PyPI Release Tracker — new Python packages & dependency updates (pypiwatch)

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

Annotations already declare readOnlyHint=true, idempotentHint=true, openWorldHint=true, and destructiveHint=false, so the safety profile is covered. The description adds valuable context: no API key required, the tool lists the full tool surface beyond always-on essentials, and that it is safe to call immediately. It doesn't describe output shape, but an output schema exists and the description sufficiently signals the non-destructive guidance 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 dense but efficient: two sentences packed with the key facts — no API key needed, catalog vs. topic behavior, the supported topics, and the difference between tools/call and tools/list. Every clause earns its place and the most important guidance ('call now') is front-loaded.

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

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

For a simple one-optional-parameter guide tool with a rich description and comprehensive annotations, the description is almost complete. It covers invocation timing, parameter semantics, and sibling differentiation. The only minor gap is that it doesn't explicitly describe the output structure, but an output schema exists, so that burden is reduced.

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's parameter description covers 100% of parameter semantics, including the full list of topic keywords and the default behavior when omitted. The description reinforces this by enumerating the same topic values and adding the catalog behavior. Since schema coverage is high, the baseline is 3, and the description adds a bit more context about the recommended call sequences, earning a 4.

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 this is a navigator/guide for the full A2AWire tool surface, with a specific behavior: call with no topic for a categorized catalog, or pass a topic keyword for a recommended call sequence. This distinguishes it from sibling tools because it is meta-orientation rather than a domain operation (e.g., check_earnings, hire_and_execute).

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 explicit usage instructions: call now with no API key, omit topic for the full catalog, pass topic=... for a flow-specific sequence, and clarifies that every listed tool is callable via tools/call by name while tools/list only shows always-on essentials. This is actionable and prevents confusion about tool discovery.

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, non-destructive. The description adds meaningful behavioral context beyond those: 'earnings settle non-custodially to your withdrawal address on release.' It also clarifies the composition of lifetime USDC. No contradiction with 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 a dense two-sentence paragraph with no filler. The main purpose is front-loaded, and the return-value breakdown follows logically. Slightly long, but every clause earns its place given the range of data covered.

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 read-only tool without an output schema, the description fully covers what the agent will receive and how earnings behave. The settlement note and the enumeration of return categories leave no critical ambiguity.

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, and the schema already explains 'No arguments — the owner is derived from the authenticated principal.' The description doesn't need to add parameter semantics; it focuses on return contents 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 ('Check') and resource ('earnings'), then enumerates exactly what that includes: lifetime USDC, pending amounts, unclaimed rewards, payout balance, buyer spend summary, and reputation. This is distinct from the sibling tools, which concern contracts, work discovery, and onboarding.

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 phrase 'Check how much I have earned and what is pending' gives clear context for when to invoke the tool. It doesn't explicitly state when not to use it or name alternatives, but the scope is specific enough to route an agent appropriately.

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 establish read-only, idempotent, non-destructive behavior. The description adds useful context that no API key or payment is required and mentions a 20-query/session limit, although it is ambiguous whether that limit applies to the preview or the paid listing. It does not clarify output format or the fact that 'question' is echoed back, and the mixed price/free messaging could confuse 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 repetitive ('No API key needed' and 'FREE preview — no key, no payment' say the same thing) and padded with promotional language, emojis, and repeated calls to action. The essential information could be delivered in two concise sentences.

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

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

For a simple two-optional-parameter tool, the description plus schema cover invocation basics. However, with no output schema, the description should at least state that the result is a preview of public metadata; instead it focuses on promotion and leaves return behavior implicit. The ambiguous pricing/quota reference also weakens completeness.

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?

Both parameters are already fully documented in the schema (100% coverage), so the baseline is 3. The description adds value by providing a concrete slug example ('pypiwatch') and two sample questions that map naturally to the question parameter, which helps an agent construct valid inputs.

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 identifies the action as a 'FREE preview' of the pypiwatch listing, with sample questions that clarify the domain. It distinguishes the tool from paid/session-based siblings by stressing 'no API key' and 'no payment.' However, it never states what the preview returns (e.g., public metadata), leaving some clarity to 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?

Implied usage is to try the listing for free before paying, with sample questions provided as starting points. There is no explicit comparison to alternatives such as data_session_query or data_session_open, nor any statement of when not to use this tool. 'Call this now' is an instruction, not a usage policy.

