healthguard

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

No annotations are provided, so the description must carry the full burden. It does not disclose output format, that it only generates a template (no submission), permissions, or side effects. The only behavioral detail is the inclusion of regulatory deadlines, which is minimal.

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 the action, and no redundant phrasing. Every word earns its place, making it easy for an agent to parse quickly.

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 lack of annotations and output schema, the description is underspecified. It does not explain what the agent should expect as output, how the inputs influence the generated template, or any limitations. This is inadequate for a tool with four parameters.

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 descriptions cover 50% of parameters (severity, reporter_type), but the tool description adds no meaning for the remaining ones (drug_name, event_description). With half the parameters undocumented, the description fails to compensate, leaving a significant gap.

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 specific verb ('Generate') and a precise resource ('pharmacovigilance adverse event report template') plus MedDRA context. It is clearly distinct from sibling tools, which cover trials, interactions, and compliance checks.

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 context for when to use the tool: generating an AE report template with EU/DE/US regulatory details. It implies use for pharmacovigilance reporting but does not explicitly state alternatives or exclusions, which keeps it at a 4.

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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does not explicitly state whether the tool is read-only, what output format to expect, or any limitations. The description is descriptive rather than behavioral, leaving the agent without clear expectations for invocation results.

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, using two sentences to convey the tool's scope. It is front-loaded with the primary topic and includes relevant details (EU CTR, phases, CTIS, German specifics) without unnecessary fluff. Every word 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 the tool's simple scope (one optional parameter, no output schema, no annotations), the description provides adequate context for what the tool covers. However, it could be slightly more complete by mentioning the return format or whether the tool is informational only, but this is not a major gap.

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 provides a description for the 'phase' parameter listing possible values (PRECLINICAL, PHASE_I, etc.), achieving 100% schema description coverage. The tool description adds no extra semantic information about the parameter itself, so it remains at the baseline of 3.

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 covering clinical trial phase requirements under EU CTR 536/2014, including Phase I-IV details, CTIS requirements, and German specifics. This distinguishes it from sibling tools that deal with other regulatory topics like adverse events or device classification. While it lacks an explicit verb like 'retrieves' or 'provides', the intent is clear.

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 that this tool is used when looking for clinical trial phase requirements under EU CTR, but it does not explicitly state when to use it over alternatives or provide exclusions. It offers context (EU CTR, CTIS, German specifics) but no direct guidance on tool 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?

No annotations are provided, so the description must disclose behavioral traits. It only states that the tool 'determines' a class, without mentioning how it handles missing information, edge cases, or any limitations. The agent is left uninformed about calculation assumptions or error behavior.

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 efficiently states the purpose and output. No redundant words or filler.

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

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

For a tool with 9 parameters, no required fields, and no output schema, the description is too thin. It does not describe the return value format, the decision logic, or what the parameters represent functionally, leaving significant gaps for an agent to use it correctly.

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

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

Schema description coverage is only 11%, with 9 boolean parameters. The description merely refers to 'device characteristics' without explaining how each parameter influences the classification. The agent lacks guidance on what each boolean means for the classification logic.

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: MDR Annex VIII medical device risk classification, determining class I/IIa/IIb/III. It includes a specific regulatory reference and output, distinguishing it from sibling tools focused on other regulatory topics.

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 context: use when needing MDR device classification. It does not explicitly list exclusions or alternatives, but the clear regulatory scope differentiates it from siblings, providing adequate situational guidance.

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

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

With no annotations, the description carries the full burden. It discloses that the tool is 'basic' and checks 'known high-risk combinations,' implying a limited scope. However, it does not describe output format, response structure, or how input is processed beyond the schema.

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

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

The description is two concise sentences, front-loaded with the core purpose, followed by a critical caveat and a clarifying statement. Every sentence earns its place with no redundant 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 is simple with a single parameter and no output schema, but the description still leaves gaps. It does not describe what the result looks like (e.g., a risk level, a list of interactions), which would be helpful for an agent to know what to expect. Overall it is minimally viable but not fully 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?

The schema already provides a complete description for the 'drugs' parameter (comma-separated drug names, min 2). The tool description adds no additional parameter-level detail, so the baseline score of 3 applies.

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

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

The description clearly states the tool's function: 'Basic drug-drug interaction risk check' and 'Checks known high-risk combinations.' This is a specific verb+resource construction that distinguishes it from sibling tools like adverse_event_report or icd10_lookup.

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

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

The description gives clear context by labeling it an 'educational tool' and explicitly excluding clinical use. It does not name alternative tools or provide a positive 'when to use' statement, but the limitations are clearly communicated.

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?

No annotations are provided, so the description carries the burden. It discloses the tool returns legal basis, DPIA requirements, and retention periods, but does not state whether the operation is read-only, whether data is stored, or any limitations/prerequisites.

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, front-loaded sentences with no filler. Every phrase carries meaning: scope, outcome, and output categories.

