gapup-mcp

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

Annotations already declare readOnlyHint=true and openWorldHint=true, indicating safety. The description adds that inputs are validated server-side, consistent with read-only behavior. No side effects or 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 concise, consisting of a few sentences. It front-loads the tool name and purpose. However, the reference case could be seen as jargon-heavy, though not overly verbose.

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 with 8 parameters, nested objects, and no output schema, the description lacks detail about the deliverable's structure, prerequisites, or result format. It is insufficient for an agent to fully understand the tool's capabilities.

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 schema description coverage at 13%, the description does not explain parameters beyond vague phrasing 'send the documented case fields'. It fails to add meaning to the 8 nested parameters, which the schema only partially documents.

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 tool as 'Architecte ABM' targeting C-suite expertise for ABM strategy. It mentions a reference case and states it returns a structured deliverable, clearly distinguishing it from siblings like 'abm_lookalike_account_finder' which focuses on finding lookalike accounts.

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 creating an ABM architecture but does not specify when not to use it or alternatives. The reference case provides a context, but no explicit guidance on selection vs. siblings.

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 provide readOnlyHint=true, openWorldHint=true, and idempotentHint=true. The description adds valuable context: it reveals that the tool analyzes public web data (robots.txt and OpenGraph metadata) and returns a ranked list. This goes beyond annotations by clarifying the data sources and result structure.

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-loading the core purpose and result. Every sentence adds value: the first defines the output and target user, the second explains the methodology and data sources. 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 the presence of annotations (readOnly, openWorld, idempotent) and an output schema, the description covers the main aspects: purpose, inputs, data sources, and output type. It could be improved by mentioning the async polling option and that results are ranked, but it is largely 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 description coverage is 75% (3 of 4 top-level parameters have descriptions). The tool description reinforces the role of firmographic filters and tech stacks but does not add new semantic details beyond what the schema provides. The description does not compensate for missing schema description on the firmographic_filters 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 clearly states the tool's purpose: 'discover 50 B2B accounts that closely match your top 10 customers' tech stacks and firmographics.' It specifies the input (customer domains and firmographic filters) and output (ranked list), and uses a specific verb-resource combination that distinguishes it from sibling ABM 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 implies usage for targeted ABM campaigns by a CMO, but does not explicitly state when to use this tool over alternatives or provide any exclusion criteria. Without mentioning sibling tools or specific use cases, the guidance is only 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 already declare readOnlyHint=true and openWorldHint=true. The description adds that it returns an audited deliverable and that inputs are validated server-side. This provides some additional context but does not delve into behavioral traits like rate limits or destructive actions beyond what annotations imply.

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 short and to the point, but the inclusion of French and the jargon 'Gapup agent-payable C-suite expertise (CRO)' reduces clarity. It front-loads the main point but could be more universally understandable.

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 complex nested parameters, no output schema, and no output description, the description is incomplete. It does not explain what the deliverable contains or how to interpret the result, relying solely on a reference case that may not generalize.

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 20% (only 'async' has a description). The description does not explain any parameters beyond 'send the documented case fields.' This lacks semantic support for the nested objects (company, accounts, ownership) which have many fields without descriptions.

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 indicates the tool returns a structured, audited deliverable for account expansion mapping, with a reference case. However, it could be more explicit about the verb (e.g., 'Generate' or 'Map') and the phrasing 'Gapup agent-payable C-suite expertise (CRO)' is confusing. It distinguishes itself from siblings due to its unique focus on expansion mapping.

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 vs alternatives. The reference case hints at B2B enterprise expansion with strategic accounts, but no when-not or alternative tools are mentioned. Sibling tools are numerous but none directly compete.

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 include readOnlyHint and openWorldHint. The description adds that inputs are validated server-side and returns a structured deliverable, but does not disclose potential side effects or async behavior beyond what the async parameter suggests. 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 relatively short but includes some jargon ('Gapup agent-payable C-suite expertise') that may not be helpful. It could be more streamlined.

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 (nested objects, 8 parameters, no output schema), the description lacks detail on output format, async handling, and parameter constraints. It is insufficient for fully accurate invocation.

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

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

Schema description coverage is low (13%). The description does not explain any parameters beyond general mention of 'documented case fields,' failing to compensate for the missing schema descriptions.

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 a structured ESG action plan deliverable and includes a reference case. However, it does not differentiate from sibling tools like esg_audit_multi or sustainability_report, so a slight deduction.

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 mentions inputs are validated server-side but provides no guidance on when to use this tool versus alternatives. There is no explicit when-to-use or when-not-to-use context.

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

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

Annotations already indicate readOnlyHint, openWorldHint, and idempotentHint. The description adds value by disclosing async behavior, required async:true, and output structure (risk scores, failure modes, adversarial examples, status, warnings, source references). 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?

Three sentences, front-loaded with purpose, no extraneous information. Every sentence serves a purpose: purpose/framework, async requirement/target audience, output summary. Highly concise and 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?

Given the tool's complexity (5 params, output schema exists, annotations rich), the description covers key aspects: purpose, framework, async requirement, personas, and output types. It does not mention the custom adversarial dataset or sensitivity threshold, but schema descriptions already handle those, making the description complete enough.

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 baseline is 3. The description mentions 'model outputs or decision boundaries' which aligns with the modelOutput parameter, but does not add further semantic enrichment beyond what the schema provides for individual 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?

The description clearly states the tool evaluates AI model resilience against adversarial inputs following NIST AI RMF protocols, specifying verbs and resources. However, it does not explicitly differentiate from sibling tools like jailbreak_attempt_detector or model_behavior_drift_monitor, lacking direct sibling differentiation.

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 usage guidance by noting that async:true is required to avoid timeout errors, and identifies target personas (security/compliance). It does not explicitly state when not to use the tool or mention alternatives, leaving the usage context implied rather than explicit.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and openWorldHint=true. The description adds valuable context: data source (Common Crawl), analyzed patterns (duplicate IPs, rapid clicks, device spoofing), and output types (fraud probability score, suspicious IP list, pattern analysis). 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 efficient: three sentences covering function, user, inputs, and outputs, plus a keywords list for searchability. It is front-loaded with the core action. The keywords section is slightly redundant but not detrimental.

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 (4 params, nested object, output schema exists), the description covers key aspects: purpose, data source, target user, and output types. It lacks details on error handling or data freshness, but the output schema supplements return value documentation. Overall, adequate for selection and invocation.