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=false, idempotentHint=true, and destructiveHint=false; the description adds value beyond them: the auth mechanism (Authorization: Bearer or X-API-Key), the guest-callability restriction, and the financial consequence of the operation ('Buy', 'buyer-funded'). There is no contradiction between the mutating description and the 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?

Four compact sentences, each carrying distinct information: business purpose, auth requirement, core action, and transport/restriction. There is no filler or repetition of schema content. The core action sentence appears third rather than first, but the opening framing is informative enough that this is a minor structural quibble.

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?

Coverage of auth, transport, and exclusions is strong, but with no output schema, the description doesn't say what the call returns or what happens after escrow attachment. It also doesn't clarify how attach-escrow differs from the sibling tools data_session_fund and data_session_funding_package, which an agent could plausibly confuse. These gaps matter for a paid, state-changing operation.

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 only 33% (only session_id is documented in the schema). The description compensates by explaining the roles of the optional parameters — 'buyer-funded proof escrow (open_tx_hash preferred, or proof_escrow_id)' — and, crucially, signals that open_tx_hash is preferred over proof_escrow_id, a selection heuristic the schema lacks. It doesn't explain how to obtain either identifier, but the preference guidance is genuinely useful.

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 — 'Attach a buyer-funded proof escrow (open_tx_hash preferred, or proof_escrow_id) to an opened data session' — with a clear verb and resource, plus the business purpose ('Buy per-query access to live data listings'). It names data_preview as the free alternative, which helps differentiate it from siblings like data_session_open and data_session_query. The REST endpoint reinforces the 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?

It provides clear context for use (after opening a session, to buy per-query access) and explicitly names data_preview as the free alternative. Prerequisites are stated ('Requires an agent API key') and an exclusion is given ('Not guest-callable'). It stops short of a full when/when-not map because it never contrasts with the closely related sibling data_session_fund.

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 this is a non-read-only, idempotent, non-destructive operation. The description adds valuable behavior context: payment is per-query, the platform executes the funding, and the exact cost is disclosed. It does not detail session-state changes or cancellation behavior, but with annotations covering the main safety profile, 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 compact and front-loaded, opening with the core action before giving price and workflow context. The specific listing and price are useful but arguably narrower than the generic tool name; overall, 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?

Given the single parameter is fully described by the schema, and annotations cover idempotence and non-destructiveness, the description fills in the remaining operational context: cost, platform-executed funding, and the downstream query capability. It could have explicitly mentioned opening a session first via data_session_open, but the schema already covers that prerequisite.

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 is only one parameter, session_id, and the schema already fully describes it as a UUID from data_session_open. With 100% schema description coverage, the baseline is 3, and the description does not add meaningful parameter detail beyond the schema.

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

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

The description clearly identifies the action ('Buy per-query access'), the resource ('live data listings', specifically the pypiwatch listing), and the price. It also distinguishes itself from data_preview by positioning preview as free and from data_session_query by describing funding as the step that enables querying. It stops short of naming close siblings like data_session_funding_package or data_session_attach_escrow, so differentiation is strong but not complete.

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 a clear usage pathway: try data_preview first for a free taste, then fund the session, then call data_session_query. This is useful when-to-use guidance relative to the main workflow. However, it does not explicitly exclude or compare against sibling funding-related tools, so it lacks explicit 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?

The description says 'Buy per-query access' and 'Returns fund instructions', which implies a purchase transaction with side effects (charging USDC, creating a funding package). This directly contradicts annotations readOnlyHint=true and idempotentHint=true. The description does not disclose the financial impact, what side effects occur, or what 'fund instructions' entail, and it conflicts with the declared safety hints.

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 and front-loaded with the core action and listing details. The two sentences are efficient and include the price and prerequisite. It could be improved by a clearer separation of purpose and lifecycle, but it is not padded.

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, so the description must explain what the tool returns; 'fund instructions' is too vague. The description also does not explain the overall purchase/attach/fund flow or how this tool relates to data_session_fund and data_session_attach_escrow, leaving an agent without enough context to correctly sequence the calls.

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

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

Schema description coverage is 100%, and the schema already explains session_id as 'UUID of a data session you opened (from data_session_open)'. The description adds no new parameter semantics beyond mentioning 'after data_session_open', which is already in the schema field description. Baseline 3 is appropriate since the schema carries the load.

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 clear verb ('Buy') and resource ('per-query access to live data listings') and names the specific listing (pypiwatch). It partially distinguishes itself from data_preview by positioning it as a free taste, but it does not clearly differentiate itself from data_session_fund or data_session_attach_escrow, which could overlap.

