LibreLink Up B2B (CGM clínica)

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

Annotations indicate idempotentHint=true and destructiveHint=false. The description adds context by explaining the two modes (link retrieval vs. token login) and the permanent config option. It does not cover error cases or response format, but annotations already cover safety profile.

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 and front-loaded, with every sentence contributing. It could be slightly more structured (e.g., bullet points), but remains efficient and informative.

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 description covers the two usage modes and the parameter. However, given no output schema, it does not describe the return format or error handling. For a simple authentication tool, it's mostly adequate but leaves some gaps.

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%, so the description carries full burden. It explains the 'token' parameter as a JWT from browser login, and specifies behavior with and without it. This adds significant meaning beyond the schema's type-only 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 is for authentication via browser login and token handling. It uses specific verbs like 'log in' and 'copy the access token', and distinguishes itself from sibling tools by being the dedicated authentication tool.

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 clear usage guidance: it explains two methods (permanent config vs. session-only token) and how to call the tool with or without the token argument. However, it does not explicitly compare to sibling tools or state when not to use it.

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, non-destructive. The description adds conditional behavior: returns authenticated:true with empty pending[] when connected, and connect_urls when credentials are missing. 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?

Two sentences, front-loaded with main purpose, no unnecessary words. Efficiently conveys all 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?

Given no output schema, the description adequately explains the two response states (connected vs. missing credentials). Could optionally detail exact field names, but sufficient for a simple status 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?

No parameters exist, so schema coverage is 100%. The description does not add parameter information, but none is needed. Baseline 4 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 returns connection status and URLs, with specific scenarios (all providers connected vs. missing credentials). It distinguishes from sibling tools like authenticate by focusing on status retrieval.

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 for checking connection status but does not explicitly state when to use this tool versus alternatives like authenticate. No direct comparison or exclusions 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 readOnlyHint and destructiveHint. Description adds context: 'Read-only CGM data — clinic/follower account' and 'not for medical decisions without clinician review', enhancing 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?

Description is four sentences, front-loaded with purpose, succinct, and no redundancy. Could be slightly more structured but efficient.

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?

Lacks output schema, so description explains output fields. Covers bulk support, read-only nature, and medical disclaimer. Complete for a simple tool, though could mention error conditions.

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%, so description must add value. It explains batch support via 'patient_ids' and implies single patient via 'patient_id', but does not describe each parameter in detail. Adequate but not exceptional.

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?

Clearly states it retrieves the latest glucose reading with fields (value, trend, flags). Distinguishes from sibling 'librelink_business_get_glucose_graph' for history, providing specific verb and 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?

Explicitly tells when not to use (for history, use graph) and provides context on read-only nature and medical disclaimer. Could be slightly more specific about other siblings but still clear.

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 annotations (readOnlyHint, idempotentHint), the description adds valuable context: the 12-hour high-resolution nature, bulk support via patient_ids, and a medical use caution. 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?

Two short sentences, the first clearly stating the core purpose and the second adding bulk support. No redundant words; highly efficient.

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 simple tool (2 parameters, no output schema), the description covers the essential behavior and key constraints. Missing details like output format or error handling, but adequate for a read-only graph 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?

With 0% schema coverage, the description does not fully compensate. It mentions patient_ids for batched execution but does not explain the patient_id parameter or its required status. Some meaning is added, but incomplete.

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 specifically states it retrieves '~12h high-resolution glucose graph (graphData)', clearly identifying the resource and verb. It distinguishes from siblings like get_current_glucose or get_glucose_stats by emphasizing graph data and bulk support.

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?

No explicit guidance on when to use this tool versus alternatives. The description mentions it is for clinic/follower accounts and not for medical decisions, but does not compare to other glucose tools or provide when-not-to-use 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, destructiveHint. The description adds value by explicitly stating 'Read-only CGM data' and including a medical disclaimer ('not for medical decisions without clinician review'). It also mentions 'clinic/follower account' hinting at authentication context, which surpasses annotation detail.

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 with two short paragraphs: first covering purpose, alternative, and disclaimer; second covering bulk support. No redundant information. However, the disclaimer could be integrated more tightly. Overall, well-structured and efficient.

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 output schema, the description falls short in explaining what the logbook contains (e.g., timestamp, glucose values). It states 'Read-only CGM data' but not the structure. Annotations cover safety, but for a data retrieval tool, return format expectations are missing, reducing completeness 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?

With 0% schema description coverage, the description compensates by explaining the bulk parameter 'patient_ids' for batched execution. However, it does not clarify the required 'patient_id' parameter beyond its obvious role, nor does it specify date range or format for the logbook entries. Minimal but adequate compensation.

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 retrieves a manual-scan glucose logbook with an approximate 14-day window. It distinguishes itself from the sibling tool 'librelink_business_get_glucose_graph' which provides continuous graph data, making the purpose specific and non-redundant.

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 directs to use 'librelink_business_get_glucose_graph' for continuous data, and notes that this tool is for manual-scan logbook. It also mentions bulk support via 'patient_ids'. However, it does not exclude other sibling tools like 'get_current_glucose' for real-time data, though the context is implied.