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

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

Schema description coverage is 75%, leaving threshold and async described only in the schema. The tool description mentions affiliate_network and date_range but not threshold (with default and range) or async. Since baseline is 3 with high schema coverage, the description adds minimal additional meaning 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 clearly states the tool's function: analyzes affiliate clickstream data to flag fraud patterns such as duplicate IPs, rapid clicks, and device spoofing. It also specifies the target user (CMOs) and the problem it solves (preventing budget waste). This distinguishes it from other fraud detectors in the sibling list, e.g., 'fraud_detector' or 'x402_payment_fraud_detector', which target different domains.

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 affiliate traffic validation but does not explicitly state when to use this tool versus alternatives. It provides no exclusions or comparisons to other fraud detection tools. The phrase 'Designed for CMOs' offers some context, but lacks guidance on 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 readOnlyHint and idempotentHint. The description adds value by naming data sources, output structure, and async behavior advice.

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: purpose and data sources, inputs/outputs, async advice. No redundant information, 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?

Covers purpose, inputs, outputs, data sources, and async usage. Output schema exists, so return format details are covered. Complete for this 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?

Schema coverage is 100%, so baseline is 3. The description adds async usage context and mentions origin/destination and HS codes, but does not significantly enhance parameter understanding 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 the tool analyzes non-tariff trade barriers using specific data sources (WITS and UNCTAD STAT) and distinguishes from siblings like africa_trade_finance_esg_rater by focusing on NTBs.

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 use as a COO and mentions async usage to avoid timeout, but does not explicitly exclude alternatives or specify 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, openWorldHint, and idempotentHint. The description adds contextual details: use of specific data sources (World Bank WITS, CDP), the async parameter to avoid timeout, and the breakdown dimensions. No contradictions, and it enhances transparency 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?

The description is three sentences: first states purpose and data sources, second describes input/output, third provides use case and async tip. Every sentence is valuable, front-loaded, and no wasted words. Excellent conciseness.

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

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

Given the tool has an output schema (not shown), the description does not need to detail return values. It covers key aspects: data sources, input, output dimensions, use case, and async behavior. Could be more explicit about error handling for unknown institutions, but overall sufficient.

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

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

Schema coverage is 100%, so baseline is 3. The description mentions 'Input the financial institution's name or identifier', which aligns with the 'institutionName' parameter description in the schema. It does not add significant new meaning beyond what the schema already provides.

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 evaluates ESG compliance of African trade finance providers using specific data sources (World Bank WITS, CDP) and returns a rating with breakdown across E, S, G dimensions. It uniquely targets African trade finance providers, distinguishing it from broader ESG tools like 'supplier_esg_audit' or 'esg_audit_multi'.

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 mentions the tool is 'Ideal for due diligence on trade partners or portfolio risk assessment', which provides usage context. However, it does not explicitly state when not to use it or suggest alternative tools, leaving some ambiguity for the agent.

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 (readOnlyHint, openWorldHint, idempotentHint), the description adds critical behavioral detail: the async parameter is required to avoid x402 timeout, which is a significant operational constraint. It also mentions data sources (WITS and OECD), enhancing transparency. 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 concise (4 sentences) and front-loaded with purpose. The first sentence states the main function, the second details data and outputs, and the third provides a crucial usage note. No unnecessary words, each 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's complexity (6 parameters, async requirement, output schema present), the description covers the domain, data sources, and target audience. It mentions key outputs (duty savings, market access, compliance). It is nearly complete but could benefit from a brief example or reference to the output schema structure.

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% description coverage, so the baseline is 3. The description adds context by linking parameters to the tool's goal (e.g., preference_scheme tied to AGOA/EBA, exporting_country to African countries), but does not significantly deepen understanding of individual parameters 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 clearly specifies the tool's purpose: analyzing AGOA and EBA trade preference arbitrage opportunities for COOs. It uses specific verbs and resources (compares tariff rates, trade volumes, preference utilization) and distinguishes from siblings like africa_trade_preference_optimizer and agoa_eba_intelligence by focusing on arbitrage opportunities and comparative analysis.

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: it is for COOs evaluating export strategies using specific trade data sources. However, it does not explicitly state when not to use this tool or mention alternatives among siblings like africa_trade_barrier_breaker. The implied usage is strong but lacks 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 already declare readOnlyHint=true, idempotentHint=true, and openWorldHint=true. The description adds behavioral context by stating it uses specific data sources (UN Comtrade, WCO tariff schedules, AU rules) and mentions the async parameter in the schema, which affects execution behavior. 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 two paragraphs: first covers the primary purpose and outputs, second provides keywords and use cases. It is front-loaded with the main function and avoids unnecessary verbosity. Could be slightly tighter but overall concise.

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 6 parameters (3 required) and an output schema, the description covers the main inputs, outputs, data sources, and use cases. It does not explain the async behavior explicitly (though schema does) but is otherwise sufficient for an agent to understand when to use it. Fairly 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 100% with all parameters described. The description reiterates the main three required parameters but adds no additional meaning beyond the schema. For instance, the async parameter is fully described in the schema, and the description does not explain how quantity and value are used in the analysis. Baseline 3 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 analyzes AGOA/EBA duty savings opportunities with HS code-level trade route optimization. It specifies inputs (origin country, destination country, HS code) and outputs (duty savings estimates, optimal trade routes, recommendations). While it does not explicitly differentiate from all sibling tools, the focus on duty savings and trade routes provides a 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 mentions use cases ('Ideal for export market evaluation, supply chain optimization, and trade agreement compliance analysis') but does not provide guidance on when not to use this tool or suggest alternatives among the many sibling tools. The context is implied but not explicit.

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

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

Annotations already declare 'readOnlyHint: true' and 'openWorldHint: true'. The description adds context about data sources (AGOA.info, EU EBA) and country coverage, but does not disclose additional behavioral traits like response time, caching, or rate limits.

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

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

The description is a single, front-loaded paragraph that efficiently conveys the tool's purpose and capabilities. It could benefit from slightly more structure (e.g., bullet points), but it is not verbose and each 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 the tool's complexity (multiple modes, 5 parameters, no output schema), the description explains modes and differentiators but does not describe the return format or result structure. This gap in completeness is notable for an intelligence 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?

Parameter descriptions in the schema cover all 5 parameters (100% coverage). The description echoes the mode options and provides some examples but does not add significant new semantic information beyond what the schema already provides. Baseline of 3 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 checks AGOA/EBA/GSP eligibility for African countries, HS code product eligibility, identifies export opportunities, and provides compliance rules. It mentions specific country counts and sources, but does not explicitly differentiate from sibling tools like 'africa_trade_preference_arbitrage' or 'africa_trade_preference_optimizer'.

