description covers core purpose and cost context. However, with no output schema provided (though context says it exists), a brief note on what 'clean text' means or how output is structured would improve completeness. It is adequate but leaves gaps in usage and error behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It mentions 'a public web page,' implying the URL parameter, and 'clean text' hints at the output, but it doesn't explain the 'credits' or 'max_chars' parameters. The cost note ('$0.005, or free with a credit token') partially clarifies the 'credits' field, but 'max_chars' is left unexplained. The description adds some value beyond the schema but is incomplete.

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 what the tool does: 'fetch a public web page as clean text.' It uses the specific verb 'fetch' and the resource 'public web page,' making the purpose unmistakable. However, it does not explicitly distinguish this tool from siblings that might also involve web content, like 'animica_web_ask', but the clear action and resource set it apart well.

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 mentions cost ('PAID (~$0.005, or free with a credit token)'), which gives some context about when this tool might be used vs. free alternatives. However, it doesn't specify when to use this tool versus other web-related siblings, nor does it detail any prerequisites or exclusions for the URL input. The cost hint implies a usage condition, but overall guidance is minimal.

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 of behavioral disclosure. It states 'Discover' suggesting a read-only operation, and 'pay-per-request' hints at potential cost, but it does not explicitly confirm safety, rate limits, authorization requirements, or what happens upon invocation (e.g., whether it returns a list or single item). The behavioral transparency is 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 sentence that front-loads the key purpose and scope. Every word contributes value, and there is no redundancy or unnecessary detail. It is optimally concise for a tool with no parameters.

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 an output schema but the description only hints at output content ('price, availability'). It does not explain what 'x402 APIs' are, how the results are structured, or whether pagination or filtering exists. While the tool is simple, the lack of output context leaves the agent partially informed. Completeness is adequate but not 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?

The tool has zero parameters, so the input schema is trivially complete (100% coverage). The description adds no parameter information because none is needed. Per guidelines, zero parameters warrants a baseline of 4, and the description does not detract from this.

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 'Discover' and clearly identifies the resource 'Animica's pay-per-request x402 APIs (USDC on Base)' with details on what it provides ('price, availability'). This distinguishes it from sibling tools like animica_credit_balance or animica_ai_ask, as it focuses on x402 product offerings.

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 versus alternatives. With 20 sibling tools, the lack of explicit when-to-use or when-not-to-use instructions leaves the agent to infer context without support.

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