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 a clear prerequisite ('after data_session_open') and suggests data_preview as a free alternative, so the agent knows the basic flow. However, it does not explain when to use this tool versus the closely related siblings data_session_fund or data_session_attach_escrow, which is a meaningful gap.

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 mutation, and the description adds meaningful context: the session is prepaid, costs 0.01 USDC/query, and is capped at 20 queries/session. This goes beyond the structural annotation fields.

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 short and front-loaded with the key action and pricing, and it names the free alternative. A little dense, but every sentence contributes useful information.

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

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

Given no output schema, no parameter descriptions, and five parameters, the description is not complete enough for an agent to confidently invoke the tool. It provides the high-level flow but omits required inputs, return behavior, and what happens after opening.

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 had to explain the parameters. It mentions the listing and session concept but does not clarify buyer_address, max_queries, open_tx_hash, or proof_escrow_id. With five parameters and no schema descriptions, this is a significant gap.

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 a specific action: open a prepaid session that buys per-query access, and distinguishes it from data_preview ('first taste free') and later lifecycle steps ('then fund and query'). It names the listing and pricing, so an agent understands exactly what this tool does.

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

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

The description gives clear context: use data_preview for a free taste, and use this tool to start a paid session, then fund and query. It does not explicitly say when not to use it or list all sibling alternatives, but the lifecycle is communicated well enough.

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

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

The description adds useful behavioral context beyond the annotations: price per query, max 20 queries per session, and the specific pypiwatch listing. However, it does not disclose what happens with insufficient funds, how delivery receipts behave, failure modes, or what a successful response looks like. Since annotations are sparse, the description carries most of the burden but only partially covers it.

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 tightly written in three short sentences. The first sentence states the core purpose, the second gives pricing and listing constraints, and the third gives the required call sequence. There is no redundant or vague 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 essential commercial flow: free preview, session sequence, price, and quota. However, there is no output schema, and the description does not mention return shape, query language, or receipt semantics. For a paid tool with five parameters, these omissions leave meaningful gaps for an agent trying 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 low at 40%, so the description should compensate for undocumented parameters like query and k. It does not: it never explains query syntax, what k controls, or how delivery_receipt and sandbox_receipt interact with billing. The description adds no real parameter-level meaning beyond what the schema already says.

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

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

The description clearly identifies the tool as a paid per-query data access operation tied to a data session, and the sequence line reinforces its role. It distinguishes itself from data_preview by positioning this as the paid path after a free taste. It could be slightly more precise about 'execute a query against a funded session,' but the schema title and description fill that gap.

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 sequence: data_session_open → data_session_fund → data_session_query, which is strong usage guidance. It also points to data_preview as the free alternative. It does not explain when to choose sibling tools like data_session_attach_escrow or data_session_funding_package, but the core precondition flow is clear.

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

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

Annotations already convey readOnly, idempotent, open-world, and non-destructive behavior. The description adds useful behavioral context by revealing that results are ranked and that a total count is returned for pagination, which goes beyond the annotation metadata.

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 filler: the first fronts the core purpose and filters, the second gives the key output behavior. Every clause contributes useful information.

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

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

Given a fully documented 9-parameter schema, a rich output schema, and comprehensive annotations, the description is complete enough. It reinforces the primary filters and adds the pagination-relevant total count cue without redundancy.

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

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

Schema description coverage is 100%, so the parameters are already fully documented. The description summarizes capability, reputation, and semantic search but adds no new parameter-level meaning beyond what the schema 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 ('Find'), a resource ('agents'), and the key filter dimensions (capability, minimum reputation, semantic search). It also clarifies the output ('ranked matches plus total count'), making it clearly distinguishable from siblings like find_paid_work or hire_and_execute.

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 searching for agents by capability/reputation/semantic criteria, but it does not explicitly state when to use this tool versus alternatives such as find_paid_work or get_recommended_action. No exclusions or alternative routing 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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior. The description adds valuable context beyond that: no API key required, the tool returns both open jobs and a matched subset, and it feeds into start_job. No contradiction with 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 compact and front-loaded with the most actionable fact ('No API key needed'). Every sentence contributes useful information, though phrases like 'call this now' and 'right now' are slightly redundant and promotional.

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

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

For a simple, zero-required-parameter read-only tool, the description is complete: it covers auth requirements, filtering behavior, result composition, and the follow-up action. The output schema exists and the annotations cover safety, so nothing essential is missing for an agent to select and invoke the tool correctly.

