LibreLink Up B2B (CGM clínica)

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

Annotations indicate idempotentHint true, destructiveHint false. Description adds context: token is JWT, calling without args returns link, with token performs session login. No contradictions. Adds value 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 somewhat lengthy but efficiently conveys necessary steps and options. Front-loaded with purpose. Slight redundancy but overall well-structured.

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; description does not specify return format for either case (link or token acceptance). Agents may need to guess response structure. Incomplete for a tool with 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 has one optional string parameter 'token' with 0% coverage. Description fully explains its meaning: when provided, it's a JWT for session login; when omitted, returns login link. Adds complete 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 the tool is for authentication: logging in, obtaining an access token, and using it. It differentiates from sibling tools which are all unrelated to auth.

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 explains when to use (for IDE agents), provides step-by-step instructions (login in browser, copy token, add to config or paste), and distinguishes between permanent and session-only login.

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 destructiveHint=false, so the agent knows it's safe and non-mutating. The description adds value by detailing the output in different states (authenticated:true vs connect_urls), which is beyond what annotations provide.

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, with no wasted words. It is front-loaded with the core purpose and then elaborates on states. 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?

Given the tool has no parameters and no output schema, the description covers the main return states well. It could be slightly more complete by mentioning possible error states or additional fields, but overall it's sufficient for the agent to understand the output.

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%. The description adds no parameter information because none is needed. It correctly focuses on the return 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 tool returns connection status and URLs, and explains the different states (all providers connected vs missing credentials). It uses specific verbs and outcomes, distinguishing itself from the sibling 'authenticate' 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 implicitly tells when to use this tool (to check connection status) versus 'authenticate' (which presumably initiates auth). It could be more explicit about when not to use it, but the context is clear from the sibling 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 indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds valuable behavioral context: 'Read-only CGM data — clinic/follower account' clarifies authentication scope, and 'not for medical decisions without clinician review' sets expectations. Also reveals bulk support 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?

Extremely concise: two sentences plus a one-line note. First sentence states core purpose, second provides usage guidance, and third adds bulk support. No filler, perfectly 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?

With no output schema, the description covers return data (value, trend, flags), usage constraints, and batch capability. It's sufficient for a simple read operation. Could mention potential errors or rate limits, but given annotations (idempotent, read-only), it's nearly complete.

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 carry parameter semantics. It explains the second parameter: 'Bulk support: accepts patient_ids for batched execution.' It does not explain that patient_id returns a single reading or that patient_ids returns multiple, but it provides enough functional context. A bit more detail on batch output would raise this to 5.

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 'Latest glucose reading for a patient (value, trend, flags)', specifying the verb (get current), resource (glucose reading), and key data elements. It distinguishes from sibling tool librelink_business_get_glucose_graph by mentioning 'For history use...', so purpose is precise and differentiated.

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 when to use an alternative: 'For history use librelink_business_get_glucose_graph.' Also provides context on appropriate use cases: 'Read-only CGM data — clinic/follower account; not for medical decisions without clinician review.' This guides agent on when to invoke and when to avoid.

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, destructiveHint), the description adds behavioral context: the data is for clinic/follower accounts, not for medical decisions, and includes a 12-hour resolution. This provides helpful additional constraints.

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: the first describes the core purpose and constraints, the second adds bulk support. No wasted words; information is 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?

Given the tool's complexity (2 parameters, no output schema), the description covers time range, resolution, account type, medical disclaimer, and batch support. It is mostly complete, though the return structure could be clarified.

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 adds some meaning by mentioning 'patient_ids for batched execution' and implying the singular patient_id. However, it lacks explicit details about parameter types or formats.

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 retrieves a '~12h high-resolution glucose graph (graphData)' for CGM data, distinguishing it from sibling tools like get_current_glucose or get_glucose_logbook. The verb 'get' and resource 'glucose graph' are specific.

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

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

The description implies use for high-resolution 12-hour graph data and mentions 'bulk support' with patient_ids, but does not explicitly specify when to use this tool versus alternatives like get_last_hours. No exclusions are provided.

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

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

Annotations already declare readOnlyHint, destructiveHint, idempotentHint. The description adds value by specifying data scope (~14 days), type (manual-scan, not continuous), and usage constraints (clinic/follower account, not for medical decisions). This behavioral context 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 two concise sentences plus a short third sentence for bulk support. It is front-loaded with the core purpose and differentiation, and every sentence adds meaningful information without 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?

The description covers purpose, usage, and behavioral aspects, but lacks details about return format, data fields, or pagination. Given the absence of an output schema and the tool's simplicity, it is adequate but not fully comprehensive.

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 mentions 'accepts patient_ids for batched execution' for the optional array parameter, clarifying its batch use. However, it does not describe the required patient_id parameter beyond its existence, leaving some ambiguity about its format or role.

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 is a 'Manual-scan logbook (~14 days)' and explicitly distinguishes it from the sibling tool for continuous graph by naming 'librelink_business_get_glucose_graph'. The verb 'get' is implied by the tool name, and the resource is clearly defined.