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 provide readOnlyHint, idempotentHint, destructiveHint. Description adds context: default targets (70–180), time window (~12h), bulk execution behavior, and a medical disclaimer. 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?

Three sentences: main output and window, default target and disclaimer, bulk support. Front-loaded, 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?

Covers purpose, parameters (defaults, bulk), constraints (time window, read-only), and output (specific stats). Lacks only minor details like required parameter mention, but sufficient 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 description coverage is 0%, but the description explains target_low/target_high defaults and patient_ids for bulk. patient_id is implied as required, though not explicitly described. Adds sufficient meaning beyond 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 it returns time-in-range, average, min/max from the current graph window (~12h). This distinguishes it from siblings like get_current_glucose (single reading) or get_glucose_graph (graph data).

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 notes read-only CGM data for clinic/follower accounts and includes a medical disclaimer. It mentions bulk support via patient_ids, but does not explicitly contrast with siblings or state when not to use it.

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 it as read-only and idempotent. Description adds caveat about medical decisions and clarifies the data window. Adds value beyond annotations without 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?

Two concise sentences with key info front-loaded. No wasted words, but could be slightly more structured with parameter hints.

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?

No output schema, so description must explain return format. It states 'normalized points' without structure. Missing details on how hours interacts with the graph window or what 'normalized' means. Incomplete for a tool with 3 parameters and no schema descriptions.

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%. Description only explains patient_ids for batched execution and implicitly hours as 'last N hours', but does not detail the relationship between patient_id and patient_ids, or the default/range of hours.

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?

Clearly states the tool retrieves normalized CGM points for the last N hours from the current graph window, with a max of ~12h. Distinct from sibling tools like get_current_glucose or get_today_summary.

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?

Mentions bulk support with patient_ids, but does not explicitly guide when to use this tool versus alternatives like get_glucose_graph or get_current_glucose. Minimal context for selection.

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 readOnlyHint and idempotentHint. The description adds value by clarifying that this is read-only CGM data, not for medical decisions without clinician review, and that it supports batch execution via patient_ids. 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 is very concise: two sentences in the first paragraph, one in the second. Every sentence adds value: it states what the tool returns, its read-only nature, usage restrictions, and bulk support. No 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 no output schema, the description lists the returned fields and warns about medical use. It covers context for a simple read tool. Bulk support is mentioned. However, missing behavioral details like error handling or data freshness could be improved. Overall adequate for a straightforward 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?

With 0% schema description coverage, the description must compensate. It mentions both parameters implicitly: patient_id for single patient, patient_ids for batch. It does not detail each parameter's format, constraints, or default behavior. While it provides context, more explicit parameter documentation would improve semantics.

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 it returns the latest reading with specific fields (mg_dl, trend_arrow, etc.). It identifies as a read-only CGM tool, distinguishing it from similar tools by mentioning 'latest reading' and bulk support. However, it does not explicitly differentiate from siblings like librelink_business_get_current_glucose, which might also return a current reading.

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: it is intended for clinic/follower accounts and warns against using for medical decisions without clinician review. It also mentions bulk support. However, it does not explicitly state when to use this tool versus alternatives (e.g., get_current_glucose), nor does it specify prerequisites or 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 confirm read-only and idempotent; description adds context about clinic account and non-medical use. No contradictions. Does not detail rate limits or permission requirements.

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

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

Two sentences, front-loaded with purpose and scope, followed by bulk and caution. No unnecessary 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?

No output schema, but description lists returned data. Missing details on batch response structure and error handling. Adequate for a simple tool but with gaps.

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 must compensate. It explains patient_id as the primary parameter and patient_ids for batching, but lacks format or constraints details, leaving partial 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 explicitly states 'Get one connection by patient_id' and lists specific data returned (target range, sensor, latest glucose envelope). It distinguishes from sibling list tool by focusing on a single patient.

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 specifies the tool is for 'clinic/follower account' and includes a caution about medical decisions. It mentions bulk support via patient_ids, but does not explicitly contrast with other get tools like get_current_glucose.

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 readOnlyHint=true and idempotentHint=true. The description adds valuable context about bulk execution via patient_ids and the intended audience (clinic/follower account), along with a medical disclaimer, 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 extremely concise with two main sentences and a separate line for bulk support, front-loading the core functionality and constraints 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?

Despite annotations providing read-only and idempotent hints, the description lacks explanation of the return value (no output schema) and parameter semantics, leaving significant gaps for a tool with 5 parameters and moderate 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?

With 0% schema description coverage and 5 parameters, the description fails to add meaning for any parameter. It only mentions 'timezone' implicitly via 'IANA timezone' and 'patient_ids' for bulk support, but does not explain target_low, target_high, patient_id, or the default values.

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 verb 'get' and resource 'today summary' of glucose stats, and distinguishes it from the sibling tool 'librelink_business_get_glucose_stats' by specifying the 'today' filter and inclusion of hypo/hyper event counts.

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 about read-only CGM data for clinic/follower accounts and includes a warning against use for medical decisions without clinician review, but does not explicitly state when to use this tool versus alternatives or when not to use it.

