mcp-knowledge

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

Annotations already declare readOnlyHint=true and openWorldHint=true, indicating no mutation and flexible input. The description adds that the tool returns an 'audited deliverable' and validates inputs server-side, which aligns with read-only behavior. However, it omits other behavioral details such as rate limits, response format, or error handling.

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 but mixes languages (French/English) and includes a lengthy reference case that, while helpful, adds clutter. The core message is front-loaded but could be more tightly structured without the example.

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 8 parameters, nested objects, and no output schema, the description is incomplete. It does not describe the deliverable structure, possible error conditions, or how to interpret results. The reference case provides partial context but leaves significant gaps for effective 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 13%, with most parameters lacking a description. The description does not explain individual parameters beyond a minimal reference case. The sparse schema and lack of parameter guidance means an agent would struggle to understand field semantics or values.

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

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

The description specifies that the tool is an ABM architect for C-suite (CMO) and returns a structured, audited deliverable. The reference case provides concrete context. However, jargon like 'Gapup agent-payable' obscures clarity, and the purpose is not sharply differentiated from sibling tools like abm_lookalike_account_finder.

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, nor does it state prerequisites or when not to use it. The only instruction is to 'send the documented case fields', which is insufficient for an agent to decide between similar 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 declare readOnlyHint, openWorldHint, and idempotentHint. The description adds valuable behavioral context by specifying the data sources (robots.txt, OpenGraph metadata) and output (ranked list with matching technologies and attributes), going beyond annotation information.

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, no wasted words. Efficiently conveys what the tool does, inputs, and output.

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 an output schema, the description adequately explains the tool's function and inputs. It could mention async behavior or result polling, but that may be covered by other tools. Overall, it is 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 high (75%), and the description provides high-level context (e.g., 'targeted ABM campaigns') but does not add significant detail beyond what the schema already describes for each parameter. 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 the tool's purpose: discovering 50 lookalike B2B accounts based on top 10 customer domains and firmographics. It uses specific verbs ('discover') and resources ('lookalike accounts'), and the unique focus on ABM differentiates 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 implies usage by describing inputs (customer domains, firmographic filters) but does not explicitly state when to use this tool instead of alternatives or when not to use it. No exclusion criteria are provided.

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

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

Annotations already declare readOnlyHint=true, so the read-only nature is clear. The description adds 'returns a structured, audited deliverable' but no additional behavioral details like auth needs 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 relatively short but includes a reference case that may not be essential. It is front-loaded with the title but contains jargon that 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 complex nested schema and no output schema, the description is incomplete. It does not explain the deliverable's format, interpretation, or prerequisites, leaving significant 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 20%. The description does not explain parameters beyond 'send the documented case fields,' failing to compensate for the low schema 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 gives a sense of account expansion mapping for B2B CRO but uses jargon ('Gapup agent-payable C-suite expertise') and a French title without a clear English verb. It doesn't differentiate from sibling tools like upsell_hunter or abm_architect.

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. Only mentions server-side validation but lacks context for selection among similar 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 provide readOnlyHint=true and openWorldHint=true, indicating safe, read-only behavior with variable output. The description adds minimal behavioral context (returns a deliverable, inputs validated server-side) but does not disclose additional traits like rate limits or side effects.

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, includes a concrete example, and 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?

For a complex tool with 8 parameters, nested objects, and no output schema, the description is insufficient. It does not explain expected inputs or outputs beyond a reference case, leaving the agent uncertain about how to invoke it correctly.

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

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

Schema description coverage is only 13%, and the description provides no explanation of parameters beyond the vague phrase 'documented case fields'. It 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 the tool generates an ESG action plan ('Plan d'action ESG') and returns an audited deliverable, with a concrete reference case. However, it does not explicitly differentiate from sibling tools like 'sustainability_report' 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?

No guidance on when to use this tool versus alternatives. The description mentions 'send the documented case fields' but does not specify prerequisites or 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 signal readOnly, openWorld, idempotent. Description adds async requirement and output types (status, warnings, references) but no further behavioral detail. No contradiction with annotations.