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 when to use this tool ('Manual-scan logbook') vs the alternative ('For continuous graph use librelink_business_get_glucose_graph'). Also cautions that data is for clinic/follower account and not for medical decisions without clinician review, giving clear usage boundaries.

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?

Adds significant behavioral context beyond annotations: time window (~12h), default target range, read-only nature, medical disclaimer, and bulk execution support. 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, each adding distinct value: first states core function, second adds defaults and medical warning, third covers bulk. Front-loaded, 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?

Covers main outputs, time window, default targets, and bulk capability. Lacks explicit output format or description of min/max scope, but overall sufficient for a read-only stats 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 compensates by explaining default target range (target_low, target_high) and bulk support (patient_ids). Patient_id is implicit but not detailed. Nearly adequate.

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 time-in-range, average, and min/max glucose data from the current graph window (~12h). Distinguishes from siblings like get_current_glucose and get_glucose_graph.

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 context: read-only, clinic/follower account, not for medical decisions without clinician review. Mentions bulk support for batched execution, but lacks explicit when-not-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 declare readOnlyHint=true and idempotentHint=true. Description adds value by explaining max ~12h availability, normalization, and bulk execution support, complementing 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 succinct paragraphs with no redundant information. First paragraph covers purpose and constraints; second adds batch capability. Every sentence adds value.

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, description adequately covers key behaviors: read-only, time window, normalization, bulk execution, and medical disclaimer. Slightly lacks return format, but overall sufficient for agent understanding.

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

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

Schema description coverage is 0%, so description must compensate. It explains 'hours' via 'last N hours' and 'max ~12h', and clarifies patient_ids for batch execution. However, it does not detail default values or formats for all parameters.

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 last N hours of normalized points from the current graph window, specifying the resource (CGM data), verb (get), and constraints (max ~12h). Distinguishes from siblings like get_current_glucose and get_glucose_graph by focusing on historical normalized 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?

Provides context for usage: read-only, clinic/follower account, not for medical decisions without review. Mentions bulk support via patient_ids. Does not explicitly exclude alternatives but implies appropriate 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?

The description adds meaningful behavioral context beyond annotations: it confirms read-only nature, specifies it is for CGM data, warns against medical use, and describes the returned data structure. No contradictions 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 concise, with two sentences covering output format and bulk support. It is front-loaded with the important output structure and avoids 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?

For a simple read-only tool with no output schema, the description adequately explains the return fields and usage constraints. Minor omissions like error handling or authentication are acceptable given 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?

The description clarifies that patient_ids is for bulk execution, adding some value over the schema. However, it does not explain the format or purpose of patient_id, and schema coverage is 0%, so more detail would be beneficial.

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 the latest normalized reading with specific fields, and distinguishes itself from siblings by specifying it is for clinic/follower accounts, read-only, and includes 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?

The description provides context on when to use (clinic/follower account, non-medical decisions) and mentions bulk support via patient_ids. It lacks explicit comparison to siblings but the usage context is clear enough.

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, and the description reinforces read-only access and adds behavioral traits: bulk execution support and data contents (envelope). 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?

Three well-structured sentences with no redundancy. Key information is front-loaded: verb, resource, data returned, then additional context.

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 read-only tool with two parameters and no output schema, the description covers purpose, data content, bulk capability, and usage caveat. No 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 clarifies the purpose of patient_id (single lookup) and patient_ids (bulk batch), adding logic beyond the schema's empty attribute descriptions. Could include format examples.

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 'connection by patient_id', specifies the returned data (target range, sensor, latest glucose envelope), and distinguishes from sibling tools like list_patients by focusing on a single patient's connection details. Bulk support is also mentioned.

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 and clinician review, but does not explicitly state when to use this tool versus alternatives like get_current_glucose or list_patients. No direct comparison or exclusion criteria.

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, destructiveHint=false, idempotentHint=true. Description adds value by confirming read-only nature, adding caution about medical decisions, and noting batch capability. 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 core purpose, then adds batch support. Every word earns its place; 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?

No output schema, so description should convey return shape. It says 'includes hypo/hyper event counts' but doesn't list other fields. With 5 parameters, lack of per-parameter detail is a gap, but overall adequate for a simple tool given 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?

Schema coverage is 0%, so description must compensate. It mentions timezone, patient_id, target_low, target_high, and patient_ids. Explains patient_ids for batch, but does not explicitly state that target_low/target_high are thresholds for hypo/hyper counts. Adds partial value but could be more explicit.

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 'Like glucose stats but filtered to today in an IANA timezone; includes hypo/hyper event counts.' It specifies the verb (get), resource (today summary), and distinguishes from sibling tool 'librelink_business_get_glucose_stats' by the temporal filter.

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 context: 'Read-only CGM data — clinic/follower account; not for medical decisions without clinician review.' Also mentions 'Bulk support: accepts patient_ids for batched execution.' However, does not explicitly exclude when not to use or compare with other siblings beyond the implicit contrast.