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 scenarios (checking eligibility, opportunities, compliance) but does not provide explicit guidance on when to use this tool versus alternatives, nor does it mention 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?

The annotations declare readOnlyHint: true, but the tool generates playbooks and notification drafts, which are write operations. The description does not clarify this contradiction and only mentions async behavior. The description contradicts annotations, making behavioral transparency poor.

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 paragraph that front-loads the purpose, then inputs/outputs, a critical usage note, and keywords. It is reasonably concise with no redundancy, though it could be slightly shorter. Structure is effective.

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, key inputs/outputs, and async requirement. With an output schema present, return values need not be detailed. However, given the tool's complexity (generating playbooks with multiple parameters), the description omits detailed parameter behavior and does not address the contradiction with readOnlyHint. It is adequate but has 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 only 17%, yet the description only lists three of six parameters (incident severity, AI system type, affected stakeholders) and mentions async in the usage note. It does not describe incident_type, incident_description, or fully explain async. With low coverage, the description adds some value but is insufficient to compensate.

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 generates EU AI Act incident response playbooks with regulator notification templates, specifying inputs and outputs. It is specific to high-risk AI system breaches and includes relevant keywords. However, it does not explicitly differentiate from sibling tools like ai_act_training_data_audit or ai_governance_full_report_async, though the purpose is distinct enough.

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 indicates the tool is 'Essential for high-risk AI system breaches requiring formal EU notification' and advises passing async:true to avoid timeout. It provides context for when to use but does not give explicit when-not-to-use or mention alternatives. Usage guidance is present but lacks exclusions or comparisons to siblings.

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, openWorldHint, and idempotentHint. The description adds context about accepting system descriptions and returning structured feedback with warnings and citations, which complements the 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?

The description is concise at four sentences, front-loading the purpose. While efficient, the first sentence is somewhat lengthy, slightly reducing clarity.

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, inputs, outputs, and even cites sources (EUR-Lex, OECD). Given the presence of an output schema, it adequately explains what the tool returns without needing to detail return values.

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?

Input schema covers all 6 parameters with descriptions (100% coverage). The description provides a general overview of input types but does not add specific parameter details beyond what the schema offers, warranting a baseline score 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 states the tool simulates EU AI Act regulatory sandbox submissions, specifying verb 'simulating' and resource 'regulatory sandbox submissions'. It differentiates from siblings like ai_act_incident_response by focusing on regulatory sandbox submissions, making its purpose distinct.

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 targets legal teams and AI developers, implying use for compliance testing. However, it does not specify when not to use this tool or mention alternative tools, leaving some ambiguity.

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 provide readOnlyHint, openWorldHint, idempotentHint. The description adds value by noting the async option and timeout avoidance, and lists outputs. 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?

Two sentences: first explains purpose and inputs/outputs, second advises on async usage. Front-loaded, no fluff, 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?

With output schema present, the description covers purpose, inputs, outputs, and usage context. The async tip adds practical guidance. Complete for a compliance audit 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?

Schema description coverage is 50%. The description reiterates dataset identifier and risk thresholds but adds no new meaning beyond the schema. Does not compensate for the missing schema descriptions.

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 audits AI training datasets for EU AI Act compliance, with bias detection and regulatory risk assessment. It specifies inputs, outputs, and distinguishes from siblings like ai_act_incident_response.

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 says 'Ideal for pre-deployment risk evaluation', giving clear context, but does not explicitly state when not to use or compare with alternatives among siblings.

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 annotation readOnlyHint=true contradicts the description's implication that submitting a job creates a new async task, which is a mutation. The description does not resolve this inconsistency and fails to disclose the side effect of job creation.

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 verbose, listing 7 output items in a dense block. While informative, it could be more structured and concise. The disclaimer adds length but is necessary.

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 output schema existence and full parameter descriptions, the description provides a comprehensive overview of the async workflow, caching, and recommendations. Missing idempotency details but otherwise complete for a complex async 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 covers all parameters with descriptions (100% coverage), so the description adds minimal semantic value. It mentions required fields but does not explain their purpose 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 clearly states the tool performs a full EU AI Act audit, classifying risk tiers and producing 7 specific outputs. It distinguishes from sibling tools by mentioning async submission and webhook usage, and references the ai_governance_full_report_result poller.

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 guidance on when to use this tool: for a comprehensive EU AI Act audit. It contrasts with polling (ai_governance_full_report_result) and recommends webhooks for faster interaction. However, it does not explicitly state when not to use it or alternatives like ai_governance_pilot.

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: enumerates all possible statuses (pending, completed with report details, failed, not_found), the TTL, and the timing hint. Annotations already declare readOnly and idempotent, so the description complements them well 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 sentences, front-loaded with purpose, succinctly covering return values and timing. Every sentence adds value, no fluff.

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

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

For a polling tool, the description fully informs the agent: when to call (after eta_seconds), what statuses to expect, what the completed report includes (detailed list), and expiration (TTL 24h). The presence of an output schema (though not shown) complements the description.

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?

Input schema covers the sole parameter with a clear description (job_id returned by async tool, prefix aigfr_). The tool description adds no further meaning; schema coverage is 100%, so baseline 3 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 'Poll the result of an ai_governance_full_report_async job,' using a specific verb and resource. It distinguishes itself from the sibling async initiation tool by naming it explicitly and describing the polling nature.

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 clear context: call after the eta_seconds hint (~90s), and notes the TTL (24h). Does not explicitly exclude other uses, but the polling intent is unambiguous and the sibling async tool makes usage boundaries obvious.

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

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

Annotations already indicate readOnlyHint=true and openWorldHint=true. The description adds that inputs are validated server-side and returns a structured deliverable, which is useful but does not 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?

The description is concise (3 sentences) and front-loaded with the French-English title. The reference case adds context but could be omitted for brevity. Slightly verbose.

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 complexity (5 params, nested objects, no output schema), the description does not explain the deliverable contents, how to use async, or handle results. Incomplete for proper usage.

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 low (20%), and the description does not elaborate on parameter semantics beyond 'send the documented case fields'. The async parameter is described in schema but not in description. The nested objects lack 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 clearly states the tool is for AI governance pilot, returning a structured deliverable. It references a case. However, it does not explicitly distinguish from sibling tools like ai_governance_full_report_async.