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

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

Three sentences, front-loaded with purpose, then async requirement, then outputs. No redundancy, 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?

With output schema implied and good annotations, description covers main purpose, async nuance, and output types. Complex tool with 5 params, but coverage is sufficient.

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

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

Schema covers 100% parameters with descriptions, so baseline 3 is appropriate. Description does not add significant parameter-specific meaning beyond schema.

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

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

Clearly states it evaluates AI model resilience against adversarial inputs following NIST AI RMF protocols. Identifies target persona (security/compliance) and outputs (risk scores, failure modes). Lacks explicit sibling differentiation but specific 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?

Provides one explicit guideline: requires async:true to avoid timeouts. No when-to-use vs. alternatives or exclusions. Adequate but minimal.

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 value by specifying output structure and data source, but does not expand on behavioral nuances like async polling 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 very concise, using four well-structured sentences covering purpose, audience, inputs, outputs, and keywords. No fluff—every sentence is meaningful.

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 an output schema (not shown) and annotations, the description provides a complete high-level overview. It lacks details about the async parameter and polling behavior, but the context signals indicate this is adequate for a tool with moderate complexity.

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

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

Schema description coverage is 75%, so the schema already documents required parameters. The description only repeats the required inputs without adding details about optional parameters like 'async' or 'threshold', offering minimal extra value.

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 affiliate clickstream data from Common Crawl to detect fraud patterns, specifying both inputs and outputs. It distinguishes from siblings by mentioning its specific fraud detection focus and data source.

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 the target audience (CMOs) and purpose (validate traffic quality, prevent budget waste), implying when to use. However, it does not contrast with alternatives like the generic 'fraud_detector' sibling, lacking explicit 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, idempotentHint, openWorldHint, indicating safe, deterministic behavior. The description adds value by specifying data sources and the async parameter to avoid timeouts. No contradictions with annotations; however, it does not discuss error handling or data freshness 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 concise at 4 sentences, front-loaded with the primary purpose. Each sentence introduces distinct information: user role and data sources, inputs, outputs, and a practical tip. No superfluous content; ideal for quick understanding.

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, output schema exists) and the safety annotations, the description covers key aspects: purpose, inputs, outputs, and async usage. It enables an agent to decide to call the tool for NTB analysis. Minor omission: no mention of typical runtime or prerequisites, but not critical.

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

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

With 100% schema description coverage, the tool description adds limited new meaning. It reiterates the role of origin/destination countries and HS codes, and emphasizes the async parameter's use. This provides marginal value beyond the schema, which already includes formats 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 clearly states the tool's purpose: analyzing non-tariff trade barriers across African corridors using WITS and UNCTAD STAT data. It specifies inputs (origin/destination countries, HS codes) and outputs (risk assessment, mitigation strategies). This distinguishes it from sibling tools like tariff_arbitrage_finder or trade_finance_eligibility, though explicit sibling contrast is missing.

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 the target user (COO) and provides a practical async usage tip. However, it does not explicitly state when to avoid this tool or compare it to similar siblings (e.g., tariff_impact_simulator). More definitive guidance on tool selection would improve clarity.

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?

Description aligns with annotations (readOnlyHint, idempotentHint). Adds context about data sources and timeout handling via async parameter, which aids agent understanding.

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 plus a short instruction. Front-loaded with purpose and data sources. No fluff.

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

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

Covers main use case, data sources, async option. Output schema exists. Could mention optional parameter effects but sufficient for an AI 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?

Schema coverage is 100%, but description adds value by explaining async in context of timeout and providing example values for institutionName and countryCode.

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

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

Clearly states the tool evaluates ESG compliance of African trade finance providers using specific data sources (World Bank WITS, CDP). Distinguishes from siblings by specifying African trade finance focus.

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

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

Explicitly states ideal usage for due diligence on trade partners or portfolio risk assessment. Mentions async option to avoid timeout. Does not explicitly 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?