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 and idempotent hints. The description adds value by stating the data is CGM data, that it's from a clinic/follower account, and provides a medical disclaimer, which is 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?

Two sentences: first states the main action, second gives usage and disclaimer. No fluff, front-loaded, 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 simple tool with no parameters and no output schema, the description provides sufficient context: source account, usage hint, and disclaimer. It could describe the output fields more fully, but it's not critical.

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 no parameters, and schema coverage is 100%. The description does not need to add parameter info; baseline score of 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 it lists all patients visible to a specific account, with a specific verb ('List all patients'), resource ('CGM data from LibreLink Up follower account'), and distinguishes from siblings like patient-specific 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 explicitly says to use patientId for other tools, providing a clear use case. It also mentions the data is read-only and not for medical decisions without review. It lacks explicit when-not-to-use but strongly implies 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 provide limited behavioral info (readOnlyHint=false, openWorldHint=true, destructiveHint=false, idempotentHint=false). The description adds value by disclosing that invoke can run MCPs even if not installed, returns connect/checkout links for credentials/payment, and requires owner/admin for writes. It does not contradict annotations. However, some behaviors like idempotency are not addressed, but the description compensates well.

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 lengthy but structured well, starting with the tool's identity and core flow, then detailing specific actions and nuances. It is front-loaded with the most critical information. While it could be slightly more concise, every sentence adds value, and the structure aids readability.

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

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

Given the complexity (13 parameters, no output schema), the description covers the core flow and key behavioral details. However, it lacks information about return values for actions beyond invoke (e.g., what search/describe returns, format of results). The absence of an output schema increases the need for description to cover this, which it does not fully. Thus, completeness is adequate but has notable 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 description coverage is 0%, so description must compensate. The description explains the core parameters (action, query, mcp_id, tool_id, arguments) in context of the flow, but does not explicitly describe all 13 parameters (e.g., limit, message, immediate, tier_slug, conversation, request_name, report_context, request_details). This leaves gaps in understanding four parameters that are not covered, making it insufficient for full semantic clarity.

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 marketplace as the official catalog for MCPs/tools, with a specific verb+resource (search, describe, invoke, install). It distinguishes itself from siblings by emphasizing 'use THIS tool FIRST, before any external/generic registry.' The core flow (search→describe→invoke) is explicitly stated, providing clear purpose.

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 guidance on when to use (when user wants a capability) and when not to use (before external registries). It details the core flow and explains when to prefer invoke over install, with alternatives like install, list_tools, subscribe, cancel, report_bug, request_mcp described. It covers context dependencies like workspace owner/admin for writes.

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 idempotentHint true and destructiveHint false, signaling safe repeated use. The description does not add behavioral details beyond this, nor does it contradict annotations.

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

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

Two concise sentences that effectively convey the tool's purpose and a key usage instruction without 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?

The description adequately covers the main function and a critical usage detail (including conversation). It lacks explanation for the 'context' parameter but overall is sufficient for a simple feedback tool with good 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 coverage, the description must explain parameters. It only clarifies the 'conversation' parameter for reproduction. The 'message' parameter is implied by the purpose, but 'context' remains unexplained.

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 three purposes: report a bug, missing feature, or send feedback. It distinguishes the tool from sibling tools which are primarily about glucose monitoring and system functions.

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

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

The description advises including the conversation array for reproduction, which provides usage context. However, it does not explicitly state when not to use this tool or mention alternatives, but given its unique purpose, the guidance is 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?

Annotations already declare readOnlyHint=true and idempotentHint=true, indicating a safe, non-destructive operation. The description adds that it shows platform and adapter versions, providing specific context beyond the annotations. No contradictions.

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

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

The description is a single sentence that conveys the purpose without any wasted words. It is front-loaded 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 the zero-parameter input and no output schema, the description adequately covers the tool's purpose. It is complete for a simple version inspection 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 input schema is empty with 0 parameters, so schema description coverage is 100%. The description does not need to add parameter details since none exist. 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 it shows current MCP platform and adapter versions. The verb 'show' and resource 'versions' are specific, and it distinguishes well from sibling tools which handle authentication, data retrieval, 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 does not explicitly state when to use this tool versus alternatives. While it's implied for version checking, no exclusions or prerequisites are provided. For a simple tool, this is adequate but not exemplary.

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, indicating a safe read operation. The description adds useful context about the return content but does not disclose other behavioral traits.

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

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

The description is a single sentence that is clear and front-loaded. It could be structured with bullet points for readability, but overall it is concise 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?

Given zero parameters and no output schema, the description fully explains what the tool returns (installed MCPs, connection status, catalog tool counts), making it complete 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?

There are no parameters, so the baseline is 4. The description adds no parameter information, which 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 explicitly states the tool returns 'installed MCPs, their connection status, and how many catalog tools each exposes.' This is a specific verb+resource combination that clearly distinguishes it from sibling tools like 'authenticate' or 'connect'.

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

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

The description implies usage for checking toolkit state but does not provide explicit guidance on when to use this tool versus alternatives. No exclusions or when-not scenarios are mentioned.

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