Tabela FIPE

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

The description adds useful behavior beyond the annotations: config header yields a permanent connection, while passing a token yields a session-only login, and no args returns a link. It does not fully spell out side effects or success/failure return values, but annotations already cover idempotency and non-destructiveness.

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 and front-loaded, with no fluff, but the long single sentence with parenthetical clauses and multiple alternatives could be structured into clearer separate instructions. Still, every part adds necessary 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 one-parameter auth tool with no output schema, it covers the no-arg return (the link), the token-paste path, and the persistent-config alternative. It doesn't state the response on a token success/failure, but the invocation guidance is sufficient for an agent 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% schema coverage, the description carries the full burden for the optional `token` parameter. It explains that token is a JWT/access token pasted by the user and how to pass it, compensating well 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?

The description clearly identifies the tool as MCP.AI authentication for IDE agents, with a concrete browser-login + access-token flow and two invocation paths (no args for a link, token for login). This specific verb+resource is unambiguous and easily distinguished from the unrelated calculo_* sibling 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?

It explicitly differentiates the persistent config-header approach ('best... permanent, non-expiring') from the session-only paste/login path, and states exactly when to call with no args versus with { token }. This gives the agent clear selection criteria for both setup and invocation.

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

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

Annotations already establish this is read-only, idempotent, and non-destructive. The description adds useful behavioral detail beyond that by specifying the two main response states: authenticated:true with empty pending[] when all providers are connected, and connect_url plus per-install URLs when credentials are missing. This helps an agent predict 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 compact, front-loads the core purpose, and then adds only the essential conditional details. Every sentence contributes meaningful information, and 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?

For a zero-parameter read-only status tool with no output schema, the description is complete enough. It tells the agent what information will be returned, what the success condition looks like, and what happens when credentials are missing. The low complexity means no additional guidance is required.

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

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

The tool has zero parameters, so the description does not need to explain any input semantics. The baseline of 4 applies because there is no parameter burden at all.

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

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

The description clearly states the tool's purpose: returning connection status and URLs. It distinguishes connect from its sibling authenticate by framing it as a status/read operation rather than an action, and the conditional output descriptions reinforce this.

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 clear that this is the tool to call when checking connection state or getting URLs. It does not explicitly mention alternatives like authenticate, but the context strongly implies connect is for status checking rather than initiating authentication, so usage is clear without being fully explicit.

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 readOnly and idempotent. Description adds return format and example, enhancing transparency 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 concise sentences, front-loaded with action and output format. No redundant 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?

Partially explains output and links to next tool, but lacks parameter details and overall flow context given no output schema.

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

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

Schema coverage is 0%, yet description gives no parameter descriptions. Parameter names are somewhat self-explanatory, but the description fails to compensate for the lack of schema documentation.

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

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

Description clearly states it lists model-years with fuel for a model, with an example and direction to fipe_preco. It distinguishes from sibling FIPE tools but does not explicitly connect to the required parameters.

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?

Specifies to use the result in fipe_preco, providing clear context. No exclusions or when-not-to-use guidance, but sufficient for a sequential 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 declare readOnlyHint and idempotentHint, covering safety. The description adds that the search is fuzzy and returns a list of candidates, which is mildly behavioral but not extensively beyond annotations.

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

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

The description is a single sentence with a helpful example, making it concise and to the point. It is appropriately sized but could be slightly more structured with separate lines for clarity.

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 two parameters and no output schema, the description provides a reasonable overview of purpose and return structure. However, it lacks detailed parameter explanations and does not specify query syntax or constraints, leaving gaps for an agent to infer.

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

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

The input schema has 0% description coverage and the tool description does not explain the parameters 'q' or 'tipo_veiculo' explicitly. The example hints at 'q' being a model name, but no types or formats are mentioned, leaving the agent without sufficient 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 fuzzy search by model name, provides examples, and indicates it returns candidates for subsequent queries. However, it does not explicitly differentiate from sibling tools like fipe_modelos, which might also list models.

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 this is a preliminary step before fipe_anos or fipe_preco, giving some usage context. But it lacks explicit when-to-use or when-not-to-use guidance, or mention of alternatives like fipe_marcas.

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 destructiveHint, so the description adds value by specifying the return format (list of objects with referencia, mes, valor) and ordering (oldest to newest). There is 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 consists of two concise sentences. The first defines the tool's purpose and value proposition, and the second specifies the return format and ordering. No unnecessary information is included.

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

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

Given the complexity (3 parameters, no output schema), the description is incomplete: it does not explain input parameters or clarify how to use them. It also does not differentiate usage from sibling tools like 'fipe_preco' or 'fipe_buscar'.

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

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

The schema has 0% description coverage, and the tool description does not explain any of the three parameters (codigo_fipe, ano_modelo, combustivel). This is a significant gap as the description fails to add meaning beyond parameter 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 clearly states the tool returns a time series of vehicle prices over reference months, specifically calling it a 'depreciation curve' and distinguishing it from consulting the website. It uses a specific verb ('retorna') and defines the resource (historical prices).