The description adds value beyond annotations by revealing the async requirement and timeout risk, which are important behavioral traits. It also mentions data sources (WITS, OECD). Annotations already declare readOnly/openWorld/idempotent, so the burden is lower. 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 with a clear purpose statement followed by technical notes. It is front-loaded and every sentence contributes. Slightly verbose in listing data sources but overall 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 complexity (6 parameters, output schema exists), the description covers purpose, data sources, output type, and a critical async requirement. It could mention country eligibility or limitations, but is largely complete for the agent to understand 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 100%, so the schema already describes all parameters. The description adds only a usage hint about async, which is not a parameter semantic. It does not provide additional meaning beyond what the schema offers.

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 AGOA/EBA trade preference arbitrage opportunities, specifies target audience (COOs), data sources (WITS, OECD), and output (structured analysis). It is a specific verb+resource with scope, and distinguishes from siblings by focusing on arbitrage opportunities.

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 technical requirement (async:true to avoid timeout) but does not explain when to use this tool versus sibling tools like agoa_eba_intelligence or africa_trade_preference_optimizer. No guidance on 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 description adds context beyond annotations by specifying data sources (UN Comtrade, WCO, African Union rules) and output types (duty savings estimates, optimal trade routes, recommendations). It does not contradict the readOnlyHint, openWorldHint, or idempotentHint 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 (about 80 words) and front-loaded with the main verb. It includes keywords and ideal use cases but is a single paragraph; a more structured format could improve scannability.

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

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

The description covers the tool's purpose, data sources, and output types. With a full output schema and good annotations, the description is adequate. No major gaps are present.

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 good schema descriptions. The description adds context that the tool expects originCountry, destinationCountry, and hsCode but does not significantly enhance understanding beyond the schema. 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 the tool's purpose: analyze AGOA/EBA duty savings opportunities with HS code-level optimization. It distinguishes itself from siblings like africa_trade_barrier_breaker and agoa_eba_intelligence by focusing on duty savings and trade route optimization.

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 (export market evaluation, supply chain optimization) but does not explicitly state when not to use it or compare directly with sibling tools. No exclusion criteria or alternative recommendations 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?

The description adds behavioral context beyond annotations by detailing data sources (AGOA.info, EU EBA, etc.) and the scope of countries (39 AGOA, 47 LDCs). It aligns with readOnlyHint=true, indicating no destructive actions. However, it does not mention the async behavior or that results might be delayed for some modes.

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, front-loaded with the tool's main purpose, and every sentence contributes essential information. No wasted words or redundancy.

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

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

Given the tool's complexity (four modes, multiple countries, two programs), the description covers the main functionality, data sources, and regional scope. It lacks details on output format or error handling, but is still fairly complete for a query tool with good annotations.

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

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

Schema coverage is 100%, and the description provides a cohesive narrative tying the modes to the tool's functions. However, the schema already includes detailed parameter descriptions and examples (e.g., HS codes), so the description adds limited new semantic value beyond restating the modes.

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: checking eligibility of African countries and products for preferential tariff programs (AGOA, EBA/GSP), identifying export opportunities, and providing compliance rules. It uses specific verbs and resources, and differentiates from sibling tools by covering both US and EU programs and including compliance details.

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 by listing the four modes, but it does not explicitly state when to use this tool versus alternatives like africa_trade_preference_arbitrage. It lacks when-not-to-use guidance and comparisons with 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 indicate readOnlyHint, openWorldHint, and idempotentHint. The description adds value by noting the async requirement and describing outputs (structured playbook steps, notification drafts, compliance checklists), which supplements 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 two sentences with an additional keyword tag. It is front-loaded with the main purpose and efficient, containing no redundant information.

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

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

Given the tool has 6 parameters and an output schema (not shown), the description adequately covers purpose, usage context, and key requirements. However, it could better complement the schema by explaining all parameters and their roles.

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%, so the description should compensate. It mentions 'incident severity, AI system type, and affected stakeholders' as inputs, but omits incident_type and incident_description. This adds partial context but is incomplete for the 6 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 generates EU AI Act incident response playbooks with regulator notification templates for risk management teams. Verb ('Generates') and resource ('playbooks') are specific, and the scope is well-defined, distinguishing it from siblings like 'ai_act_sandbox_regulatory_sandbox' and 'ai_act_training_data_audit'.