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 it's a pilot for governance, but does not provide criteria for when to use this vs. alternatives, nor 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 and openWorldHint=true. The description adds that it returns a structured audited deliverable and that inputs are validated server-side, but does not disclose any additional behavioral traits (e.g., async, rate limits, or what 'audited' entails). Minimal value added over 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 short (2 sentences) and front-loaded with the name and reference case. However, the second sentence is vague and lacks specificity. While concise, it sacrifices informativeness.

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 (5 parameters with nested objects, no output schema), the description is insufficient. It does not explain the deliverable's structure, required fields, or how to use the output. The reference case provides some context but is incomplete.

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 20% (async parameter described). The description does not explain the key parameters (company, signals, employees) beyond telling users to 'send the documented case fields.' This adds no semantic meaning and fails to compensate for the schema's lack of descriptions.

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

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

The description provides a clear purpose: detect employees at risk of leaving (anti-démissions), returning a structured audited deliverable. The reference case with Buffer Inc illustrates the functionality. However, it could be more explicit about the exact verb and resource (e.g., 'detect at-risk employees').

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 offers no guidance on when to use this tool versus alternatives. Sibling tools include churn_defender, talent_poaching_risk, etc., but the description does not differentiate or state appropriate contexts. The note about server-side input validation is technical but not usage 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?

Beyond annotations (readOnlyHint, idempotentHint), the description adds valuable behavioral context: 24h cache, no API key required, data stability, and the limitation to public awards only. This fully equips the agent to understand the tool's behavior and 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?

The description is well-structured with bullet-pointed modes, front-loading the purpose. It is slightly verbose but every sentence adds value. Could be tightened by removing redundant phrases like 'Note: Most arbitration awards are confidential' which is already implied.

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

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

While the description covers the three modes well, it does not explain how date parameters (date_from, date_to) apply to all modes, only mentioning them for institution_index. The 'async' parameter is present in the schema but omitted from the description. Given the output schema exists, return values are not needed, but the missing parameter details reduce completeness.

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 100%, but the description adds meaning beyond the schema by explaining how the 'query' parameter behaves differently per mode (e.g., 'searches 20 landmark public awards + JusMundi best-effort' for party_lookup) and clarifying the 'institution' parameter limitations. However, the 'async' parameter is not mentioned at all.

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: commercial arbitration intelligence with three specific modes (party_lookup, institution_index, clause_check). It distinguishes itself from sibling tools by focusing on arbitration awards and clause auditing, and no sibling tool duplicates this 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 provides clear guidance on when to use each mode and notes that most awards are confidential, only public ones are available. It also mentions cache duration and no API key needed. However, it does not explicitly state when not to use the tool or suggest 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 indicate safe read (readOnlyHint: true). The description adds that it returns a structured, audited deliverable and that inputs are validated server-side, providing some behavioral context, but does not go beyond annotations meaningfully.

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 somewhat verbose and includes mixed language (French/English) and a reference case. It front-loads the purpose but could be more concise and 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?

Given the tool has 6 parameters and no output schema, the description provides examples but does not fully explain the return format or all inputs. Partially complete but could be more 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 only 17%, and the description does not elaborate on most parameters (focus, exclusions, scope_cidrs, etc.). Only domain and include_email_surface are implied through examples, but not clearly explained.

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 that the tool monitors attack surface and returns a structured deliverable, with example queries. However, it does not explicitly distinguish itself from sibling tools like cyber_risk_auditor or cve_security_lookup, missing some specificity.

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 example use cases (CISO-ready report, M&A due diligence, email phishing risk) but lacks explicit guidance on when not to use or alternatives. No exclusions or comparisons to siblings.

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 openWorldHint, so the description adds little behavioral context beyond stating that inputs are validated and a deliverable is returned. It does not contradict annotations but adds only marginal value.

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, somewhat messy paragraph that mixes French and English. The reference case is specific and not generic, making it less concise. It could be better structured with clear sections or bullet points.

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 (nested objects, multiple required fields, no output schema), the description lacks necessary context. It does not explain the deliverable's structure, how the 'async' parameter works, or what the return value looks like, leaving significant gaps for the 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 only 17% schema description coverage, the description should explain parameter meaning, but it does not provide any details about the six parameters (company, audit, financials, etc.). The schema itself has some descriptions, but the description fails to compensate for the low coverage.

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

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

The description clearly states it performs a pre-audit for accounting, targeting CFO-level users, and returns a structured deliverable. The reference case provides a concrete example. However, the mix of French and English may cause ambiguity, and it doesn't explicitly differentiate from similar sibling tools like 'qa_pre_flight'.

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 the tool is for CFO-level expertise and mentions that inputs are validated server-side, but it does not provide explicit guidance on when to use this tool versus alternatives, nor does it specify any prerequisites or context for 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?

The description adds value beyond annotations by revealing data sources (World Bank, ECB SDW), the analytical process ('data-driven negotiation recommendations'), and output specifics (benchmarks, levers). This aligns with the readOnlyHint and idempotentHint annotations, confirming non-destructive analysis. No contradictions found.

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 plus a keyword list, efficiently conveying purpose and context. It is front-loaded with the core function and target audience. The keyword section could be omitted or integrated, but overall it is concise and readable.

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 rich annotations (readOnly, idempotent) and existence of output schema, the description provides sufficient context. It explains what the tool does, its inputs, and outputs (benchmarks, recommendations). The async parameter is covered by schema, and the description implies the tool is analytical, not action-oriented.

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 covers 100% of 7 parameters with descriptions, so the baseline is 3. The description mentions 'current fees' and 'bank details' but does not elaborate on parameter syntax or defaults. It adds marginal context by grouping fee types but does not significantly enhance schema content.

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 CFO-focused bank fee negotiator that analyzes fee structures and provides negotiation recommendations. It specifies the analysis areas (account maintenance, wire transfers, credit lines) and data sources (World Bank, ECB SDW), differentiating it from broader financial tools. However, it does not explicitly distinguish from sibling tools like 'treasury_optimizer' or 'working_capital'.

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 when the user has current fee data and wants to optimize costs, stating 'Input your current fees and bank details to receive benchmark comparisons...' It uses keywords suggesting cost optimization and treasury management. However, it lacks explicit guidance on when not to use this tool or mention of alternatives, which would be helpful given the many sibling tools.

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

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

Annotations indicate readOnlyHint and openWorldHint, but description does not mention the async parameter present in schema, which is a significant behavioral trait. No disclosure of polling mechanism.

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 relatively concise but includes a lengthy reference case that may not be essential and uses French terminology without translation, potentially confusing some users.

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 of 5 parameters including nested objects and no output schema, the description is incomplete: no explanation of return format, no mention of async behavior, and insufficient parameter guidance.