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 use for historical price data versus current price tools like 'fipe_preco', but does not explicitly state when to use this tool over siblings. No exclusions or alternative recommendations 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 indicate readOnly, idempotent, and non-destructive. Description adds return format and role in workflow, but no additional behavioral traits beyond what annotations imply.

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 concise sentences that efficiently convey purpose, parameter, output, and context without any wasted words.

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

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

Tool has low complexity (1 param, no output schema). Description sufficiently explains input, output, and its place in the workflow.

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% coverage, but description fully explains the sole parameter (tipo_veiculo) with mapping to vehicle types, compensating completely.

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

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

Description clearly states it lists brands for a vehicle type with specific codes (1=carro, 2=moto, 3=caminhão) and return format. Distinguishes from sibling tools like fipe_modelos and fipe_preco.

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 it is the first step of a drill-down to price, implying when to use it. However, does not explicitly exclude alternatives or provide when-not scenarios.

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 destructiveHint. The description adds the return format but no additional behavioral context beyond what annotations imply, earning a baseline score.

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

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

The description is extremely concise with no superfluous words, front-loading the key action and output format in two 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?

While the tool is simple and annotations cover safety, the description omits parameter semantics and lacks an output schema. The return structure is mentioned but insufficient for a complete understanding.

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

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

The schema has zero description coverage, and the description does not explain the purpose or possible values of the two parameters, especially tipo_veiculo, leaving the agent without critical information.

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

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

The description clearly states that the tool lists models for a brand, specifies the return structure, and references the prerequisite tool fipe_marcas, making the purpose unambiguous.

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 explicit guidance to use the codigo_marca from fipe_marcas, establishing a clear dependency. However, it does not explain when not to use this tool or suggest alternatives for similar functionality.

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 declare readOnlyHint=true and destructiveHint=false, so the safety profile is clear. The description adds value by detailing the two ways to identify the vehicle, the default for referencia, and the return object structure, which goes beyond 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 concise, consisting of two sentences in Portuguese. It front-loads the purpose and efficiently covers identification methods, default behavior, and return format without wasted words.

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

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

Given the tool has 7 parameters, no output schema, and no enums, the description is fairly complete. It explains both identification methods, the optional referencia parameter, and the return object structure. However, it does not mention error cases or constraints like the format of codigo_fipe.

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 compensates by explaining two identification methods: using codigo_fipe or a tuple of five parameters (tipo_veiculo, codigo_marca, codigo_modelo, ano_modelo, combustivel). It also clarifies that referencia is optional and defaults to the current month. However, it does not provide individual parameter types or 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 'Preço de referência FIPE de um veículo' (FIPE reference price of a vehicle), which uses a specific verb and resource. It clearly distinguishes from sibling tools like fipe_anos, fipe_historico, etc., by specifying that this tool retrieves the current reference price.

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 explains two identification methods and the default for referencia, but does not provide explicit guidance on when to use this tool versus alternatives (e.g., fipe_historico for historical prices) or when not to use it. The context is clear but lacks exclusions.

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

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

Annotations already provide readOnlyHint, idempotentHint, and destructiveHint. The description adds valuable behavioral context: the date range (~2001→hoje), the return structure with fields (codigo, mes, is_current), and the meaning of is_current. No contradictions.

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

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

The description is two sentences, front-loaded with purpose, then return format and usage. Every sentence adds value without 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?

Given no parameters and no output schema, the description is complete. It explains what the tool does, what it returns, and how to use it with sibling tools. No missing 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 zero parameters, so baseline is 4. The description adds meaning beyond the schema by explaining the return format and how the output is used, which is sufficient.

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

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

The description clearly states the tool lists reference months of the FIPE table from ~2001 to today, and specifies the return format. It distinguishes itself from siblings by explaining how its output (codigo) is used as a parameter in other tools like fipe_preco and fipe_historico.

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 explains when to use the tool (to get reference months for price queries) and how to use its output (use codigo as referencia in other tools). While it does not explicitly list when not to use it, the context is clear and sufficient for an agent to decide.

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, the description discloses important behaviors: invoke runs an MCP even when it is not installed, does a one-off run without adding the MCP to the toolkit, returns a connect link when credentials are needed, returns a checkout/top-up link when payment is needed, and requires workspace owner/admin for write operations. The description enriches the annotations and does not contradict them.

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 identity and the core flow, and nearly every sentence carries useful guidance. However, it is one dense, wall-of-text paragraph with mixed language ("pontualmente") and heavy inline emphasis, which makes the many action alternatives hard to scan and parse.

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 complex 23-parameter, 14-action facade with no output schema, the description is remarkably complete: it covers the core flow, one-off invoke semantics, auth/credential/payment behavior, permission requirements, installed flags, the prompt library, and most action outcomes. The main gaps are the resume action and return-shape details for a few actions, but the overall guidance is sufficient for correct invocation in most cases.

