Crisp

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

The description adds context beyond annotations: it explains the two authentication modes (permanent vs session), the return of a link, and the optional token parameter. It doesn't contradict the idempotent and non-destructive hints, but it omits error handling details, so it doesn't fully cover all behavioral aspects.

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

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

The description is three concise sentences, front-loaded with purpose and context, followed by two clear usage modes. Every sentence provides essential information without 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 an authentication tool with one optional parameter and no output schema, the description adequately covers the workflow, return behavior, and integration options. It is complete given the tool's complexity and available annotations.

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 fully compensates by explaining the 'token' parameter as a JWT and clarifying the behavior when omitted (returns a login link). This adds meaning beyond the bare schema definition.

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: authenticating the user via browser login and token exchange. It distinguishes itself from siblings like 'connect' by explicitly focusing on authentication and token handling, making the tool's specific role obvious.

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 usage alternatives: adding the token to config for permanent access, pasting a JWT for session-only login, or calling with no arguments to obtain a link. It also indicates the best practice, which clearly differentiates when to use each approach.

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, establishing this as a safe, non-mutating operation. The description adds behavioral context by explaining the state-dependent output: when all providers are connected vs. when credentials are missing (returns connect_url). This transparency about conditionality goes beyond the annotations and helps the agent anticipate different return scenarios. However, it doesn't disclose details like rate limits or whether it hits external APIs, but given the annotations cover the core behavior, this is acceptable.

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

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

The description is only two sentences, each packed with meaningful information. It front-loads the primary purpose ('Returns connection status and URLs') and then explains the two main states. There is no filler or repetition. It earns every word, making it an example of excellent 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 tool with no parameters, no output schema, and clear annotations, the description is quite complete. It covers the happy path (all providers connected) and the missing-credentials path (returns connect_url). The trade-off between completeness and brevity is well balanced. The only minor gap is that it doesn't explain what 'pending[]' represents when not empty, but the description already implies that when authenticated:true, pending is empty, and when not, it likely contains pending items. Given the tool's simplicity, this level of completeness 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?

The tool has zero parameters, and schema description coverage is 100% (vacuously). With no parameters, the baseline for this dimension is 4. The description does not attempt to fake parameter explanations and instead clarifies the output semantics. This is appropriate; the description adds value by explaining the meaning of the returned fields ('authenticated:true', 'empty pending[]', 'connect_url'), which is more relevant than parameter semantics 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 clearly states the tool's purpose: it returns connection status and URLs, with specific details about the 'authenticated' field and 'pending' array. This distinguishes it from siblings like 'authenticate' (which likely initiates authentication) and 'toolkit_info' (which provides general toolkit info). The verb 'returns' plus the resource 'connection status and URLs' is specific and 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 implicitly conveys when to use this tool: to check connection status or obtain connect URLs when credentials are missing. However, it does not explicitly state when NOT to use it or explicitly name alternative tools. Given the sibling tools, 'authenticate' is the obvious alternative for initiating authentication, but the description does not make that comparison. Still, the context is clear enough for an agent to infer correct usage.

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 discloses crucial behaviors not fully captured by annotations: invoke works for uninstalled MCPs, returns auth/checkout links, and performs a 'one-off' install that requires permissions. It also explains that search/describe flag installation states. Annotations only indicate non-read-only and open-world, so the description substantially enriches the behavioral model.

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 every sentence adds value. It front-loads the core purpose, then systematically covers the flow, key distinctions (invoke vs install), edge cases (auth/payment), and auxiliary functions (prompt library, subscriptions). While lengthy, the density of actionable information justifies its length; 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 tool with 23 parameters, no output schema, and complex multi-action semantics, the description covers the essential context comprehensively. It explains the main workflows, prerequisites, permission requirements, and sibling differentiations. It also addresses the two distinct domains (MCPs and prompt library) without confusion.

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 must explain the 23 parameters, and it does so effectively. It describes the 'action' enum values (search, describe, install, invoke, etc.), explains the roles of mcp_id, tool_id, arguments, prompt_* fields, and provides context for messenger/report fields. All parameters are given meaning either explicitly or implicitly through the described workflows.

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, detailing its dual role as catalog and execution engine. It specifies the core flow (search → describe → invoke) and covers both MCP tools and the prompt library. It distinguishes from siblings like search_tools by stating its own search and invoke capabilities.

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

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

Provides explicit guidance on when to use invoke vs install, when to use subscribe/cancel, and when to use the prompt functions. It even specifies auth requirements for writes and mentions retrying after connect/checkout links. This goes beyond simple 'when to use' by offering actionable decision rules.