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 schema description coverage at 20%, the description should add meaning for parameters, but it only says 'send the documented case fields' without explaining any parameter details, such as the structure of competitor or dealContext objects.

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 states it returns a structured, audited deliverable and mentions a reference case, but the concept of 'battle card' is not clearly defined, and it does not distinguish from sibling competitor tools like competitive_deep_dive or competitor_profiles.

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 on when to use this tool vs alternatives. Does not mention any conditions or contexts that would make it preferable to sibling tools like competitor_intel.

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 openWorldHint=true, so the read-only nature is clear. The description adds that inputs are validated server-side, a minor behavioral detail. It does not disclose async behavior (despite the 'async' parameter), error handling, or any rate limits. Overall, the description adds little 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?

The description is brief (two sentences) and to the point, conveying the core purpose and a reference case. It avoids unnecessary words. However, it could be slightly more structured to include key aspects like output summary or 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?

Given the tool has 11 inputs and no output schema, the description is insufficient. It does not explain what the 'structured, audited deliverable' contains, how to interpret the result, or any dependencies between inputs. The reference case provides a concrete example but does not generalize. The agent likely needs more context to use this tool effectively.

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 9%, with only the 'async' parameter having a description. The other 10 required parameters have no descriptions in the schema. The tool description does not explain any of these parameters, relying solely on their names (e.g., companyName, arrCurrent). While parameter names are somewhat informative, the agent lacks guidance on formats, constraints (e.g., quarter format), or how to fill them correctly.

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 name and description clearly indicate it generates a marketing battle plan ('Plan de bataille marketing'). The phrase 'Returns a structured, audited deliverable' reinforces the purpose. However, it lacks explicit differentiation from sibling marketing tools like 'brand_builder' or 'positioning_strategist', and the deliverable's exact nature is vague.

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 is given on when to use this tool vs alternatives. The reference case (Gapup Hub) provides a concrete scenario but does not explain under what circumstances this tool is appropriate or preferred over similar tools. 'Inputs are validated server-side' implies no manual validation needed, but no when-not-to-use information.

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 provide readOnlyHint, idempotentHint, openWorldHint. The description adds that it returns 'structured bias metrics and amplification trends,' which is consistent but does not elaborate on behavior like error handling, result update cadence, or output schema details. It does not 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 front-load key purpose and user context. No fluff, but could be slightly more structured (e.g., list inputs/outputs). Still 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 tool's 4 optional params, output schema existence, and rich annotations, the description is mostly complete. It covers purpose, inputs, and output type. Missing are prerequisites (e.g., need for HuggingFace access) and explicit guidance on when to prefer this over similar bias/drift tools.

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?

All 4 parameters have schema descriptions (100% coverage). The description mentions 'model identifiers or output samples' and 'demographic groups to monitor' but adds no new meaning beyond the schema, e.g., no parameter relationships, default values, or format hints.

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 action ('Tracks bias amplification') and resource ('LLM outputs') and specifies it analyzes fairness metrics from HuggingFace. It identifies the target user ('risk assessment personas') and differentiates from siblings like adversarial_input_stress_tester or model_safety_certification_checker by focusing on demographic/gender/racial bias amplification.

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 when to use (for risk assessment, detecting bias in generated text) and inputs (model identifiers or output samples), but does not explicitly state when not to use or mention alternative tools for related concerns like overall model safety (sibling model_safety_certification_checker).

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 provide readOnlyHint, openWorldHint, and idempotentHint. The description adds minimal behavioral context (e.g., 'quickly assess', 'source references') beyond what annotations convey.

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 four sentences with a clear structure: purpose, method, output, use cases. Every sentence adds value, though it could be slightly more concise.

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 output schema exists, the description adequately covers the tool's purpose and output ('structured compliance insights with source references'). It does not address edge cases like async behavior, but annotations and schema cover basic behavioral traits.

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 100%, so baseline is 3. The description implies the main input (covenantText) but does not elaborate on other parameters like couponType, issuerSector, or maturityDate.

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 ('assess') and resource ('bond covenants') with a clear ESG compliance focus. It distinguishes from siblings like 'bond_covenant_monitor' by specifying regulatory benchmarks from BIS and ECB.

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 states ideal usage contexts ('pre-issuance due diligence or ongoing monitoring') but does not explicitly exclude other contexts or mention alternatives like 'bond_covenant_monitor' for non-ESG covenant monitoring.

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 read-only, open-world, and idempotent behavior. The description adds that it analyzes real-time data and returns compliance status, metrics, and SEC filing references, consistent 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 plus keywords; all are informative and front-loaded with the core action. Could be slightly shorter but no unnecessary fluff.

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

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

For a tool with nested parameters and output schema, the description adequately covers inputs, outputs, and use-case context. The presence of an output schema reduces the need to detail return values.

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 high (67%), and the description adds meaning by explaining the role of covenant thresholds and the output (compliance status, metrics, SEC references), beyond the schema's parameter names.

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 monitors bond covenant compliance using specific ratios (debt-to-equity, debt-to-EBITDA, interest coverage). It distinguishes itself from sibling tools by focusing on covenant compliance rather than general financial analysis or SEC filing 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?

It explicitly targets CFOs for proactive debt management and regulatory compliance tracking. While it doesn't list when not to use or alternative tools, the context among siblings implies it's the go-to for covenant monitoring.

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 openWorldHint. The description adds context about server-side validation and the audited nature of the deliverable, but does not elaborate on handling of inputs or potential side effects beyond what annotations imply.

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 relatively short but not optimally front-loaded. The first sentence includes branding ('Gapup agent-payable C-suite expertise (CFO)') which is less actionable than the return characteristic. The reference case and validation note are useful but could be more tightly integrated.

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 nested parameters and no output schema, the description is insufficient. It does not describe the return format beyond 'structured, audited deliverable,' nor does it compensate for low schema coverage by explaining key parameter purposes or providing examples.

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 25%, yet the description adds no meaning to parameters beyond a vague reference to 'documented case fields.' It does not explain the required fields (company, raise, keyMetrics) or their structure, leaving the agent reliant on the incomplete 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 that the tool returns a structured, audited business plan narrative, with a reference to Stripe Series A 2012. It identifies the tool as a CFO-level expertise. However, it does not explicitly distinguish from sibling tools like ftg_business_plan, which may also generate business plans.

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 CFO-level business planning but provides no explicit guidance on when to use this tool versus alternatives. There are no examples of when not to use it or mention of alternative tools, leaving the agent to infer the scope.