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 specifies that the tool is 'Essential for high-risk AI system breaches requiring formal EU notification' and includes a critical usage note about passing 'async:true' to avoid timeouts. While it doesn't explicitly mention when not to use or list alternatives, the context is strong enough for correct invocation.

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 context about returning structured feedback with citations and recommendations, which goes beyond the annotations. 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 a single paragraph of five sentences, front-loading the purpose and then listing inputs and outputs. It is concise and well-structured, though could be slightly tighter.

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 an output schema, the description covers the main purpose and outputs adequately. It does not explain the async parameter or output schema details, but the schema and annotations fill those gaps. 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 coverage is 100%, so the description adds no additional meaning beyond the schema. The description mentions accepting system descriptions and use cases, but this is already clear from the schema. Baseline 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, specifies it is legal-focused, and distinguishes it from sibling tools like ai_act_incident_response and ai_act_training_data_audit by focusing on sandbox simulation.

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

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

The description implies use for sandbox submissions, but does not explicitly state when not to use it or mention alternatives. However, the context is clear and the purpose is well-defined, earning a 4.

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

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

Annotations declare readOnlyHint, openWorldHint, and idempotentHint. The description adds behavioral context by mentioning the async option to avoid timeout, implying the tool can be slow. It does not contradict annotations and provides useful operational guidance.

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

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

The description is one paragraph, concisely covering purpose, inputs, outputs, use case, and async note. It is front-loaded with the main action and uses minimal words effectively.

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 (4 parameters, async support, output schema), the description covers all essential aspects: what it does, inputs, outputs, ideal use case, and async behavior. The output schema likely provides return values, so description is 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 50%. The description names dataset_id and risk_threshold, and mentions async in the last sentence, but omits include_bias_metrics. It adds meaning by explaining dataset_id as 'Hugging Face ID or URL' and risk_threshold as optional, but the coverage gap prevents a higher score.

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

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

The description explicitly states the tool audits AI training datasets for EU AI Act compliance with bias detection and regulatory risk assessment. It specifies inputs and outputs, and the 'Ideal for pre-deployment risk evaluation' distinguishes it from siblings like ai_act_incident_response. The verb 'audit' and resource 'training data' are clear and 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 says 'Ideal for pre-deployment risk evaluation' and advises passing async:true to avoid timeout, giving context on when to use. It does not explicitly state when not to use or list alternatives, but the sibling set implies this is for proactive audit, not incidents.

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?

Discloses key behaviors: returns job_id in <300ms, caches 7 days, async with polling or webhook, includes disclaimer. However, annotations state readOnlyHint=true while description implies a write operation (creating a job), creating a minor inconsistency.

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?

Well-structured and concise: front-loaded purpose, enumerated outputs, timing, cache, and alternative. 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?

Fully explains tool's purpose, output, async nature, caching, and integration options. Given complexity (7 params, output schema exists), description covers all necessary context for 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 coverage is 100% with param descriptions. Description does not add significant detail beyond schema, but the baseline of 3 applies as schema handles param semantics adequately.

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 clearly states the tool audits EU AI Act, classifies risk tiers, and produces 7 specific outputs. It distinguishes itself from siblings like ai_governance_full_report_result and ai_governance_pilot by being an async submit that returns a job_id.

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

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

Explicitly advises when to use (for full EU AI Act compliance) and provides alternatives: polling with ai_governance_full_report_result or using webhooks via webhooks_manage. Mentions faster+lighter webhook approach.

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

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

Annotations declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, which are consistent. The description adds behavioral details: returns status=pending while running, status=completion with full report, status=failed on error, status=not_found for unknown/expired job_id, TTL 24h. 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 with high information density. Front-loaded with purpose. Every part adds value: purpose, status outcomes, timing hint, and parameter context. 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?

