Tewdy

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

Annotations already declare readOnlyHint and idempotentHint. The description adds value by disclosing that authentication is required and that the tool produces no acceptance/payment side effect, with those actions deferred to an external site.

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, the key behavioral constraint is next, and the auth requirement closes it. 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 single-parameter lookup tool with safety annotations, the description is largely complete: it covers purpose, external flow, auth, and non-mutation. It stops short of explaining the return format or possible failure cases, but this is minor given the tool's simplicity.

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 quote_id at all. The parameter name is self-explanatory, but the description fails to compensate for the schema's lack of explanatory context, such as where to obtain the quote_id.

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: 'Look up a quote and return a deep link.' It clearly differentiates itself from action-oriented siblings by explicitly saying it 'deliberately does not accept or pay,' and the title reinforces the deep-link 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 gives clear usage context: quote acceptance and payment happen on tewdy.com via the deep link, not in chat. This tells the agent when not to attempt acceptance/payment in chat, though it does not explicitly name alternative sibling tools for those actions.

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 the tool read-only and idempotent, and the description adds valuable behavior beyond that: transacted prices are preferred only when at least 5 samples exist, otherwise listed prices are used, and the exact return statistics are enumerated. This makes fallback behavior and output shape predictable without an output 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?

Three sentences with no fluff: purpose first, data-source behavior second, return values third. Every sentence provides non-obvious information needed to use the tool correctly.

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 low-complexity read-only tool with no output schema, the description covers purpose, optional filtering, fallback logic, and return fields. The main remaining gap is the ambiguous semantics of the currency parameter and lack of explicit empty/no-data behavior, so it is near-complete but not perfect.

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% schema description coverage, the description must carry the semantic burden. It clarifies 'category' as the service category and 'location' as an optional filter, which is useful. However, it does not explain the role of the optional 'currency' parameter as an input, and it gives no value format or vocabulary guidance for the free-text string 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 opens with a specific verb and resource: 'Get aggregated price ranges for a service category'. This clearly differentiates it from sibling tools like submit_quote, accept_quote, or get_provider_details, and the optional location filter further narrows its 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 makes the intended use clear: use this when you need aggregate price statistics for a category, optionally narrowed by location. It does not explicitly name alternatives or exclusions, but the use case is unambiguous enough that an agent can select it 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 readOnly/idempotent annotations, the description discloses the projection methodology (baseline hourly rates from existing providers), scenario assumptions (40%/60%/80% utilisation), and a key caveat that results are ranges, not guarantees. This gives an agent a clear picture of what the tool will and will not return.

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 carry the full meaning with no filler: the first states the action and scope, the second explains method and output. The critical caveat ('not guarantees') is included without extra prose.

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 read-only estimator, the description covers the estimation basis, scenarios, and output type despite lacking an output schema. It is slightly incomplete on how weekly versus monthly projections are structured and on the role of optional parameters, but annotations plus the scenario list make it close to self-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?

With 0% schema description coverage, the description must explain the parameters, but it only mentions category and implicitly refers to hours through 'weekly and monthly' projections. It does not clarify how currency or location are used, nor how hours_per_week is interpreted, leaving most parameter semantics to property names and constraints.

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 ('Estimate') with a concrete resource (weekly and monthly earnings for a Tewdy provider in a category) and a clear output type (ranges, not guarantees). It distinguishes this from the sibling get_category_pricing by framing it as a projection tool built on rate baselines rather than a pricing lookup.

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 purpose strongly implies the tool is for earnings-projection questions, but the description never explicitly states when to use it versus get_category_pricing or when not to apply it. No alternative tools or exclusion conditions are named, so usage guidance is only inferred.

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 the tool as readOnlyHint and idempotentHint, and the description reinforces this with 'Read-only — no state changes.' It adds value by disclosing the authentication requirement and the status/count information returned. No contradictions with annotations exist.

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

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

Two sentences, front-loaded with the core action and resource, followed by a critical read-only/authentication note. Every word earns its place and there is no redundancy with the schema. The structure is ideal for quick agent parsing.

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 reasonably conveys the main return content: quote requests with statuses and quote-response counts. It also covers authentication and read-only behavior. It could be slightly more complete by mentioning pagination/sorting defaults, but the schema already documents those parameter boundaries.

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 five parameters. It does partially by enumerating the status values and noting status-related output, but it does not explain limit, offset, sort_by, or sort_order semantics. The description adds little meaning beyond the schema's enums and property names.