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 indicate readOnlyHint=true, so the description's claim of returning a deliverable aligns. The description adds minimal behavioral context beyond 'inputs validated server-side' and does not address what happens during processing (e.g., external data access despite openWorldHint). More detail on side effects (none) and execution characteristics would help.

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 short (3 sentences) and uses a mix of French and English. While concise, it sacrifices clarity by not explaining the core function upfront. The structure is front-loaded with the French title but lacks a clear English summary of the tool's purpose.

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 5 parameters, nested objects, no output schema, and high complexity, the description fails to provide sufficient context for an AI agent to use it correctly. No information is given about the deliverable's structure, typical use cases, or how to format the input fields beyond what the schema shows.

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 20%, yet the description omits any explanation of parameters like 'founder', 'brand', 'target', or 'existingAssets'. It merely says 'send the documented case fields' without elaborating on how to structure these complex nested objects. This forces the agent to rely solely on the sparse schema, risking incorrect invocations.

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 tool as 'Architecte de marque' and states it returns a structured, audited deliverable, with a reference case (Pennylane). However, it does not explicitly define what the tool does—e.g., designing a brand identity—leaving the purpose somewhat ambiguous. A more direct statement of the output (e.g., brand identity framework, recommendations) would improve clarity.

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 a reference case but no guidance on when to use this tool versus siblings like 'brand_equity_voice_share_calculator' or 'positioning_strategist'. No context is given for prerequisites, alternatives, or scenarios where this tool is inappropriate.

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 context about data sources and async behavior. Annotations already declare readOnlyHint, openWorldHint, and idempotentHint, which the description does not contradict. The added behavioral info (async usage, data sources) is valuable.

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 four sentences, front-loaded with the core purpose and key inputs. 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?

With output schema present, the description mentions key outputs (voice share percentage, sentiment distribution, top sources) and async behavior. It could be more complete about the time_range format but is adequate for the given 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 20%, so the description should compensate. It lists brand, competitors, and time range but does not detail their schema (e.g., time_range object with start/end). The description adds some meaning but misses nested details.

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

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

The description clearly states the tool calculates brand equity voice share by analyzing mentions across news articles and forums from specific sources (Common Crawl and Wayback Machine). It distinguishes itself from siblings with a 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?

It mentions it's ideal for competitive benchmarking and brand visibility tracking, and hints at using async to avoid timeout. However, it does not explicitly exclude alternative tools or provide 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.

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

Annotations declare readOnlyHint=true and openWorldHint=true, so the description does not need to reiterate safety. The description adds that inputs are validated server-side and mentions the async parameter behavior. However, it lacks details on potential limitations (e.g., rate limits, data freshness) or what exactly the 'audited deliverable' contains.

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 front-loads the purpose effectively but becomes lengthy with a list of example questions. While the examples are helpful, many could be removed or condensed to improve conciseness. The structure is clear but not as tight as it could be.

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 of nested objects and low schema coverage, the description leaves gaps. It does not explain what the output deliverable looks like, how to interpret results, or whether results are cached. The mention of a reference case ('Doctolib Q3 2026') provides some context but is insufficient for complete 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?

With only 25% schema description coverage, the description does not compensate. It does not explain the parameters 'entity' or 'budgetContext' beyond what is in the example questions. The example questions imply fields like company name and period, but the structure of 'budgetContext' with categories, amounts, and variance percentages is not clarified.

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 that the tool analyzes budget variance and returns a structured, audited deliverable aimed at CFO-level expertise. It lists specific example questions that illustrate its purpose, making it distinct from sibling tools like financial model builders or cash flow analyzers.

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 example questions but does not explicitly state when to use this tool over alternatives, nor does it mention when not to use it. There is no guidance on prerequisites or scenarios where the tool might be inappropriate, such as needing real-time data or non-financial inputs.

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 goes far beyond annotations (readOnlyHint=true, idempotentHint=true, destructiveHint=false) by detailing local processing, no external API calls, instant response, criteria weights, diversity signals with ethical warning, red/green flags, and interview questions. It is fully transparent with 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 dense but well-structured, front-loading purpose, then listing criteria, output details, and warnings. Every sentence adds value 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?

Given the tool's complexity (5 parameters, nested objects, output schema exists), the description covers all aspects: input requirements, processing behavior, criteria explanation, output fields (including red flags, green flags, interview questions), diversity signal warning, and privacy guarantees. It is fully complete for an agent's decision-making.

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 schema already documents all 5 parameters clearly. The description adds context about criteria weights and output fields but does not significantly enhance parameter understanding beyond what the schema provides.

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 AI-powered candidate screening and ranking, specifying input (JD + 1-50 resumes) and output (ranked shortlist with score breakdowns across five weighted criteria). It distinguishes from sibling tools like talent_intelligence or recruiting_architect by detailing the exact output and criteria.

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 identifies the target users (recruiters, hiring managers, ATS providers, recruitment AI agents) and mentions local processing and instant response, which suggests when to use it (privacy-sensitive, no external calls). However, it does not explicitly state when not to use or name alternatives, leaving some inference to the agent.

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 openWorldHint. The description adds that inputs are validated server-side and returns an audited deliverable, but does not contradict annotations. However, it does not significantly extend behavioral understanding 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 short but includes a reference case that adds context, though it could be more concise. The structure is reasonable but the French phrase may disrupt clarity. It could be trimmed without losing 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 the complexity (nested objects, no output schema), the description lacks information about the deliverable's structure, how to use the async parameter, and what response to expect. It is insufficient for an agent to fully understand the tool's capabilities and 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?

Schema description coverage is only 14% (only 'async' parameter documented in schema). The description adds no explanations for the 7 parameters, including required ones like company, financials, and currentTeam. With low coverage, the description should compensate but fails to do so.

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 this is a capacity planning tool for CHRO-level expertise, returning a structured deliverable. The reference case provides concrete context. However, it uses French ('Planification capacitaire') which may not be universally understood, and the domain (Gapup agent-payable) is niche, slightly reducing clarity.

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 given on when to use this tool versus the many siblings. There is no mention of prerequisites, exclusions, or alternatives. The description only says 'send the documented case fields' without differentiating this tool from similar planning or HR tools.

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

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

Annotations already indicate readOnlyHint=true and openWorldHint=true. The description adds that inputs are validated server-side and returns a deliverable, but does not detail behavioral aspects like processing time or side effects. 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?

The description is relatively short but includes jargon and French text. It is front-loaded with a title but contains a reference case that may not be essential. Could be more concise for its length.