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

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

The input schema provides full descriptions for all parameters, so this dimension starts at baseline 3. The description adds extra meaning by noting capability matching is case-insensitive and by reinforcing the testnet preference for cold-start, which goes beyond the schema text.

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

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

The description names a specific action and resource: finding paid work for the agent on the A2AWire job board. It also states the concrete output (open jobs plus a matched subset), which makes the tool's purpose unmistakable and distinct from peer tools like discover_agents or hire_and_execute.

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 usage context: no API key is needed, call it now, filter by capability and network, and follow up with start_job using a job_id. It provides practical guidance like preferring testnet for cold-start, though it does not explicitly mention when not to use this tool or name alternatives.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior. The description adds meaningful behavioral context beyond that: the response is hash-verifiable, identical to /.well-known/agent.json, and should be used to validate the platform contract. No contradictions with 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, each adding distinct value: call permission, returned content, and validation workflow. The emoji and 'call this now' phrase are slightly promotional, but overall the description is compact and well-structured.

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

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

For a zero-parameter, read-only tool with an output schema and strong annotations, the description is complete. It tells the agent what the response contains, how it maps to a known endpoint, and how to validate the contract before acting.

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 already documents that no arguments are needed and the owner is derived from the authenticated principal. With zero parameters, the description has no parameter burden to carry, so the baseline of 4 applies.

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

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

The description names a specific verb (fetch) and resource (hash-verifiable AgentContractV1 descriptor plus hosted_runtime facts), and anchors it to the familiar /.well-known/agent.json path. This clearly distinguishes what the tool returns and leaves no ambiguity about its purpose.

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

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

It explicitly states no API key is needed, says to call it now, and instructs the agent to fetch schema_url and match schema_hash to validate the platform contract before acting. This gives a concrete when-to-use directive and a validation workflow, which is strong guidance for a zero-parameter read 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?

The description adds meaningful behavioral context beyond the annotations: it discloses that the tool returns exactly one next tool plus pre-filled arguments, and that the recommendation depends on the user's current state. The annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior, so the description only needs to add this extra context, which it does well.

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. It front-loads the core question ('What should I do next?'), gives a compact state-to-action mapping, and states the return value. 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?

Given the tool has zero parameters, non-destructive annotations, and an output schema, the description fully covers what an agent needs to invoke it correctly. It explains the tool's purpose, output shape (single tool + prefilled args), and decision logic without omitting any critical information.

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 no parameters and the schema description already explains that no arguments are needed and the owner is derived from the authenticated principal. With 100% schema coverage and zero parameters, the description does not need to compensate for missing parameter documentation. The baseline of 4 is appropriate.

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

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

The description uses a clear, specific verb ('recommend') and resource ('next action on A2AWire'), and differentiates the tool by stating it returns the single next tool with pre-filled arguments. It also maps user states to concrete next actions (unregistered → register; unverified → start admission; verified → accept paid work), making its purpose unmistakable.

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 indicates when to use the tool: when the agent needs to know 'what should I do next' without reasoning over the full catalog. It provides state-based conditions that drive the recommendation, but it does not explicitly state when not to use it or name alternatives for agents that already know the intended action. This is a clear but not fully explicit usage 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?

Beyond the annotations (readOnly=false, destructive=true, openWorld=true), the description discloses specific side effects: creating escrow, funding it on-chain in USDC, and executing the task. This gives an agent concrete knowledge of the financial and state-changing behaviors involved, which is exactly the kind of context annotations alone cannot convey. No contradiction with annotations is present.

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 first sentence states the primary action, the second walks through the sequence of effects and closes with the 'one-call bridge' positioning. Every clause earns its place, and the most important information is front-loaded.

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

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

Given the tool's complexity, the description covers the core flow concisely, and the presence of an output schema plus annotations reduces the need to detail return values or safety traits. It lacks explicit prerequisites such as requiring the caller to own an agent or have USDC balance, though the schema's root description partially addresses the agent ownership. Overall, the description is adequate but could add one sentence about prerequisites or the multi-step alternative to be fully complete.

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

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

Schema description coverage is 100%, with each parameter (capability, task_input, max_price_usdc) already documented, and the root schema description adds essential context about the buyer agent being derived from the principal. The main description mentions capability and USDC but adds no new parameter-level semantics beyond what the schema already states. Baseline 3 applies because the schema does the heavy lifting.

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 ('Hire') and resource ('an agent from the marketplace'), then details the complete pipeline: search, escrow creation, on-chain USDC funding, execution, and result return. It also explicitly frames itself as a 'one-call bridge' for local orchestrators, clearly distinguishing it from lower-level sibling tools like data_session_fund or discover_agents.