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 ('List') and resource ('authenticated user's quote requests'), with explicit scoping. It also clarifies the data included (status, quote-response counts), which distinguishes it from siblings like list_inbound_requests. The title reinforces the intent, leaving no ambiguity about what the 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 clearly establishes the context: this lists the authenticated user's own quote requests. It does not explicitly name alternative tools or state when not to use it, but the 'my'/'authenticated user's' framing inherently differentiates it from list_inbound_requests. The read-only note also implies this is for viewing, not modifying.

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 and idempotentHint=true, and the description's 'Look up' language is consistent with them. It adds meaningful context by disclosing both accepted identifier forms and the scope of the returned profile, although it does not cover error behavior. No annotation contradiction.

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, accepted inputs, and returned data. The most important disambiguation (which identifiers are valid) is front-loaded and every sentence contributes actionable 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?

For a single-parameter lookup with read-only/idempotent annotations and no output schema, the description provides enough detail to invoke the tool correctly: what to pass and what to expect back. No missing fields or workflow information is needed for a first call.

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 0%, so the description carries the full burden for `provider_id`. It explains that the parameter may hold either a Tewdy UUID from search_providers' `id` or a tewdyTag handle, gives an example, and specifies that the handle appears as `tewdyTag`. This fully compensates for 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?

States a specific verb ('Look up'), a clear resource ('full public profile for a Tewdy provider'), and enumerates the returned fields. It also names the sibling search_providers as the source of identifiers, so an agent can tell it apart from the search/list 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?

Gives concrete input context: the tool accepts either the Tewdy UUID returned as `id` by search_providers or a tewdyTag handle, with an example. It does not explicitly state when-not-to-use it versus siblings, but the guidance to source identifiers from search_providers is actionable and implies the intended workflow.

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 readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds useful behavioral context beyond that by specifying the response shape (slug, name, description, businessType) and the optional business_type filtering behavior, which helps the agent know what to expect.

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 three focused sentences with no filler. It front-loads the core purpose, then delivers return-value information and usage guidance in a compact, well-structured 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?

For a simple read-only tool with one optional parameter and no output schema, the description covers purpose, output format, usage context, and parameter semantics. Nothing critical is missing for an agent to call this 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?

Schema description coverage is 0%, so the description must compensate. It does so by naming the optional business_type filter and giving concrete examples ('individual', 'company'). It doesn't enumerate all valid values, but for a single optional parameter with no required fields, this is adequate semantic guidance.

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 verb ('List') and resource ('service categories Tewdy supports'), with concrete examples. It also names the exact returned fields (slug, name, description, businessType), making the tool's purpose unambiguous and distinguishable from siblings like search_providers.

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 tells the agent when to use this tool: 'Use this to map a free-text user request to a known category before calling search_providers.' This gives both a clear usage context and a direct reference to the downstream alternative, which is exactly the kind of guidance needed.

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 and idempotentHint=true; the description reinforces this with 'Read-only' and adds 'Requires authentication' plus the matching-behavior statement. It adds context beyond annotations without contradiction, though it doesn't disclose pagination or return specifics.

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-loads the core purpose. Every sentence adds scope, filter guidance, safety, or usage. The minor redundancy with annotations (Read-only) does not hurt.

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?

It covers what the tool does, who it's for, available filters, read-only behavior, and authentication. With no output schema, it doesn't describe the return format, but the schema covers parameter constraints. Given the complexity, this is mostly complete but leaves some param-to-filter mapping to the agent.

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 names filter groups (category, location radius, budget range, urgency) that map roughly to category_id, radius_km/lat/lng, min/max_budget, and urgency, but it doesn't describe individual parameters or their constraints. This is helpful but incomplete for a 9-parameter tool.

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 ('list') and resource ('open quote requests'), scoped to the authenticated provider's capabilities and service area. It clearly distinguishes this from siblings like get_my_requests or request_quote by positioning it as the tool to find work to quote 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?

It explicitly says 'Use this to find work to quote on', giving clear intended context. However, it does not name alternatives or provide when-not-to-use guidance, so it stops short of full exclusion 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 indicate a non-read-only mutation. The description adds that authentication is required, which is not in annotations. It doesn't detail side effects like notifications or rate limits, but given the simple nature, this is acceptable.

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

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

Two sentences, front-loaded with the action, and includes necessary context in a compact 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?

For a simple two-parameter tool with no output schema, the description covers the main usage, authentication, and chat_id source. It doesn't mention success/failure behavior, but that might be inferred from standard API responses.