For an async polling tool, the description covers all necessary aspects: what the tool does, when to call, possible return statuses, error conditions, TTL, and outputs (report contents). Even though an output schema exists, the description enumerates key fields. Highly 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 a single required parameter job_id described as 'The job_id returned by ai_governance_full_report_async (prefix: aigfr_)'. The description doesn't add new parameter semantics beyond the schema, but given high coverage, 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 its purpose: 'Poll the result of an ai_governance_full_report_async job.' It distinguishes itself from siblings by being the polling counterpart to the async variant. The verb 'poll' and the specific resource are unambiguous.

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

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

Explicitly instructs to call this after the eta_seconds hint from ai_governance_full_report_async (~90s). Describes the various status returns (pending, completed, failed, not_found) and the TTL. This provides clear when-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 already indicate readOnlyHint=true and openWorldHint=true. The description adds context about returning a structured, audited deliverable and gives a reference case, which is helpful but does not detail side effects, authorization needs, or rate limits. 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?

The description is brief (two sentences) but contains a typo ('Gapup') and uses technical jargon without clarification. While concise, it sacrifices clarity and could be better structured with a clear action verb and resource scope.

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 AI governance with nested input objects and no output schema, the description falls short. It does not specify what the deliverable contains, how to interpret the output, or how this pilot differs from the full report sibling. The reference case helps but is insufficient.

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

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

Schema coverage is low (20% - only 'async' has a description). The description does not explain the purpose or format of the key nested parameters like 'company', 'aiUseCases', or 'targetFrameworks', leaving the agent to infer from the schema alone.

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 'Pilotage de gouvernance IA' and returns a structured audited deliverable, with a concrete reference case. However, it could better distinguish from the sibling 'ai_governance_full_report_async' which likely performs a similar but more comprehensive 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 mentions inputs are validated server-side but provides no guidance on when to use this tool versus alternatives like the full governance report or other governance-focused tools in the sibling list. There is no when-to-use or when-not-to-use advice.

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 mentions server-side input validation and a reference case, adding slight behavioral context. Annotations already provide readOnlyHint=true, so the description's added value is limited and 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 but uses jargon ('Gapup agent-payable C-suite expertise (COO)') that may confuse the agent. It is front-loaded but could be more straightforward.

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 parameters and lack of an output schema, the description is incomplete. It does not explain what constitutes an 'at-risk' signal or describe the deliverable's format, leaving the agent underinformed.

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 (20%), and the description does not elaborate on any parameters beyond saying 'send the documented case fields.' This fails to compensate for the schema's lack of clarity.

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

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

The description states it returns a structured, audited deliverable for detecting at-risk employees, as shown by the Buffer Inc reference. While the purpose is clear, it does not distinguish itself from sibling tools like 'churn_defender' or 'talent_poaching_risk'.

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

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

There is no guidance on when to use this tool versus alternatives. No explicit when-to-use or when-not-to-use conditions are provided, leaving the agent without decision criteria.

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

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds value by stating cache duration (24h), no API key required, and the fact that most awards are confidential (public only). This goes beyond annotations to set proper expectations. 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 well-structured with sections for overview, modes, notes, and cache. It is front-loaded with the core purpose. While slightly long, each sentence adds value (no fluff). It 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 complexity of three modes, the presence of an output schema (so return format not needed), and the annotations covering safety/idempotency, the description is complete. It covers limitations (confidential awards), cache behavior, modes, and parameters. No gaps remain.

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% (all parameters have descriptions). The description enriches parameter understanding by providing examples for `query` (e.g., 'Yukos', 'Russia') and clarifying the purpose of each mode. It also briefly explains the `async` parameter. This adds meaning beyond the schema definitions.

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 provides commercial arbitration intelligence with three distinct modes (party_lookup, institution_index, clause_check). It specifies the resource (arbitration awards) and the action (lookup/search/audit). It distinguishes from sibling tools by being a specialized arbitration tool, with no direct alternative among the listed 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 explicitly describes each mode's use case (e.g., 'party_lookup — find awards by party name', 'clause_check — audit an arbitration clause for missing elements'). It also notes the limitation to public awards, providing context for when the tool is appropriate. While it doesn't explicitly state when not to use it, the guidance is clear and sufficient.

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 value by stating it returns a 'structured, audited deliverable' and that inputs are validated server-side. This context goes beyond the annotations without contradicting them.

