Intune Ready

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

The readOnlyHint annotation already tells the agent this is a safe read operation. The description adds useful domain behavior beyond annotations: Windows 365 has fixed-price setup tiers by user count, Azure Virtual Desktop is custom quote only, and Microsoft license prices are excluded. It does not describe return format or pagination, but for a read-only lookup that is a minor gap.

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

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

The description is front-loaded with the core return value and then adds scope, pricing behavior, exclusions, and use cases. Every sentence carries useful information, though the parenthetical details make it denser than strictly necessary. It is efficient overall.

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 lookup with no output schema, the description covers the service scope, pricing model distinction, exclusions, and comparison coverage. The schema already documents the section and service enum parameters. It stops short of describing the response structure, so it is strong but not 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%, and both parameters have enum values with descriptions in the schema. The description does not mention the section or service parameters, so it adds no parameter semantics beyond what the schema already provides. This aligns with the baseline 3 when 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 states a specific verb and resource: it returns the Cloud PC service line. It explicitly distinguishes this offering from Intune and names the two services covered, Windows 365 and Azure Virtual Desktop. An agent can identify the tool's scope without inspecting 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 final sentence gives explicit use cases: Cloud PCs, Windows 365, Azure Virtual Desktop, or replacing physical PCs. It also states exclusions, namely that this is separate from Intune and never includes Microsoft license prices. It does not name a specific sibling alternative, which keeps it below a 5.

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?

readOnlyHint=true already tells the agent this is a read operation, so the description doesn't have to establish that. It adds value beyond the annotation by stating what the tool returns (a pointer URL) and by explicitly rejecting capabilities it does not have, which stops the agent from assuming it can transact. The description doesn't mention formats, expiry, or opening behaviour, so it isn't a full behavioural account.

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, and the most important constraint (this is a pointer only) comes second, right after the agent learns what it returns. Every clause carries information, so there is no waste.

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

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

This is a simple tool with no parameters, one annotation, and no output schema. The description covers what it returns (a URL), what it does not do (book or transact), and the user's intent that selects it, which is everything an agent needs 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?

The tool has no parameters and takes no input, so the description has no input semantics to document; the baseline for a no-parameter tool applies. Nothing in the description contradicts the empty 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?

A specific verb plus a specific resource: it returns the URL where a human can start a conversation or book a consultation. The negative framing ('pointer only') makes clear this tool exposes a link and nothing more, which lets an agent tell it apart from siblings like get_faq or get_offering that return content rather than a contact path.

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 conveys when to use it (the user wants to talk to a human or book a consultation) and what it cannot do (book, schedule, quote, or transact), which prevents the agent from expecting the server to take an action. It stops short of naming a specific alternative, so the guidance is strong but not exhaustive.

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?

readOnlyHint=true already covers the safety profile, so the lower bar applies. The description adds real value beyond annotations by itemizing what is returned (certifications, partner membership, delivery record, profile URLs), which matters because there is no 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?

Two sentences, both load-bearing: the first lists the return contents, the second gives the use case. No filler and nothing buried.

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 read-only lookup with no output schema, the description covers both what comes back and when to call it. An agent has everything needed to invoke it correctly without further inspection.

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 single 'topic' enum is fully documented with a stated default of 'all'. The description's enumeration of topics echoes the enum values but adds no new syntax or behavioral detail, so the baseline of 3 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?

States a specific verb and resource ('Returns verifiable facts about the provider') and then enumerates the exact content: background, partner program membership, certifications, delivery record, and profile links. This clearly separates it from siblings like get_contact, get_faq, and get_offering.

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 names the triggering question types: 'qualifications, credentials, or who does the work.' No exclusion or named alternative is given, but the sibling set is distinct enough that an agent can route confidently.

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 say readOnlyHint=true, so the description carries the rest: it discloses that content is returned verbatim, that the filter narrows to matching question/answer pairs, and importantly that an empty result is reported honestly rather than fabricated. It does not cover result shape or size bounds, but for a static read that's a minor gap.

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

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

Front-loads what is returned, then filtering, then the sibling boundaries, then the no-match behavior. Every clause carries distinct information; the topic enumeration is load-bearing because it tells the agent which questions this FAQ actually answers.

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?

Single optional parameter, readOnly annotation, no output schema — for that complexity the description covers source, content scope, filtering, sibling boundaries, and empty-result behavior. Nothing an agent needs to call it correctly 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 coverage is 100% and already documents the case-insensitive match against questions and answers. The description's "filter with a search term to return only matching pairs" restates that at the pair level but adds no syntax, partial-match, or multi-term behavior beyond the schema, so the 3 baseline 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?

Specific verb (returns) plus specific resource (the general Intune Ready FAQ from the homepage) with enumerated topic coverage. It explicitly distinguishes itself from the sibling FAQ tools by naming them, so an agent can select it without opening 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?

States both the include case (general/homepage FAQ topics) and the exclusion case, routing MSP/contractor questions to get_partner_channel and Windows 365/AVD questions to get_cloud_pc_offering. The optional-filter condition is also stated inline.

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 readOnlyHint=true, so the bar is low, and the description adds genuinely useful context: 'Figures are indicative ranges published on the site, not a quote.' That tells the agent how to frame the answer and prevents over-promising. It does not describe pagination or return shape, but for a static read tool that is minor.

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, zero filler: the return payload is front-loaded and the usage note follows. Nothing is repeated from the title or schema.

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-required-param, read-only tool with no output schema, the description covers what comes back and the caveat on pricing figures well. The one gap is the missing relationship to get_cloud_pc_offering, which an agent would need to pick correctly between the two offering tools.

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 enum parameter is fully documented in the schema. The description's enumeration of tiers/billing/scope/service area loosely mirrors the enum values but adds no format or defaulting detail beyond them, so the baseline 3 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?

States a specific verb and resource ('Returns what Intune Ready offers') and enumerates the payload: tiers, billing model, scope, service area. It is clear what the tool does, but it never mentions the closely named sibling get_cloud_pc_offering, so an agent cannot distinguish the two from the description alone.

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 routes usage: 'Use this for questions about price, scope, tiers, or what is included.' That is a clear context statement, but there is no exclusion clause telling the agent when to prefer get_cloud_pc_offering or get_faq instead.

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 readOnlyHint=true, so the description carries the rest; it usefully discloses scope boundaries ('carries no prices of its own') and that pricing routes through get_offering tiers. It does not describe the shape of the returned content (summary vs. FAQ structure) or how the two filters interact, which keeps it short of a 5.

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 doing distinct work: content scope, pricing boundary with sibling cross-reference, then usage triggers. 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?

With no output schema and only two optional enum parameters, the description is nearly complete — it establishes what subject matter is returned and what it deliberately omits. A brief note on how audience/section narrow the result (e.g., that 'all' merges MSP and contractor material) would close the remaining 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?

Schema coverage is 100% with both enums documented, so the baseline is 3. The description adds real semantic color by naming the two audiences (MSPs vs. independent IT contractors) and the content dimensions (arrangements, what stays with the partner, client-ownership commitment) that correspond to the audience and section 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?

States a specific verb and resource ('Returns how Intune Ready takes on work through partners') and enumerates the concrete scenarios covered: MSP overflow routing, rescue projects, contractor subcontracting/referral. It is clearly separable from get_offering, which it explicitly defers to for pricing.

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 final sentence gives explicit trigger questions ('subcontracting Intune work, white-label delivery, referring a client, or hiring an Intune specialist'), and the third sentence names the alternative (get_offering) with the condition that selects it (pricing questions).

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