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

The description discloses that the tool writes a pending local task session and explicitly states it does not publish, mint, or submit an on-chain transaction. It also reveals that a unique nonce and EIP-712 authorization are produced, providing meaningful behavior beyond the sparse annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences with no filler: purpose is front-loaded, required inputs and return values are summarized, and side-effect/sequencing guidance is included. Every sentence 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?

For a tool with no output schema, the description adequately states the returned artifacts (nonce and signed EIP-712 authorization), the local-only side effect, and the next step. It could mention response structure in more detail, but what an agent needs to call it correctly and chain it with complete_ai_task is present.

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

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

Schema description coverage is 100%, so each parameter is already documented in the input schema. The description reinforces that taskHash is a 32-byte hash and warns not to send raw task text, but it does not substantially add 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 a specific action and resource: 'create a task session' before AI work, and clarifies the local non-publishing nature. It also names the companion tool complete_ai_task, helping distinguish begin from complete in the same workflow.

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 gives clear contextual timing ('Use once before meaningful AI work') and explicit sequencing ('Pass the returned nonce to complete_ai_task; do not reuse it'). It does not explicitly enumerate when not to use the tool versus other siblings, but the workflow guidance is strong enough for an agent to route correctly.

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?

Beyond the sparse annotations, the description discloses that it validates the nonce and bounded usage, derives an evidence tier, rejects replay, writes a local receipt store, and returns an Ed25519-signed receipt. It also explicitly states non-goals: no Merkle root publication, no token minting, and no blockchain transaction. This is rich behavioral 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 front-loaded with the most critical usage rule and every sentence contributes new information: requirements, behavior, non-goals, and alternative tool routing. It is dense but appropriately sized for a 6-parameter lifecycle 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?

With sparse annotations and no output schema, the description carries the full burden of contextual explanation. It covers what happens, what is returned, what fails closed, and what not to expect. Everything an agent needs to invoke this tool correctly is present.

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

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

Schema coverage is 100%, so the schema already documents all parameters. The description adds useful relational context: the nonce comes from the begin_ai_task session, outputHash should never be the raw output, and providerEvidence is optional and only for a configured provider key. This goes beyond the schema without repeating 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 states a specific action: completing an AI task after begin_ai_task finishes meaningful work. It clearly distinguishes the tool from publishing, minting, and blockchain transactions, and names get_aipou_status for inspection. An agent can accurately identify what this tool does and what it does not do.

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?

'Use exactly once after begin_ai_task finishes meaningful work' is an explicit trigger condition. The description also explains when providerEvidence should be supplied, what happens on replay, and which sibling tool to use for inspecting recorded work. This gives both positive and negative usage guidance.

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

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

Annotations already mark this as read-only, idempotent, and non-destructive. The description adds valuable behavioral context beyond those hints by explicitly stating it is a 'pure local calculation' and listing what it does not do, such as persisting the link or interacting with Paybox. This fully discloses the operation's side-effect profile.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact, front-loaded with purpose, and every sentence earns its place. The second sentence efficiently enumerates exclusions and usage caveats without unnecessary elaboration.

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 pure local correlation tool with full schema coverage and strong annotations, the description is nearly complete: it explains purpose, side-effect safety, and verification guidance. It does not specify the exact shape/length of the returned link, but since there is no output schema, this is a minor gap rather than a blocker to 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?

Schema description coverage is 100% and all four parameters already have detailed descriptions in the schema. The tool description reinforces concepts like 'existing', 'opaque', and 'digest-only', but it does not substantially add new parameter-level meaning beyond what the schema already provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: 'derive and return a digest-only external evidence link' between an AIPOU workReceiptId and a Paybox operation artifact. It clearly distinguishes itself from the sibling create_technocore_work_link by naming the exact artifact types involved.

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 gives clear when-to-use context: 'Use it for portable correlation only' and instructs verifying artifacts with native systems before relying on the link. It also gives strong when-not-to-use exclusions (does not persist, call Paybox, unlock a wallet, create payment, sign transaction, or change eligibility), though it does not explicitly name an alternative 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?

Annotations already declare readOnly, idempotent, and non-destructive behavior, and the description adds substantial context beyond that: it is a pure local calculation, it does not persist, call Technocore, sign, read a key, verify, change eligibility, or establish delivery. The caution to verify with the native verifier further enriches 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?

Three dense sentences with no filler. The core function is front-loaded, the non-effects are compactly listed, and the verification caveat is placed last as a natural usage warning. Every sentence 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?

The description fully covers invocation-relevant context: inputs are schema-documented, side effects are clearly denied, and the intended use and caveat are stated. The only notable gap is that, with no output schema, the description does not describe the shape or format of the returned link, though 'derive and return' gives a minimal expectation.