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, mixing general purpose with multiple example questions. It lacks clear structure or prioritization of key information. Though front-loaded with the title, the text is a dense block that 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?

Given 6 parameters, low schema coverage, and no output schema, the description should be more complete. It covers the 'what' but omits details on parameters, return format, and limitations. For a complex tool, this is insufficient.

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

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

Schema description coverage is only 17%, so the description must compensate. It mentions domain and include_email_surface implicitly via examples, but does not explain focus, exclusions, scope_cidrs, or async. No parameter details or usage hints 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?

The description clearly identifies the tool as an attack surface monitor that returns a structured, audited deliverable. Example questions illustrate its functionality well. However, it does not explicitly differentiate it from sibling tools like cyber_risk_auditor or cve_security_lookup, which may overlap in purpose.

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

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

The description provides example queries that imply usage for domain attack surface assessment, email phishing risk, M&A due diligence, etc. However, it lacks explicit guidance on when to use this tool versus alternatives, and does not state when not to use it.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=true, indicating safe, read-only behavior. The description adds that inputs are validated server-side and returns a deliverable, which is consistent but does not significantly expand on behavioral traits beyond what annotations and schema (async handling) 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 (3 sentences) but includes a specific reference case that may be irrelevant for general usage. It lacks structured formatting (e.g., bullet points) and contains jargon (French terms, 'Gapup agent-payable'), which reduces clarity. Not overly long, but not highly concise or 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 (nested parameters, no output schema, low schema coverage), the description is insufficient. It does not specify the return format, readiness scoring, or how to interpret findings. Users cannot determine what the deliverable contains or how to handle results, leaving significant gaps for effective 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 description coverage is low (17%), with only the 'async' parameter described. The tool description does not explain any parameters, merely stating to send 'documented case fields.' This leaves most parameters (nested objects like company, audit, financials) without semantic clarification, failing 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 states the tool performs a pre-audit (Pré-audit comptable) and returns a structured deliverable, but the phrasing mixes French and jargon without explicitly stating the output format (e.g., readiness score, findings). The reference case provides an example but lacks generalization, making it moderately clear but not fully transparent.

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

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

No guidance is provided on when to use this tool versus alternatives. There is no mention of appropriate use cases, prerequisites, or exclusions. The sibling list contains many tools but no differentiation is made.

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 context about data sources (World Bank, ECB SDW) and output (levers, benchmarks), enriching behavioral understanding without contradicting 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: first explains purpose and input, second describes output and ideal use, third provides keywords. Front-loaded and concise with 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 existence of an output schema, the description sufficiently covers purpose, input, and output nature (benchmark comparisons and levers). It does not need to detail return values but could mention the output schema existence.

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 descriptions for all parameters. The description merely lists fee types already covered in the schema, adding no new meaning 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 it analyzes bank fee structures and provides negotiation recommendations using benchmarks. It is specific and distinct from sibling tools like treasury_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 says to input current fees and bank details for negotiation recommendations, and mentions it's ideal for optimizing treasury operations, but does not explicitly state when not to use it or provide alternative 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 mark readOnlyHint=true and openWorldHint=true. The description adds server-side input validation and mentions an audited deliverable, providing slight incremental context 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 short but mixes languages and is not front-loaded with the core action. It includes a reference case that is not essential for understanding.

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 nested parameters, low schema coverage, and no output schema, the description lacks details on return format and parameter constraints. The tool's complexity is not addressed.

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%, and the description does not explain parameters. It vaguely says 'send the documented case fields' but does not map to the parameter names or nested structure.

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 indicates the tool returns a structured deliverable for competitive battle cards, but the verb+resource is unclear due to jargon ('Fiche de combat live') and mixing French/English. It doesn't distinctly separate from sibling 'battle_plan'.

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 like 'battle_plan' or 'competitive_deep_dive'. The reference case is an example, not a usage rule.