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 (5 parameters, nested objects, no output schema), the description is incomplete. It does not explain what the deliverable contains, how to interpret results, or any prerequisites for use.

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 20% (one parameter described). The description says 'send the documented case fields' without explaining individual parameters or their meanings. It adds minimal 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 states 'Stratégie de financement' (financing strategy) and mentions returning a structured, audited deliverable, which provides a clear purpose. However, it does not differentiate from sibling tools like cap_table_strategist or funding_hunter, lacking explicit distinction.

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 no guidance on when to use this tool versus alternatives, nor any conditions for usage. It only gives a reference case but no explicit context.

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

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

Annotations already indicate readOnlyHint=true. The description adds that it returns a structured deliverable, but does not clarify async behavior (despite an async parameter) or any other behavioral traits. The mention of server-side validation is useful but insufficient.

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 relatively short but includes a specific reference case that may not be universally helpful. It could be more concise and front-load the core action. The mix of French and English reduces clarity.

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 (nested objects, 6 parameters) and lack of output schema, the description fails to provide complete context. It does not explain what the deliverable contains, how results are retrieved (async vs sync), or how to interpret outputs.

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 17%, yet the description provides no additional meaning for the parameters. It merely says 'send the documented case fields', which does not explain the role of each field. The nested objects are left entirely undocumented.

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 cap table strategist for fundraising, and mentions it returns a structured, audited deliverable. The reference case adds concrete context, but the purpose is somewhat implied rather than explicitly stated as a verb-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 explicit guidance on when to use this tool versus alternatives. The description mentions fundraising context but does not specify when not to use or recommend other tools. Among many sibling tools, no differentiation is provided.

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

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

Annotations already indicate readOnlyHint=true, idempotentHint=true, and destructiveHint=false, making the tool non-mutating and safe. The description adds behavioral context: accuracy ±20%, tier-2, and returns breakdown, hotspot, levers, trajectory, and CSRD readiness. It also mentions that if async=true, it returns a job_id. 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 relatively long but well-structured, front-loading the core function and deliverables, then providing usage guidance and inputs. Every sentence adds value without redundancy. However, it could be slightly more concise by removing the 'Delivered by Émilie' line, which is not essential for tool selection.

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 (8 parameters, nested objects, output schema), the description is remarkably complete. It details all major outputs (breakdown, hotspot, levers, trajectory, scope-3 categories, CSRD readiness) and explains when to use it. The existence of an output schema reduces the burden, but the description still adds value. Only minor gap: no mention of the specific parameters beyond vague 'activity data'.

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 coverage is only 13%, meaning most parameters lack descriptions in the schema. The description adds only 'Inputs: the company profile and its activity data', which is too vague to compensate for the low coverage. While some nested fields have schema descriptions (e.g., async, category), the overall parameter meaning is not sufficiently elaborated.

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 calculates a company's GHG footprint under the GHG Protocol, specifying Scope 1+2+3 with tier-2 accuracy. It lists specific outputs such as emissions breakdown, hotspot identification, reduction levers, trajectory, and CSRD readiness. This distinguishes it from sibling tools like carbon_roadmap or sustainability_report by its focus on detailed footprint calculation for compliance and financing.

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 'When to use this tool: the user needs a carbon assessment for CSRD compliance pre-audit, green-finance access, or supplier ESG scorecards.' It also mentions inputs are company profile and activity data, providing clear when-to-use and context.

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

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

Annotations provide readOnlyHint and openWorldHint, covering safety and external data use. Description adds that inputs are validated server-side and returns a deliverable, but no detail on computation time, cost, or side effects 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 short with three sentences, but mixes French and English which may reduce clarity. Front-loaded with the core purpose. Could be more 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?

Given complexity (8 params, nested objects, no output schema), the description is insufficient. It fails to describe the deliverable's format, how to interpret results, or any usage constraints. No guidance on required fields beyond implicit required list.

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 very low (13%), and the description fails to compensate. It only says 'send the documented case fields' without explaining any of the 8 parameters, their structure, or relationships. No parameter descriptions are provided.

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 states it returns a structured, audited carbon roadmap deliverable for C-suite. Verb and resource are clear, but it doesn't explicitly distinguish from similar sibling tools like 'carbon_footprint_calculator' or 'sustainability_report'. Some differentiation implied by mention of C-suite expertise and audited deliverable.

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?

References a demo case and implies use by C-suite, but no explicit when-to-use, when-not-to-use, or alternatives. No mention of when to prefer this over sibling tools like 'carbon_footprint_calculator'.

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 openWorldHint=true. Description adds that inputs are validated server-side and returns a structured deliverable, which is consistent. However, no details on timing, output content, or side effects (none expected). Adds modest 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?

The description is concise, with no wasted words. It includes a concrete reference case. However, it could be better structured (e.g., bullet points) and front-loads the purpose well.

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 (nested objects, audited deliverable) and no output schema, the description is incomplete. It does not explain what the deliverable contains, how to interpret it, or any behavior for edge cases. The reference case is illustrative but insufficient for general use.

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 25% (only 'async' has description). The tool description does not explain the parameters beyond a vague 'send the documented case fields'. It fails to add meaning for the complex nested parameters, leaving agents to infer from names and constraints.

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 it returns a structured, audited deliverable for champion mapping, but the verb is unclear (implies analysis/generation). The reference case helps but doesn't clearly specify the tool's primary action. It distinguishes from siblings by being about champions, but not precisely.

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 on when to use this tool versus alternatives. The description mentions sending documented case fields but gives no context on prerequisites or when not to use. Sibling tools include other mapping tools, but no differentiation.

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, openWorldHint, and idempotentHint. The description adds behavioral traits by specifying data sources (GitHub PR metadata, Snyk vulnerability data) and output format (structured root cause categories with confidence scores). 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 concise (3 sentences), front-loaded with the main purpose, and structured logically. Every sentence adds value 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?

Given the tool has 5 parameters (1 required) and an output schema, the description covers the core use case but omits details on optional parameters (async, snyk_org_id, time_range_days). This is a gap for comprehensive 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 only 20%, so the description must compensate. It explains the primary inputs 'pr_url' and 'incident_id' but does not clarify 'async', 'snyk_org_id', or 'time_range_days'. Partial coverage, hence a 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 states the tool's verb ('classifies'), resource ('root causes of change failures'), and scope ('CTO-level incident analysis'). It mentions specific data sources and outputs, distinguishing it from siblings.

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 usage context: 'Ideal for post-mortem analysis and change risk assessment'. It also mentions inputs, which helps the agent decide when to use it. However, 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?