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 100% of parameters with descriptive text, so the description is not required to repeat parameter details. It adds conceptual context such as 'digest-only' and 'existing AIPOU workReceiptId', but no per-parameter meaning beyond what the schema already provides, so the baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Derive and return') and a precise resource ('digest-only external evidence link between an existing AIPOU workReceiptId and a Technocore transport artifact'). It distinguishes the tool from its sibling create_paybox_work_link by naming the AIPOU/Technocore domain and the digest-only nature.

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 gives an explicit usage context ('Use it for portable correlation only') and clearly enumerates what the tool does not do, such as persisting, calling Technocore, signing, or establishing delivery. It lacks an explicit named alternative tool, but the when/when-not guidance is strong enough to route an agent correctly.

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?

Annotations already declare readOnlyHint, idempotentHint, and non-destructive, but the description adds specificity: it enumerates what the call does not do (creates no task or receipt, changes no state, submits no transaction) and that the result is informational rather than final. That goes well beyond a generic readOnlyHint with actionable nuance about a client-signed estimate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three dense, ordered sentences front-load the rule ('Use before complete_ai_task only when a preview is useful') and keep every subsequent sentence purposeful: parameter/behavior constraints, then scope and authority. No filler or repetition exists.

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 three-parameter, read-only tool with 100% schema coverage, the description tells the agent what it returns (JSON estimate with estimatedReward and unit), when in the workflow to call it (before completion), and why the result is non-binding. Given the output schema is absent and annotations carry the safety profile, no essential info 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?

Schema condition is 100%, and each parameter already has names, types, bounds, and descriptions. The description only mirrors 'non-negative' scoping, so it adds no semantic information over the schema. 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?

Description states a precise verb (estimate) and resource (reward for AI task), and — critically — distinguishes it from its near-sibling complete_ai_task by noting it produces no receipt or state change. It also says exactly what it returns (JSON with estimatedReward and unit), so an agent can select it without studying the schema.

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 opening clause is explicit about timing and selection: 'Use before complete_ai_task only when a preview is useful.' It also names the authoritative alternative (complete_ai_task derives the receipt evidence; the validator determines final eligibility), letting an agent know when it is NOT a substitute.

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?

Annotations already mark it read-only, idempotent, and non-destructive, and the description reinforces this by stating it does not create receipts, validate claims, contact Base, or submit transactions. It also discloses that it returns JSON with count and receipt payloads, adding useful behavioral context beyond annotations.

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 with no filler, front-loads the core purpose, and packs in output format, exclusions, and an alternative tool. Every sentence contributes meaningful guidance.

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

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

For a single-optional-parameter, read-only export tool, the description covers what it does, what it returns, when it should be used, and what it deliberately avoids. There is no output schema, so the explicit mention of count and receipt payloads is valuable and sufficient.

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

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

Schema coverage is 100% and the single parameter's description already explains the wallet filter and omission behavior. The tool description restates this without adding new parameter-level meaning, so it meets the baseline but does not exceed 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 states a specific verb ('Export') and resource ('signed receipts already stored in this MCP installation'), and clarifies the optional wallet filter. It distinguishes itself from get_aipou_status by naming the alternative explicitly, so an agent can select it correctly.

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

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

It gives explicit when-to-use guidance ('only where local receipt data is appropriate to expose') and lists what the tool does not do, pointing to get_aipou_status as the alternative for a private summary. This is clear exclusion and routing behavior.

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?

Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds specifics that it does not contact a wallet, submit a transaction, or change local state, which is useful and consistent. No contradictions.

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 deliver the purpose, what is returned, and what is not done. Every sentence earns its place, and the core purpose 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 no parameters, a rich set of annotations, and an output listing (Base chain metadata, explorer URLs, minimal ABIs), the description fully equips an agent to call this tool correctly without missing details.

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?

There are zero parameters, so the baseline is 4. The description correctly notes it reads 'configured' contract details, implying fixed configuration, which aligns with having no 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 verb 'Read' and the specific resource 'AIPOU token and claims contract details', along with its purpose ('for display or client setup'). This distinguishes it from sibling tools that handle identity, status, or transactions.

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 context for when to use it ('for display or client setup') but does not explicitly contrast with alternatives. The read-only nature is clear, and given the sibling list, it is evident this is for contract metadata retrieval rather than operations.

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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the read-only nature is structured data. The description adds meaningful context beyond that: it never returns private keys, submits transactions, or changes state, which is valuable safety and privacy information for an agent.

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 with no filler. The purpose and usage context are front-loaded, followed by return contents and explicit non-behaviors. Every clause 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?

There is no output schema, but the description enumerates the key return values: wallet address, Ed25519 public key, and fingerprint. Combined with the annotations and the absence of parameters, this is enough for an agent to understand what the tool returns and how to use it safely.

