JUCESP: Lista de Documentos

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

Goes beyond annotations by describing the flow: it returns a login link, accepts a pasted token, and differentiates between persistent and session-based auth. It clarifies side effects (e.g., setting headers in config) and explicitly states the result of calling with no arguments.

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 moderately long but efficient, packing multiple scenarios into two sentences. It could be split into bullet points for readability, but every clause adds essential 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 an authentication tool with no output schema and minimal parameters, the description covers all critical aspects: obtaining the token, persistence, and exact call patterns. It leaves no obvious gaps for an agent to misuse the tool.

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 only defines `token` as a string with 0% coverage, but the description fully explains its purpose (paste the JWT for session login) and contrasts it with the no-argument case. This compensates for the sparse 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 states the tool's function: authenticating IDE agents by logging in and handling access tokens. It distinguishes itself from siblings by specifying the exact flow and token/cookie usage, and provides a concrete example invocation (`{ token: "<jwt>" }`).

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?

Explains two usage modes (permanent config vs session-only) and shows how to call the tool with or without arguments to get a link. While it doesn't explicitly contrast with the sibling 'connect' tool, it gives clear actionable guidance on when to use this authentication method.

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 adds valuable context about response structure and conditional fields (authenticated, pending[], connect_url, per-install URLs), which goes beyond the structured 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?

Two concise, front-loaded sentences. The first states the core purpose; the second adds conditional behavior. Every word adds value with no fluff.

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 parameterless status tool with no output schema, the description covers the key scenarios (all connected vs missing credentials) and reveals the main fields. It could mention edge cases like partial connectivity, but overall it is sufficiently complete for an 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?

The tool has zero parameters and schema coverage is 100%, so the baseline is 4. The description does not need to explain params and appropriately focuses on output behavior, which is all that is needed 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 clear verb+resource structure ('Returns connection status and URLs') and explains both success and failure modes with specific fields (authenticated, pending[], connect_url). It clearly distinguishes this from siblings like authenticate or show_version by focusing on status/URL output.

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 by describing outcomes in different states (all connected vs missing credentials), but it does not explicitly state when to prefer this over siblings or provide exclusions. Usage context is implicit, not 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?

Beyond the annotations (read-only, idempotent), the description discloses payment requirements, authentication via credentials, the non-confidential nature of the data, and LGPD compliance, adding meaningful behavioral transparency.

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, starting with the primary function and then adding necessary context. It is not overly verbose and maintains a clear logical flow.

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 basic purpose is clear, the description lacks details about the output format, expected behavior, and the role of each parameter. Without an output schema, this missing information leaves the tool incomplete for a user.

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?

Only the 'nire' parameter is mentioned in the description. The authentication parameters (login_cpf, login_senha, pkcs12_cert, pkcs12_pass) are not explained, leaving their purpose and format unclear.

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 action: listing archived documents of a company in JUCESP based on NIRE. It also mentions hosting, payment, and data source, making it specific and distinct from generic 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?

The description gives context (JUCESP, NIRE, payment) but does not explicitly state when to use this tool over alternatives. The need for NIRE and the company context is implicit, but no comparative guidance is 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?

The description thoroughly documents behavioral traits: invoke works even when MCPs aren't installed, returns connect links for auth, and checkout links for payments with retry instructions. It also discloses permission requirements for writes. Minor deduction for not explicitly mentioning the effect of annotations on behavior, but it 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 a single, dense paragraph with no structure like headings, bullet points, or logical grouping of related functions. It's overly verbose, mixing high-level concepts with implementation details, which makes it hard to parse. This hurts maintainability and quick understanding.

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

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

Given the tool's vast complexity, the description covers numerous critical flows (search, describe, invoke, install, billing, prompt library) and edge cases (auth, payment, permission). However, the lack of structure makes it difficult to verify completeness, and several parameters and the report_bug/request_mcp flows remain underspecified.

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 carries the full burden, and it does explain most parameters contextually through the action flow (e.g., mcp_id and action=describe, prompt_vars for get_prompt). However, many parameters like message, immediate, request_details, and report_context receive no elaboration, leaving their semantics to inference.

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 provides a comprehensive overview of the marketplace tool, including its role as the official catalog and runner for MCPs. It clearly distinguishes between different actions (search, describe, invoke, install) and even differentiates from siblings by noting it's the platform's way to run 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 gives explicit guidance on when to use invoke versus install, when to use action=search versus list_tools, and even covers edge cases like invoke working without installation. It also covers the prompt library separately, clarifying their distinct use cases.

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=false and destructive=false, and the description adds useful context that conversation history is expected for reproduction. However, it doesn't disclose what happens after submission (e.g., confirmation, external transmission, or error behavior), leaving some behavioral ambiguity.

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 conveys purpose and usage guidance without redundant wording. Every phrase contributes meaning.

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 feedback/reporting tool, the core purpose and conversation requirement are covered, and annotations help clarify side effects. However, the absence of an output schema and the lack of any explanation about response behavior or the 'context' parameter leave notable gaps for an 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 clarifies the conversation parameter ('recent messages for reproduction') but does not explain the required 'message' parameter or the optional 'context' parameter, nor does it specify the expected JSON format for the conversation string.

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

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

The description clearly identifies the action ('Report') and the targets ('bug, missing feature, or feedback'), making the tool's purpose immediately obvious. It also distinguishes itself from sibling tools like show_version or marketplace, which serve informational or transactional purposes.

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 defines the supported use cases: bug reports, feature requests, and general feedback. It also instructs the user to include conversation data for reproduction. It doesn't mention alternative tools or exclusions, but the purpose is clear enough within its tool list.

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

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

Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds value beyond those annotations by specifying that the output covers current MCP platform and adapter versions, which is useful 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?

A single clear sentence that directly states the tool's purpose with no filler or redundant information. Every word 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 simple, zero-parameter, read-only version lookup tool with rich annotations, the description is complete. It explains what versions are shown, and no output schema or additional behavioral caveats are necessary for this level of complexity.

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 baseline is 4. The description correctly implies no inputs are needed and does not need to add parameter-level meaning.

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 names a clear resource ('current MCP platform and adapter versions'). This distinguishes it from sibling tools like toolkit_info and authenticate, which are broader or concerned with other concerns.

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: call this tool when you need the current platform and adapter versions. It does not explicitly discuss alternatives or when not to use it, but for a simple zero-parameter informational tool, this is sufficient.

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 value by specifying exactly what state is returned (installed MCPs, statuses, accounts, tool counts), which is especially helpful given there is no output schema. It does not discuss caching or freshness, but for an annotations-backed read-only tool this is sufficient.

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

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

The description is a single, tightly packed sentence that front-loads the purpose ('Returns the current toolkit state') and enumerates the relevant output categories after a colon. Every clause adds information with no filler or redundancy.

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 covers the essential output facets. It does not describe the exact data format or possible status values, but it provides enough detail for an agent to decide to call this tool and to interpret the general shape of the result.

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

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

There are zero parameters and the schema coverage is 100%, so no parameter documentation is needed. The baseline of 4 applies; no additional semantic explanations are 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 uses the specific verb 'Returns' and clearly identifies the resource ('current toolkit state') with concrete components: installed MCPs, connection status, accounts, and catalog tool counts. This distinguishes it from sibling tools like authenticate/connect, which perform actions, and show_version, which is narrower.

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 use case clear: call this when you need a summary of the toolkit's overall state. It does not explicitly mention when not to use it or name alternative tools for narrower queries, but the context strongly implies it is the go-to read-only overview tool.

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