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds behavioral details: live data requirement, proxy dependency, async mode (job_id), and data quality notes. 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 front-loaded with purpose and well-structured into overview, mode list, and notes. While somewhat lengthy, every section adds necessary context. Could be slightly more concise, but appropriate given complexity.

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 (five modes, multiple platforms, proxy requirements, output schema exists), the description covers all essential aspects: mode descriptions, input formats, data quality notes, and async behavior. No gaps identified.

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%, but the description adds valuable context beyond schema: explains the five modes in detail, provides input format examples for seller_profile and price_history, and clarifies the async flag. This enriches the schema descriptions.

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 provides Chinese e-commerce intelligence and enumerates five distinct modes (product_search, seller_profile, price_history, brand_monitoring, market_intel), which sets it apart from siblings like 'china_market_data'. The verb+resource is 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 identifies target users (ZH diaspora, import-export teams, brand IP enforcement, etc.) and specifies when to use each mode. It also notes proxy requirements for some platforms. 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?

Beyond annotations (readOnlyHint, openWorldHint), the description details data source dependencies (Yahoo Finance, Eastmoney via proxy) and async behavior (returns job_id when async=true). This adds valuable behavioral context not captured in 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 well-structured with clear sections and bullet points, front-loading the core purpose. It is somewhat lengthy but efficient given the tool's complexity. Every sentence adds value; no 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?

Given the tool's complexity (four modes, multiple input formats, async option, proxy dependency), the description covers all essential aspects. An output schema exists, so return values need not be explained. Slight gaps in usage guidelines limit completeness.

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 adds substantial meaning beyond the schema: it explains each mode's output, acceptable input formats (ticker patterns, company names in ZH/EN, sector keywords), and default behavior (e.g., period_days default 30). Schema coverage is 100%, but the description enriches understanding.

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 provides Chinese capital market intelligence, specifies four modes (company, market_quote, sector_overview, regulatory_filing), and distinguishes it from siblings like india_market_data by geographic focus. The verb 'covers' and resource detail 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 provides context on when to use different modes and input formats, but does not explicitly guide when not to use this tool versus alternatives (e.g., india_market_data, sec_filing_decoder). No exclusion criteria or direct comparisons to other tools 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 indicate readOnlyHint=true, so the tool is read-only. The description adds context about server-side validation and returning a structured deliverable, but does not disclose additional behaviors like authentication requirements or side effects. 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 concise with two sentences and a reference case. It is front-loaded with the tool's purpose. However, it could be more structured to separate purpose from usage notes.

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 of the input schema (nested objects, many fields) and no output schema, the description is incomplete. It does not explain what the deliverable contains or how the tool processes the input, leaving gaps for agent selection.

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 low (20%) and the description does not explain parameter meanings beyond referencing 'documented case fields'. It adds minimal value over the schema, which has many fields without descriptions.

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 'Bouclier anti-churn' and 'Returns a structured, audited deliverable', clearly indicating it is an anti-churn analysis tool. The name 'churn_defender' reinforces this. However, it does not differentiate from sibling tools like 'renewal_optimizer' or 'save_plays' which may also deal with churn.

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 provide guidance on when to use this tool versus alternatives. It mentions 'C-suite expertise (CRO)' but no explicit when/when-not conditions or comparisons with sibling tools.

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

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

The description adds significant behavioral context beyond annotations: async capability, cache duration (7j), SLA (≤20s), and details on multi-scenario comparison, probability, and business impact signals. 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 a single dense paragraph covering all key aspects without redundancy. It could be slightly more structured with bullet points, but it is efficient and 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 (multiple scenarios, metrics, a nested location object), the description is very complete. It explains output types, sources, and use cases, and notes that an output schema exists. The description fully compensates for the lack of separate output schema text.

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 100%, so baseline is 3. The description adds valuable context for parameters like 'async' (returns job_id) and 'metrics' (explains default), going beyond the schema enumerations.

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 provides long-term climate projections by IPCC scenario for any location, listing scenarios, horizons, metrics, and outputs. It distinguishes itself from siblings like 'weather_climate_intel' by its specific scope.

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 lists use cases (TCFD/CSRD physical risk, due diligence, etc.), giving clear context. However, it does not explicitly state when not to use or mention alternative tools, but the specificity makes usage 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 indicate readOnlyHint and openWorldHint. The description mentions it returns a structured, audited deliverable and that inputs are validated server-side, but omits the async behavior parameter documented in the input schema. 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 a solid paragraph with a bulleted list of examples, but contains jargon ('Gapup agent-payable C-suite expertise (RISK)') and could be more concise.

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 8 parameters, no output schema, and missing async behavior explanation, the description is not complete enough for an agent to reliably use the tool. It does not detail the return format beyond 'structured, audited deliverable'.

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 13%, and the description only partially compensates by mentioning parameters like topic, intervention, and target_disease in examples, but does not explicitly describe all parameters, leaving many (e.g., max_studies, date_range_years) 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 it briefs clinical evidence with GRADE ratings, key trials, and safety signals, and provides example queries. However, it does not explicitly differentiate from siblings like clinical_pharma_intel.

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 example questions and a reference case, giving context for use. But it lacks explicit guidance on when not to use or alternatives among siblings.

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, destructiveHint=false, and openWorldHint=true. The description adds significant value by disclosing source public access, optional API key quota increase, SLA of ≤16s p95, and cache durations per mode. It also explains signal detection logic (P0/P1/P2), exceeding annotation 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?

The description is well-structured with bullet points and sections (modes, signal detection, SLA, caching). It is concise despite its length, with every sentence providing meaningful information. No redundant statements.

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 (7 parameters, multiple modes, signal detection, caching, SLA), the description covers all critical aspects. An output schema exists, so the description does not need to detail return values. It is fully complete for an AI agent to understand and invoke the tool 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?

The input schema already provides 100% coverage with detailed descriptions for all 7 parameters. The description adds extra context beyond the schema, such as the signal detection logic and what each mode returns, but does not add new parameter-specific details.

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

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

The description clearly states the tool aggregates clinical and pharmaceutical intelligence across five specific modes (trials, pipeline, approvals, recalls, adverse_events). It uses specific verbs like 'aggregates' and identifies target users (biotech analysts, fund managers, etc.). While there is a sibling 'clinical_evidence_briefer', this description uniquely differentiates its scope and capabilities.

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

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

The description explicitly lists target users and explains each mode's purpose, providing context for when to use each. However, it does not explicitly state when not to use this tool or compare it to alternatives like clinical_evidence_briefer, leaving some ambiguity for agents.

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