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 100% schema description coverage, so there is no parameter documentation burden. The description does not need to explain parameters, and it appropriately focuses on what the call returns instead.

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') and names an exact resource: the MCP installation's public identity with its farming wallet and collector. It also distinguishes the tool's scope by explicitly listing what it returns and what it never does, so an agent can tell it apart from the sibling tools like get_aipou_status or get_aipou_contract.

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 when to use the tool: when a client needs to display or verify the farming wallet and collector. It does not discuss exclusions or alternatives, but the stated trigger is clear enough for an agent to decide on invocation.

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?

Annotations already mark it read-only and idempotent. The description adds valuable behavioral details: it may read Base via RPC but never signs/submits transactions, changes receipts, reveals private keys, or returns full receipt payloads. This goes beyond the annotations to clarify side-effect-free and privacy-preserving behavior, with no contradictions.

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 with no fluff. The first sentence front-loads the primary use case and the exclusion of settlement tools; the second packs a dense but well-organized list of return fields and behavioral constraints. Every clause 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?

With no output schema, the description must explain what is returned, and it does: a JSON summary of receipt counts, pending AIPOU, settlement state, and wallet balance. It also clarifies what it does NOT return (full receipt payloads) and its network access. For a zero-arg read-only tool, this is complete and actionable.

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 fully covered (100%). Per the rubric, baseline is 4 for 0 params. The description doesn't need to reference parameters since none exist; it focuses on the output, 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 clearly states the tool's purpose: a read-only status checker for pending, claimed, and wallet-balance information. It lists the specific data returned (receipt counts, pending AIPOU, settlement state, wallet balance) and distinguishes it from settlement tools, making it unmistakable among its 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?

Explicitly says 'Use this first when the user asks for pending, claimed, or wallet-balance information' and tells when NOT to use settlement tools ('only after an explicit claim request'). This provides unambiguous routing guidance and prevents misuse.

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?

Annotations already indicate a non-read-only, non-idempotent operation. The description adds meaningful behavioral details: it validates receipts, publishes one Merkle root, mints rewards, and submits two Base transactions requiring user confirmation. It doesn't disclose whether eligible receipts are consumed or marked settled after the call, which would be useful, but it goes well beyond what annotations provide.

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?

Every sentence earns its place: the first sets the usage gate, the second explains the parameter and operational mechanics, the third addresses confirmation, and the last names the alternative. It is dense, front-loaded, and free of 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?

The description covers the trigger, scope, core behavior, transactions, and alternative. However, there is no output schema, and the description does not state what the tool returns (e.g., transaction hashes, settlement receipt, or confirmation status) or what post-conditions exist on the included receipts. For a multi-transaction settlement action, this is a meaningful gap.

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

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

The single parameter maxReceipts is fully documented in the schema with default, minimum, maximum, and a descriptive explanation. The description repeats the default and cap but adds no new semantic 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 uses a specific verb ('settle'/'claim') and clearly identifies the resource: one limited batch of eligible local receipts. It also explicitly distinguishes itself from settle_all_ai_rewards, so an agent can reliably know exactly what scope this tool operates on.

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 opens with an explicit trigger condition: 'Use only after an explicit user request to claim one limited batch'. It also states what it is not for ('not for status or reward previews') and names the alternative for all-pending claims, giving clear when-to-use and when-not-to-use 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?

Annotations only declare readOnly=false and idempotent=false; the description adds concrete behavior: validating eligible receipts, submitting one Merkle-root publication and one claim transaction per batch, and requiring host/user confirmation policy. It also explains bounded execution, which is valuable beyond the structured annotations.

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 four dense sentences with no filler. Each sentence earns its place: usage trigger, parameter bounds, transaction behavior, and alternative routing. It is front-loaded and easy to scan.

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 two self-describing parameters and no output schema, the description covers trigger conditions, exclusions, batching semantics, transaction details, confirmation responsibility, and sibling differentiation. No critical operational gap remains for an agent deciding whether and how to invoke it.

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 defaults and bounds already documented, but the description adds operational meaning: batchSize represents eligible receipts per batch, maxBatches bounds total settlement work, and the per-batch transaction count clarifies the effect of each parameter. This goes beyond the schema's basic descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names the operation as settling all eligible local receipts and submitting Base transactions per batch, making the tool's scope explicit. It also distinguishes itself from settle_ai_rewards, so an agent can tell them apart without opening the sibling schema.

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

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

It states a precise trigger condition ('claim my AIPOU' or 'settle all pending AIPOU'), explicitly says not to use it for a status check, and names settle_ai_rewards as the alternative for a single limited batch. This gives clear when-to-use and when-not-to-use guidance.

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