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 output schema and no annotations, the description lists key returned items, but omits details about output format, how inputs map to results, and when to use this vs siblings. It is adequate but not rich.

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 covers purpose and data_types with brief descriptions but does not describe cross_border_transfer. The tool description adds no parameter-level meaning and doesn't explain how the inputs influence the compliance result, leaving a gap especially for the undocumented boolean.

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?

Description names the specific GDPR Art.9 health data compliance assessment focus, with a clear verb/outcome ('Returns required legal basis, DPIA requirements, retention periods'). This distinguishes it from siblings like hipaa_vs_gdpr and mdr_compliance_check.

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 intended use is implied by the GDPR Art.9 health-data scope, but no explicit guidance is given for when to choose this over sibling tools like hipaa_vs_gdpr or mdr_compliance_check. No alternatives or exclusions are mentioned.

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?

With no annotations, the description must carry the full behavioral burden. It only lists content areas and gives no information about return format, how the facility_type parameter affects the checklist, or whether the tool is read-only. This leaves the agent guessing about the tool's behavior and output.

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

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

The description is a single, concise sentence that front-loads the core purpose and lists key areas, with no filler or redundant information. Every word 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?

The tool has an optional parameter that is not explained in the description, and there is no output schema to indicate return structure. The description does not explain how facility_type tailors the checklist or what the agent should expect as output, making it incomplete for confident 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?

The input schema fully documents the single optional parameter (facility_type) with allowed values in its description, so baseline is 3. The tool description adds no additional context about how this parameter influences the checklist, despite the schema not using enums.

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 a GMP audit checklist under EU EudraLex Vol.4, listing covered areas (QMS, personnel, premises, documentation, production, QC). It distinguishes itself from sibling tools focused on other regulatory topics, though it lacks an explicit action verb like 'generate' or 'provide' to say what the tool does.

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 GMP audit contexts via the topic and reference to EU regulations, and the sibling tools cover different domains (e.g., clinical trials, device classification). However, it does not explicitly state when to use this tool versus alternatives or any exclusions, leaving usage to be inferred from the niche topic.

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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It indicates the output is a 'side-by-side analysis' but does not specify whether it is a static reference, whether any data is fetched, or how the results are presented (e.g., text, comparison table, report). There is no mention of access restrictions or data handling, though the tool appears read-only in nature.

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 and immediately front-loads the core purpose ('HIPAA vs GDPR comparison'). Every word is substantive, with no redundancy or fluff. It is optimally concise while conveying target audience and key benefit.

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 informational tool, the description covers the topic, target audience, and expected output format ('side-by-side analysis with dual-compliance tips'). It could be more explicit about whether this is an informational reference or an interactive tool, but given the simplicity and lack of parameters, the description is adequately 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?

The input schema has zero parameters, and the schema description coverage is essentially complete (vacuous). The baseline for no parameters is 4, and the description does not need to elaborate on any parameters since none exist. The description adds no parameter-related details, 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 clearly states a comparative analysis tool ('HIPAA vs GDPR comparison') for a specific audience ('healthcare organizations'), with a concrete deliverable ('side-by-side analysis with dual-compliance tips'). This distinguishes it from sibling tools like gdpr_health_check or mdr_compliance_check, which focus on specific compliance checks rather than a comparative overview.

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 when a side-by-side comparison of HIPAA and GDPR is needed, but it does not explicitly mention when to use this tool over alternatives like gdpr_health_check. There is no guidance on exclusions or prerequisites, leaving usage to be inferred from the subject matter.

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?

With no annotations, the description must carry the transparency burden. It usefully discloses that the tool uses ICD-10-GM and is limited to common codes and chapter overview, which are meaningful constraints. However, it does not describe output format, error handling, or behavior when both 'code' and 'search' are provided.

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 short sentences, front-loaded with the core purpose, and contains no filler. Every clause adds information: the action, the resource, the coverage scope, and the German modification.

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 is relatively simple with two optional parameters and no output schema, but the description omits expected output/return behavior and the relationship between code and search. It covers scope and language variant, yet an agent might still be uncertain about what to expect when invoking it.

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 100%, so the parameters are already documented. The description does not add extra usage details about code/search syntax, response format, or interaction between parameters, which is acceptable at the baseline.

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 states a specific verb ('lookup and search') and resource ('ICD-10 code'), making the tool's purpose immediately clear. It also adds scope ('common codes and chapter overview') and locale ('ICD-10-GM'), which helps differentiate it from the unrelated sibling regulatory 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 provides clear context that this tool is for ICD-10 code lookup/search and clarifies its scope (common codes, chapter overview). It does not explicitly name alternatives or exclusions, but no sibling tool offers similar functionality, so the usage context 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?

No annotations are provided, so the description must carry the full burden. It does not disclose what the tool actually does (e.g., read-only lookup, mutation, required authentication) or what the response contains, leaving behavior completely opaque.