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 description explains that chat_id is the same id from get_my_requests or accept_quote, which adds semantic meaning beyond the schema. It doesn't explain text, but text is self-explanatory.

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 'send' and resource 'chat message', and specifies the context 'into an existing Tewdy thread'. It distinguishes from siblings which are about quotes/requests, not messaging.

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 context on when to use (typically after quote submitted/accepted) and where to obtain chat_id. It doesn't explicitly mention when not to use, but given no other messaging tools, the guidance is adequate.

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 substantial behavioral context beyond the annotations: it returns a deep link, requires user review and manual publish, does not auto-post, and requires authentication. It also clarifies that acceptance and payment never happen in chat. This is rich, non-obvious behavior that an agent needs to know, and it aligns with the idempotentHint annotation.

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 three sentences with no filler. It front-loads the core purpose, then explains the deep-link workflow, and finally states the critical no-auto-post and authentication caveats. 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 covers the output (deep link), the workflow (user reviews and publishes), and authentication, which is good. However, with 8 parameters and no schema descriptions, it lacks field-level guidance such as accepted date formats, currency codes, or how to map user input to budget fields. It is adequate for understanding the tool's role but not fully complete for reliable 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 0%, so the description must compensate for parameter meaning, but it does not explain any of the eight parameters. It only says 'all fields pre-filled' and 'from the user's description,' which gives a general sense but leaves urgency, budget_min/max, preferred_date, currency, and location_name undefined. The agent would have to infer semantics from field names alone.

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: 'Compose a Tewdy quote request from the user's description.' It also clearly distinguishes this from posting by noting it 'deliberately does not auto-post' and returns a deep link for the user to review and publish. This separates it from siblings like submit_quote and accept_quote.

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: this tool is for composing a quote request and returning a deep link, not for final posting. It says quote acceptance and payment confirmation happen on tewdy.com, which implies when not to use this tool. However, it does not explicitly name alternative sibling tools or state 'use submit_quote instead,' so it falls just short of full explicit routing.

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 cover readOnly, openWorld, and idempotent behavior. On top of that, the description adds concrete behavioral details: results are ranked and include name, rating, response time, and profile tag, which is useful 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?

Two focused sentences with no filler; the purpose, search criteria, and return fields are front-loaded and each sentence adds 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?

Adequate for a basic provider search: no required params, and description gives examples, search dimensions, and output fields. However, with no output schema and several optional params undocumented—especially query—an agent cannot fully determine all valid invocation patterns.

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 has 0% description coverage, so the description must compensate for seven parameters. It only broadly clarifies 'category and/or location'; query and limit are not addressed, and individual params like radiusKm and locationName are left underspecified.

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?

States the verb 'Search', the resource 'Tewdy service providers', gives concrete examples, and specifies filtering by category and/or location. This clearly distinguishes it from detail-lookup siblings like get_provider_details.

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?

Provides a clear use context: find providers by category and/or location, returning a ranked list. It does not mention exclusions or explicitly compare with get_provider_details, but the intended usage is evident.

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 significant behavioral context beyond the annotations: it is a write operation (non-readOnly), requires authentication, and is explicitly not a financial commitment, with acceptance going through a deep-link confirmation. It does not mention side effects like edit/withdraw capabilities or idempotency, but the provided details are substantive.

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 that front-load the purpose ('Submit a quote on an open quote request'), then add provider actions and key behavioral caveats. Every sentence carries meaning, with no redundancy or 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?

For a 9-parameter mutation tool with no output schema and no parameter descriptions, the description covers the core action and important caveats but omits details about required identifiers, currency expectations, business context, and any return/response behavior. It is adequate for a simple call but not fully complete for an agent operating without additional context.

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

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

With 0% schema description coverage, the description must compensate, but it only explains a subset of parameters: estimated price, optional duration, availability, and message. It leaves quote_request_id, provider_type, currency, business_id, and terms_conditions unexplained, so an agent still has to infer their meaning from names and constraints.

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 ('Submit') and resource ('a quote on an open quote request'), and explains the provider's role in setting price, duration, availability, and message. It is clearly distinct from sibling tools like request_quote (creating a request) and accept_quote (accepting one), even without naming them.

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 specifies the tool applies to 'open quote request' and explains the subsequent acceptance flow, implying the tool is only for submission, not acceptance. It provides clear context but does not explicitly name alternatives or state when not to use it beyond the 'open' qualifier.

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