FBI Most Wanted

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

Annotations already provide idempotentHint=true, readOnlyHint=false, and destructiveHint=false. The description adds behavioral context about permanent vs session-only authentication and explains what happens when called with or without a token. This goes beyond the annotations without contradicting 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 slightly run-on but front-loaded with the key purpose ('log in in the browser, copy the access token') and each sentence contributes to usage or behavior. It's dense but not overly long, earning a 4.

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

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

For a tool with one optional parameter and no output schema, the description explains both invocation modes and the token lifecycle. It doesn't detail return values beyond 'get the link', but this is sufficient for 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?

With 0% schema description coverage and only one optional 'token' parameter, the description compensates by clarifying that token is a JWT for session-only login, and that calling with no args returns a link. This gives the parameter meaning beyond the bare string type.

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: 'log in in the browser, copy the access token' for MCP.AI IDE agents. It distinguishes itself from siblings by focusing specifically on authentication and token handling, with a clear verb ('log in') and resource ('MCP.AI').

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 explicit usage guidance with two clear options: adding a bearer token to server config for a permanent connection, or pasting a JWT for session-only login. It also instructs calling with no args to get the link. However, it does not explicitly exclude alternatives like the 'connect' sibling, so it's not a full 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds meaningful behavioral detail by specifying that it returns authenticated:true with empty pending[] when all providers are connected, and connect_url with per-install URLs when credentials are missing. This goes beyond the annotations, though it doesn't cover partial connection states or error scenarios.

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 long, immediately states the main purpose, and efficiently explains the two key output states. Every sentence earns its place with no redundant or fluff content.

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, the description covers the primary use cases (all connected, missing credentials) and explains what the return value indicates. However, it does not mention a partial-connection state (some but not all providers connected), which could be an important edge case. The lack of an output schema increases the burden, and the description mostly meets it but not fully.

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

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

The tool has zero parameters, so the schema is trivially 100% covered. Baseline for 0 params is 4. The description adds no parameter-specific semantics, but none are needed; it focuses on output behavior, which is appropriate here.

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

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

The description uses a specific verb ('Returns') with a clear resource ('connection status and URLs'), and differentiates itself from sibling 'authenticate' by emphasizing it reports status rather than performing authentication. It also details two specific output conditions, making the 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 implies when to use the tool (to check connection status) and gives context around the two scenarios (all connected vs. missing credentials), but it does not explicitly say 'use this instead of authenticate' or provide exclusions. Sibling tools like 'authenticate' hint at the distinction, but the description itself stops short of explicit 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 readOnly, idempotent, and non-destructive behavior. The description adds valuable operational context: hosted platform, no credentials required, pay-per-query with prepaid credit, data is public (not private/confidential), and LGPD responsibilities for the client. This goes beyond the annotations and addresses authentication, cost, and legal compliance.

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 compact, three-sentence paragraph that leads with the main purpose, then adds practical and legal context. Each sentence carries meaningful information without fluff. The legal and payment details add a bit of density but are relevant to usage.

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 description provides strong context about the tool's purpose, access model, payment, and data classification, it fails to explain the required parameters or expected return values. Without parameter semantics, an agent cannot reliably construct a valid request, making the description incomplete despite its thorough operational 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?

The input schema has three parameters (Nome, Sobrenome, completo) with zero description coverage, and the tool description gives no explanation of these parameters. The description mentions checking a name but does not specify how to structure the input, what 'completo' means, or any formatting requirements. This is a critical gap for an agent to invoke the tool correctly.

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 function: 'Verifica se um nome consta na lista de mais procurados do FBI' (checks if a name appears on the FBI's most wanted list), with a specific use case in compliance and AML. It distinguishes itself from sibling tools (authenticate, connect, etc.) which are generic platform utilities.

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

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

The description provides context on when to use the tool ('para diligência de compliance e AML') and clarifies operational aspects (no credentials, prepaid credit, public data only). It does not explicitly mention alternatives or when-not-to-use, but the sibling tools are clearly different in function, making the intended usage 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 context beyond the annotations: invoke runs a tool even if not installed without adding to the toolkit, returns a connect link when credentials are needed, and returns a checkout link if the wallet is empty. It also discloses that install/uninstall/subscribe/cancel and the one-off install behind invoke require owner/admin, and distinguishes permanent vs. temporary execution. This is rich behavioral detail that helps the agent predict side effects.

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 long but highly information-dense, with no wasted flowery language. It is front-loaded with the core flow and then covers install vs. invoke, permissions, and the prompt library in a structured way. Some sentences are complex and packed with multiple ideas, which slightly hurts skimmability, but 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?