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, so the description adds context: 'Read-only CGM data — clinic/follower account' and a disclaimer about not for medical decisions. This is useful 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 sentences with zero waste: first states purpose and equivalence, second gives usage guidance and disclaimer. Front-loaded with the key action.

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?

Adequate for a listing tool; mentions return of patient IDs implicitly. Could specify that it returns all patients (no pagination) or describe return format, but the description is sufficient given no output schema and simple 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?

No parameters exist; schema coverage is 100% and description adds no parameter explanation, which is acceptable. Baseline for 0 parameters is 4.

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 it lists all patients visible to the follower account, using specific verb 'list' and resource 'patients'. It distinguishes from sibling tools like librelink_business_get_patient by indicating this is the starting point to get patient IDs.

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

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

Explicitly instructs to use patientId from each row for other tools, providing clear workflow guidance. Does not explicitly list alternatives but the context of sibling tools and the phrase 'same as /llu/connections' implies when to use.

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 that invoke works without installing the MCP, returns connect/checkout links for auth/payment gaps, and that writes require workspace owner/admin. It also explains that search/describe flag installed_in_toolkit vs installed_in_workspace, adding meaningful behavioral context the annotations do not capture.

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 information-dense, with front-loaded purpose and a logical flow from search to invoke to install. It would score 5 with bulleted or sectioned formatting; as a single paragraph it still earns its length because every clause adds a distinct distinction (one-off vs permanent, auth links, billing, prompt library).

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 no parameter descriptions, the description covers the major workflow, permission requirements, and edge-case behaviors. It misses a few enum actions (e.g., resume) and does not describe return shapes for search/describe, but the overall operational context is sufficiently complete for an agent to select and invoke 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?

With 0% schema description coverage for 23 parameters, the description compensates by explaining the action enum values, the role of mcp_id/tool_id/arguments in the core flow, and prompt-related fields (prompt_slug, prompt_body, prompt_vars). However, several parameters such as immediate, tier_slug, conversation, and request_name remain undocumented, so the compensation is strong but not complete.

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 a clear statement: 'The official mcp.ai marketplace — the in-platform catalog of every MCP/tool, AND the way to run them.' It names the specific resource (mcp.ai marketplace) and the two core functions (cataloging and running MCPs). It also distinguishes itself from sibling tools by covering capability searches and prompt-library operations.

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 an explicit core flow (search → describe → invoke), and then contrasts invoke with install: 'Use install only to make an MCP PERMANENT in the active toolkit... prefer invoke for a single/occasional use.' It also names alternatives like list_tools, subscribe/cancel, report_bug, and request_mcp, making when-to-use guidance unambiguous.

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 does not explain behavioral traits like data storage or server interaction, and annotations set idempotentHint=true which contradicts the non-idempotent nature of bug reporting.

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?

Single, clear sentence with no redundancy, though some structure could help.

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?

Missing details on what happens after reporting (e.g., confirmation, errors), and parameter explanations are insufficient.

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 only partially explains the conversation parameter, leaving context and message undefined.

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 reports bugs, missing features, or feedback, and no sibling tool offers this functionality.

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 advises including the conversation array for reproduction, but lacks when-to-use or 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 readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the agent knows this is a safe, idempotent read operation. The description adds that it returns platform and adapter versions, which is useful but not extensive. With annotations providing a strong safety profile, a score of 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, self-contained sentence with no verbiage. Every word serves a purpose, and the key information (what is shown) is front-loaded. Perfect 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?

Given the tool has no parameters, no output schema, and annotations already cover its safety and idempotency, the description is complete. It tells the agent exactly what the tool does (show versions) and implies the return value. No additional context is necessary.

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 input schema is empty and schema coverage is 100%. The description does not need to explain parameter semantics. A score of 4 reflects the baseline for tools with no parameters, as the description adds no extra value here but is not 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 a specific verb 'show' and identifies the exact resource 'current MCP platform and adapter versions'. It clearly distinguishes this tool from sibling tools that focus on glucose data, leaving no ambiguity about its function.

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 tool can be used anytime to retrieve version information. While it doesn't explicitly state when not to use it or provide alternatives, the purpose is simple enough that no further guidance is needed. Sibling tools are sufficiently different to avoid confusion.

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, confirming a safe read operation. The description adds beyond this by specifying the exact contents of the returned state, providing full 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?

A single, front-loaded sentence that conveys the core purpose without any unnecessary words. Every part 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?

With no parameters and annotations covering behavioral traits, the description fully explains what the tool returns. No output schema is needed as the description sufficiently describes the return value.

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 zero parameters, so schema description coverage is 100%. Baseline is 4; no additional parameter info is 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 clearly states the verb 'Returns' and the specific resource 'current toolkit state', detailing what it includes: installed MCPs, connection status, and catalog counts. This distinguishes it from sibling tools which handle business operations or authentication.

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 for checking toolkit status, but does not explicitly state when to use or exclude alternatives. However, the purpose is clear enough for an agent to infer appropriate contexts.

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