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

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

The description is a single concise sentence with no filler, but it is under-specified. Being short is good, but it sacrifices the clarity needed for a useful tool description.

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 compliance-check tool with no output schema, the description should explain what information is returned (e.g., risk class, notified body, deadlines). It only lists topics without describing the tool's behavior or return value, leaving the agent with insufficient context.

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

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

Schema coverage is only 50% (risk_class has a description, device_name does not). The description mentions risk class A-D but does not explain how risk_class or device_name are used in the compliance check, adding little value beyond the 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 names the topic (EU IVDR 2017/746 compliance) but lacks a specific action verb. It does not clarify whether the tool checks compliance, provides requirements, or returns a classification, making the purpose vague and not clearly distinct from sibling tools like mdr_compliance_check.

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?

There is no guidance on when to use this tool vs alternatives. It does not mention prerequisites, exclusions, or situations where another tool (e.g., mdr_compliance_check) would be more appropriate.

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?

With no annotations provided, the description carries the full transparency burden. It discloses the inputs checked and the output (compliance score) but does not mention whether the operation is read-only, how the score is computed, the score range, or how missing boolean fields are handled. This is moderate transparency but leaves gaps.

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, front-loads the core purpose, and each word adds value. It efficiently lists the checks and the return type 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?

The tool has 9 parameters and no output schema, so the description must be self-sufficient. It covers the purpose, the factor areas checked, and the return of a compliance score. However, it omits the score interpretation, whether missing parameters are treated as false, and any prerequisites like device class. This is adequate but not fully complete for a tool with this many inputs.

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 only 22% (device_name and device_class have descriptions, booleans only have types). The description compensates by listing the compliance checks (CE marking, Notified Body, Clinical Evaluation, PMS, UDI, QMS, EUDAMED), which map to the boolean parameters and give them meaning. It does not explain exact parameter value effects or score calculation, but it adds significant semantic context.

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 performs 'EU MDR 2017/745 compliance assessment for medical devices' and specifies it checks CE marking, Notified Body, Clinical Evaluation, PMS, UDI, QMS, and EUDAMED, returning a compliance score. This distinct verb+resource combination separates it from sibling tools like ivdr_check and udi_validate.

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

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

The description gives clear context: use this for EU MDR compliance assessment of medical devices. It does not explicitly exclude non-medical devices or mention alternatives, but the reference to EU MDR 2017/745 and the sibling ivdr_check imply differentiation. No explicit 'when not to use' is provided, so it stops short of a 5.

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

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

No annotations are provided, so the description carries the full burden. It discloses the content areas (MDR/IVDR, AI Act, NIS2, PSUR) but does not describe behavioral aspects such as whether the operation is read-only, the output format, pagination, or any side effects. This leaves significant gaps in understanding what the tool returns.

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, consisting of one short sentence and a list of topics. It is efficient and avoids verbosity, though it lacks a verb for structural completeness.

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 and no annotations, the description needs to explain what the tool returns or how it should be used. It only lists topic areas, leaving the agent uncertain about the actual output (e.g., a list of dates, explanations, or compliance statuses). This is inadequate for a tool with only a single optional parameter.

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 100% coverage for the single 'year' parameter, including its default value (2026). The description adds no parameter-specific detail, but the baseline of 3 applies because the schema already fully documents the parameter.

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 identifies the resource as 'EU healthcare regulatory deadlines' and enumerates specific areas (MDR/IVDR transitions, AI Act obligations, NIS2, PSUR deadlines), which gives some clarity. However, it lacks a verb stating what the tool actually does (e.g., 'returns', 'lists'), making it a noun phrase rather than a clear action.

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 guidance is provided on when to use this tool versus siblings like mdr_compliance_check or ivdr_check. The description implies a general calendar of deadlines but gives no exclusion criteria or alternative recommendations.

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?

With no annotations, the description must carry the full transparency burden. It discloses the tool's core behavior (validation and agency identification) but does not clarify whether it is read-only, how invalid input is handled, or what the response structure looks like. These aspects are left inferred from the tool's name and nature.

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 no filler; the first states the validation purpose and regulatory reference, the second adds the agency identification output. Every word earns its place.

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

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

For a simple single-parameter validation tool, the description adequately covers purpose, regulatory basis, and an additional output detail. It does not specify return format or error behavior due to the absence of an output schema, but the tool's simplicity makes these minor gaps acceptable.

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 fully describes the single 'udi' parameter as 'UDI string to validate' (100% coverage). The tool description adds regulatory context but no additional parameter-level detail such as formatting examples or optionality, so it meets the baseline without exceeding it.

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

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

The description uses the specific verb 'Validate' and names the resource 'UDI format' with reference to MDR Art. 27, clearly distinguishing it from sibling tools that handle adverse events, classification, or other regulatory checks.

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

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

The description makes the use case explicit: validating UDI format and identifying issuing agency. However, it does not mention when to prefer this tool over alternatives or any exclusions, so it lacks explicit when-not-to-use guidance.

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