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 does a lot of compensating work: it maps action values such as search, describe, invoke, install, list_tools, publish_prompt, and explains tool_id, arguments, and prompt-related intent. However, several parameters and enum actions remain unexplained, including resume, limit, immediate, tier_slug, cancel_reason, report_context, conversation, request_name, and request_details, leaving agents under-specified for those paths.

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 the official mcp.ai marketplace: the in-platform catalog of MCPs/tools and the way to run them. It states the core discovery→describe→invoke flow, distinguishes the prompt-library subdomain from the MCP flow, and makes it clear this is a marketplace orchestrator rather than one of the sibling calculator/authenticate tools.

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

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

The description gives explicit when-to-use guidance: use install only to make an MCP permanent, prefer invoke for one-off use, use list_tools to see what is callable now, use subscribe/cancel for billing, and use request_mcp when nothing fits. It also explains what to do when invoke returns a connect link or checkout link, including retry behavior.

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

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

Annotations already carry the safety profile with idempotentHint=true and destructiveHint=false. The description adds that conversation data is needed for reproduction, which is useful context. However, it does not disclose what happens after submission, such as whether a ticket is created or whether the report is asynchronous, though the annotations lower the burden.

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

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

The description consists of two tight sentences: the first states the purpose, the second gives the key usage instruction. There is no filler, repetition, or irrelevant detail.

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 three-parameter reporting tool with annotations already covering idempotency and destructiveness, the description is mostly sufficient. The main gaps are the unexplained `context` parameter and the absence of any indication of what the response or outcome will be, though no output schema is expected.

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 undocumented parameters. It only clarifies the `conversation` parameter via 'conversation array with recent messages,' leaving the required `message` and optional `context` undefined. The agent must guess at their intended content.

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 the verb 'Report' and explicitly enumerates three targets: 'bug, missing feature, or send feedback'. This makes the tool's purpose unmistakable and easily distinguishable from the sibling calculo_* and authentication tools, which serve entirely different functions.

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 establishes a clear context: use when a user reports a problem or wants to provide feedback. It also adds practical guidance to 'Include the conversation array with recent messages for reproduction.' It does not name alternatives, but none of the sibling tools overlap with bug reporting, so exclusions are unnecessary.

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 declare readOnlyHint=true and idempotentHint=true, so the agent knows this is a safe, non-mutating call. The description adds little beyond that—it names the output as versions but doesn't specify the format (e.g., semver strings, JSON object) or whether the output is human-readable. Since the annotations carry the safety profile, a 3 is appropriate.

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

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

The description is a single sentence of 9 words, front-loading the action ('Show') and the object ('version'). There is zero waste, and it fully conveys the tool's purpose within its scope. This is a model of conciseness.

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, idempotent tool with no output schema, the description is nearly complete. An agent can confidently invoke it without additional context. The only minor gap is that the return format is unspecified, but since there is no output schema, a brief note on the output structure (e.g., 'returns a plain-text summary') would elevate completeness. Still, the description is sufficient for correct invocation.

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

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

The tool has zero parameters, and the schema coverage is 100% (no properties). The description doesn't need to explain parameters. The baseline for zero-parameter tools is 4, and the description is consistent with that—it correctly implies that no input is required.

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

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

The description clearly states the tool's purpose: 'Show the current MCP platform and adapter versions.' This is a specific verb-resource pair that distinguishes it from sibling tools, which are all calculation or authentication tools. It could be slightly more explicit about what 'show' returns (e.g., a text summary vs. structured data), but the resource is unambiguous.

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 that this tool is for checking version information, which makes sense in contexts where an agent needs to confirm platform/adapter versions before proceeding. However, it does not explicitly state when to use this tool versus alternatives, nor does it mention whether version information is needed for authentication or compatibility checks. Given the sibling tools are all calculations, the usage context is reasonably clear, but not explicitly delineated.

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 readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the description does not need to restate safety. It adds value by detailing what kind of state is returned, including connection status and account bindings, which helps the agent understand the tool's informational scope.

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

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

A single tightly packed sentence with the main action front-loaded, followed by a colon-delimited list of return contents. Every phrase earns its place with no repetition 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 zero-parameter, read-only introspection tool, the description fully covers what the agent needs to know before calling: what information it will receive. No output schema exists, but the description essentially provides a light output contract by enumerating the returned components.

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 is empty with zero parameters, and schema description coverage is 100%, so the description has no parameter burden. Per calibration, zero-parameter tools receive a baseline of 4; the description's output-focused content is more than sufficient.

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 ('Returns') and resource ('current toolkit state'), then enumerates exactly what is included: installed MCPs, connection status, connected accounts, and catalog tool counts. This is specific enough to distinguish it from computational siblings like calculo_* and action tools like authenticate or connect.

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 conveys that this is the tool to call when an agent needs an overview or snapshot of the toolkit's current state. It does not explicitly list exclusion criteria or name alternatives such as show_version, but the context is clear enough for routine selection.

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