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 (readOnlyHint=false, idempotentHint=true, destructiveHint=false) already cover safety and side-effect behavior. The description does not contradict these annotations and adds no new behavioral details beyond the purpose, so the baseline credit applies.

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, direct, and free of redundancy. It efficiently conveys the purpose and a key usage hint without any 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 tool with three parameters, the description only hints at two (message and conversation) and omits context entirely. There is no mention of expected output or side effects beyond annotations. This is sufficient for basic use but lacks detail about parameter formats and edge 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?

The description mentions 'conversation array' and implies message content, but does not clarify that conversation is a string (though it appears as a string in the schema) or explain the 'context' parameter at all. This adds some meaning but leaves ambiguity and incomplete coverage.

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: to report a bug, missing feature, or send feedback. It uses a specific verb (report) and resource (bug/feature/feedback), and clearly distinguishes from sibling tools like authenticate, connect, or search_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 provides a concrete usage hint: 'Include the conversation array with recent messages for reproduction.' This tells the agent when to use it (when reporting issues with context) and what to include. It could be more explicit about when not to use alternatives, but there are no competing reporting tools.

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 reveals behavioral differences between actions: search returns relevance-ordered tools, describe returns schema, and invoke executes a tool by ID. However, it does not disclose what invoke does with argument, whether side-effects are possible, or fmt of error responses; the annotations are all false and add no meaningful safety context.

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 one tight sentence with three semicolon-delimited clauses, each carrying one concrete action. It is far-leading, readable, and contain no redundant or filler 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?

The tool has 7 parameters, no output schema, no meaningful annotations, and a multiplexed action/action//argument interface. The description covers only the action triage and leaves parameter semantics, invocation payload format, scoring/filtering behavior, and side-effect expectations undocumented, so it is not complete enough for reliable invocation.

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

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

Schema description coverage is 0%, and the description only explains the action param via enum examples. The six remaining params (query, queries, tool_id, arguments, min_score, force_reindex) are not described at all in the prose, leaving ambiguous matters like the difference between query and queries, the expected shape of arguments, and meading of min_score/force_reindex.

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 explains what the tool does: it is a single entrypoint for the MCP catalog, with action=search returning available tools ordered by relevance, action=describe returning tool schema, and action=invoke executing by tool ID. This differentiates the tool from the sibling tools because it names both the catatog resource and the distinct actions on that resource.

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 practical routing guidance for each possible action: use search to list/find tools, describe to get a schema, and invok to run a tool by ID. It does not explicitely discuss competitors such as marketplaces or auth, but the 'single entrypoint' wording and action-specific returns provide enough context.

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 disclose readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is established. The description adds the scope ('MCP platform and adapter'), but no additional behavioral details beyond that.

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 one short, front-loaded sentence containing no filler or redundant information. It fully serves its purpose 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?

For a simple zero-parameter tool with strong annotations, the description is sufficiently complete. It does not explicitly describe the return format, but the intent is clear enough for version-information retrieval.

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 accepts zero parameters, so there is nothing semantically to clarify. The schema coverage is complete and the description adds no unnecessary or missing parameter details.

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 clearly identifies the resource: current MCP platform and adapter versions. This clearly distinguishes it from sibling tools like authenticate, marketplace, and report_bug.

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: call it when current version information is needed. However, it does not explicitly state when to prefer this tool over alternatives or any selection context beyond the obvious.

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

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

Annotations already mark the tool as readOnly, idempotent, and non-destructive, so the bar is lower. The description adds meaningful context by emphasizing 'current' state and detailing exactly what aspects are reported, making side effects and behavior reasonably transparent.

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, well-structured sentence that immediately leads with the primary action and then enlists the relevant output categories. It contains no redundant or throwaway words and is easy to parse at a glance.

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, zero-schema tool, the description fully tells the agent what will be returned: installed MCPs, connection status, per-MCP account connections, and catalog tool counts. There is no output schema, but the description compensates sufficiently by enumerating the major content areas.

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 also specifies an empty object, so there is nothing to document. The baseline for zero-parameter tools is 4, and the description does not need to compensate for parameter coverage gaps.

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') and clearly identifies the resource ('current toolkit state') with a concrete list of fields included: installed MCPs, connection status, connected accounts, and catalog tool counts. This provides strong differentiation from siblings like show_version and search_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 clearly signals when to use this tool: when the agent needs a snapshot of the currently installed/connected toolkit. It does not explicitly name alternatives or exclusions, but the specified output scope provides adequate contextual guidance for selecting this tool over the listed siblings.

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