For a multi-action tool with 23 parameters and no output schema, the description is remarkably complete: it explains the full workflow, billing/auth-edge cases, permission requirements, the distinction between one-off and permanent installation, and the separate prompt library feature. It does not explicitly describe return formats, but the important decision-driving context is all present.

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

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

With 23 parameters and 0% schema description coverage, the description must compensate. It does explain the central `action` enum values and key parameters like mcp_id, tool_id, prompt_slug, prompt_body, and prompt_vars through the narrative flow. However, many parameters such as limit, query, immediate, tier_slug, message, request_name, cancel_comment, report_context, and prompt_targets are not individually described. The description provides high-level semantics but does not fully document each parameter's purpose.

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 every MCP/tool and the way to run them. It goes beyond a simple verb+resource by outlining the core search→describe→invoke flow, though the breadth of actions (install, subscribe, prompt library) makes the ultimate purpose somewhat multiplexed. It is not a tautology and is distinguishable from sibling tools like authenticate or connect, but it does not explicitly name 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?

The description provides explicit, actionable guidance: use the core flow search→describe→invoke, prefer invoke for one-off use versus install for permanent toolkit membership, and retry after the user completes a connect/checkout flow. It also states when writes require workspace owner/admin and when to use prompt library actions. This is a model of when-to-use versus 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 idempotentHint=true and destructiveHint=false, which the description does not contradict. The description adds the behavioral guidance to include conversation for reproduction but does not disclose what happens after reporting (e.g., ticket creation, response format). This is a modest addition beyond annotations, earning a 3.

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 only two sentences, with the purpose stated first and the usage instruction second. No filler or redundant phrasing. It is concise and 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 tool with no output schema, the description adequately explains what to send and what to include. However, the 'conversation array' could be misinterpreted since the schema type is string, and the 'context' parameter is not mentioned. These are minor gaps in an otherwise complete description.

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 explicitly explains the 'conversation' parameter and implies 'message' is the report content, but 'context' is left unexplained. The description partially covers the parameters but not fully, so a score of 3 is appropriate.

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

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

The description clearly states the tool's purpose: 'Report a bug, missing feature, or send feedback.' The verb 'Report' is specific, and the resource types are enumerated. This distinguishes it from sibling tools like marketplace or toolkit_info, which serve 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?

It provides clear context for use: when users encounter a bug, missing feature, or want to give feedback. It also instructs to include the conversation array for reproduction. It does not explicitly state when not to use it, but the purpose is unambiguous and there are no close alternatives among siblings.

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. The description adds minimal behavioral context beyond the raw safety profile, such as providing a 'current' snapshot. It does not contradict annotations but also doesn't add significant new information.

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, concise sentence that conveys the exact purpose without filler. It is appropriately sized and 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, parameterless, read-only version-check tool, the description is complete. Given the annotations and empty schema, there is no missing information that would prevent an agent from selecting and invoking 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 takes zero parameters, so the schema is fully descriptive. The description adds no parameter information, but none is needed. Baseline 4 is appropriate for a parameterless 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 ('Show') and identifies the resource ('current MCP platform and adapter versions'). It clearly distinguishes from sibling tools that handle authentication, connectivity, marketplaces, etc.

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 (checking version info) but does not explicitly state when to use it versus alternatives. There is no mention of exclusions or alternative tools, so guidance is only implicit.

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. The description goes further by specifying exactly what state is returned (installed MCPs, connection status, accounts, catalog tool counts), which is valuable behavioral context beyond the annotations. There is no 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?

The description is a single front-loaded sentence that immediately states the return value and then lists the key components. Every word adds value, 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 read-only information tool with no parameters and no output schema, the description fully communicates what the agent will receive. There are no hidden behaviors, prerequisites, or side effects to disclose. The tool is simple, and the description is 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?

Tool has zero parameters, so schema coverage is trivially 100%. The description correctly omits parameter details since none exist. Baseline for 0 params is 4, and no additional parameter semantics are needed.

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 the specific verb 'Returns' followed by a clear resource ('current toolkit state') and enumerates the exact contents (installed MCPs, connection status, accounts, catalog tool counts). This fully distinguishes it from sibling tools like authenticate or connect, which handle setup rather than introspection.

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 implies usage: it is the tool for checking toolkit status and connectivity. It does not explicitly name alternatives or exclusions, but given the clear scope and sibling list, an agent can infer when to choose this tool over others. A minor gap is the lack of explicit 'when to use' guidance.

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