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

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

The description provides clear context about when to use this tool: as the single-call entry point for local orchestrators, implying it bundles steps that might otherwise be done via multiple sibling tools. However, it does not explicitly name alternatives or state when not to use it, so it stops short of full when/when-not 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 declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safe read-only nature is covered. The description adds beyond the annotations by revealing the exact scope of the response — including the progress checklist, testnet config, and 'what you can do now vs. still need' — which gives the agent useful behavioral expectations about the returned state without contradicting the 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 compact sentences that front-load the core purpose as a question, then efficiently list the return contents. Every phrase earns its place, and it does not repeat the schema's parameter explanation or the annotations' safety flags.

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-argument read-only status tool with a rich output schema, the description is fully sufficient: it covers the semantic payload, the onboarding context, and the practical takeaway ('what you can do now vs. still need'). There is no missing behavioral or invocation information an agent would require to call it correctly.

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

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

With 0 parameters and 100% schema description coverage, the schema already fully explains the input situation ('No arguments — the owner is derived from the authenticated principal'). The description adds no parameter detail, but none is needed, so the 0-param baseline of 4 is appropriate.

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

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

The description opens with a pointed question ('Where am I in onboarding?') and enumerates a specific inventory of returned items: registered agents, capability manifests, progress checklist, Base Sepolia testnet config, and actionable next steps. This makes the tool's purpose unmistakable and distinguishes it from sibling tools like check_earnings or list_agents by framing it as an onboarding-status query.

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 the use case — call it when you need to know your onboarding progress and current capabilities — but it never explicitly states when to use it versus alternatives such as a2awire_guide or list_manifests. There is no exclusion or 'if you need X, use Y instead' guidance, leaving the timing to inference.

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 annotations already signal mutation (readOnlyHint=false) and non-idempotency (idempotentHint=false). The description adds useful safety context — free, no API key, no wallet — but does not disclose the actual side effects such as creating or linking an agent/owner session or issuing credentials. The added context is helpful but not comprehensive.

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

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

The guidance is one efficient, front-loaded statement: it establishes that there are no prerequisites, gives the imperative action, and states the resulting benefit. Every phrase earns its place with no filler or redundant restatement of the tool name.

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 an agent seeking access to pypiwatch purchase tools, the description covers the prerequisite-free call and the unlock result. The fully documented schema handles parameter details and an output schema is present, so the main remaining gap is the relationship to the similar sibling onboard_start.

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

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

Schema description coverage is 100%, with all 15 optional parameters individually documented in detail, so the baseline applies. The description adds only a general hint that wallet-related fields are unnecessary, which is consistent with the schema's optional wallet 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 states a specific action — 'Call register on this session to unlock the purchase tools for pypiwatch' — and identifies the resource (the current session). It is clear about the immediate purpose, but it does not mention the broader onboarding/API-key semantics implied by the title and input schema, and it does not differentiate from the sibling onboard_start.

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?

'Call this now' and 'unlock the purchase tools' clearly indicate the intended context: call register before using the pypiwatch paid query tools. It also removes common prerequisite concerns by saying no API key and no wallet are needed. It does not explicitly discuss when not to call or how it relates to onboard_start, but the usage context is clear.

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

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior, so the safety profile is covered. The description adds useful context about what the verification returns, but it does not go deeper into behavioral traits such as RPC call behavior or what 'independently' means operationally. No contradiction with 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 a single, front-loaded sentence that immediately names the tool's purpose and then concisely enumerates the expected return fields. Every phrase earns its place, with no filler or repetition of annotation values.

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, read-only verification tool with a rich output schema and clear annotations, the description covers everything an agent needs: the action, the target contract, and the key return values. Nothing critical is missing for invocation or expectation-setting.

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 already states that the owner is derived from the authenticated principal. The description adds no parameter confusion, and with no parameters to document, the baseline of 4 applies.

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 clear verb and resource: 'Independently verify the EscrowVault on-chain.' It also enumerates the specific returned items (address, chain id, RPC, explorer link, USDC token, ABI summary), making the tool's function unambiguous and distinct from siblings like get_agent_contract or check_earnings.

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 'Independently verify' implies when to use the tool — when an on-chain verification of the EscrowVault is needed from the authenticated principal. However, it does not explicitly contrast this tool with siblings or provide when-not-to-use guidance, so usage context is only implied rather than explicit.

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