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 and openWorldHint=true, so the tool is likely a read-only computation. The description adds that inputs are validated server-side and returns a deliverable, but does not detail authentication, rate limits, or whether the deliverable is cached. 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 a verbose reference case that may not be necessary for general use. It front-loads the purpose, but the case adds clutter without enhancing 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?

With 11 parameters, no output schema, and no details on the deliverable's structure, the description is incomplete. The reference case provides a concrete example but does not generalize well. Annotations only partially compensate.

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 described. The description does not elaborate on the 11 parameters or their expected formats, leaving agents to infer from the reference case. With low coverage, the description fails 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 states it returns a 'structured, audited deliverable' for a marketing battle plan, citing a specific reference case. While the verb is implied ('creates' or 'generates'), it lacks explicit differentiation from sibling tools like 'growth_path_architect' or 'market_entry_strategist'.

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 that inputs must be validated server-side and to send the documented case fields, implying a structured input scenario. However, it does not specify when to use this tool versus alternatives, nor does it provide context for exclusion criteria.

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

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint, so the description's job is lighter. It adds context about HuggingFace leaderboard and fairness metrics but does not disclose additional behaviors beyond those annotations, such as rate limits or authentication needs.

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 consists of two concise sentences, front-loading the core function and audience without unnecessary words. Every sentence adds value.

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

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

Given the tool has an output schema, no required parameters, and moderate complexity, the description covers the key aspects: data source (HuggingFace), audience, inputs, and outputs. It could mention default behavior when no parameters are provided or data freshness, but overall it is sufficiently 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 4 parameters described. The description reinforces the purpose (model identifiers, output samples) but does not add significant semantic depth beyond the schema's existing parameter 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 tracks bias amplification in LLM outputs using HuggingFace's model leaderboard, specifying the resource (LLM outputs) and verb (tracks, analyzes). It distinguishes from siblings like hallucination_confidence_meter by focusing on fairness metrics and demographic bias.

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 it is designed for risk assessment personas to detect bias amplification, implying usage context. However, it does not explicitly state when to use this tool versus alternatives or when not to use it, lacking exclusions or direct alternative references.

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 as true. The description is consistent and adds context about the analytical nature and output format ('structured compliance insights with source references'). 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 (4 sentences), front-loaded with the main purpose, and efficiently communicates key tasks and value. Every sentence adds value; no redundant information.

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

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

Given the presence of an output schema (not shown but indicated), the description adequately covers the return format. It specifies the type of analysis and regulatory benchmarks. For a tool with 6 parameters, the description is reasonably complete, though it could mention the specific parameters or sectors more explicitly.

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 parameters are already well-documented. The description generically mentions 'bond covenant details' without adding meaningful detail beyond what the schema provides. It does not clarify parameter usage 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 purpose: assessing bond covenant ESG compliance against BIS and ECB standards. It uses specific verbs and resources ('analyzes covenant text', 'identifies ESG-related risks') and distinguishes itself from siblings like bond_covenant_monitor by its regulatory focus.

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

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

The description provides clear usage context: 'pre-issuance due diligence or ongoing monitoring'. It implicitly advises when to use (for ESG compliance) but does not explicitly mention when not to use or list alternative tools. A slight gap in exclusion 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 already indicate read-only, open-world, and idempotent behavior. The description adds transparency by noting it uses real-time data and returns compliance status, metrics, and SEC filing references. No contradictions or missing behavioral warnings.

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 plus keywords, which is slightly verbose. It front-loads the CFO role and key action, but includes some redundant or keyword-style content that could be trimmed. Adequate but not highly 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 an output schema exists (implied by context signals), the description adequately covers inputs (ticker, thresholds) and outputs (compliance status, metrics, SEC refs). It also provides use case context. A slightly more complete description of behavioral nuances would improve it.

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

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

Schema coverage is 67%, including descriptions for 'async' and 'ticker'. The description adds value by clarifying 'optional covenant thresholds' for 'maxDebtToEquity' and 'minInterestCoverage', which are not fully explained in the schema. This helps agents understand parameter purpose.

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 by analyzing specific ratios (debt-to-equity, debt-to-EBITDA, interest coverage) and outputs compliance status, metrics, and SEC references. This distinguishes it from siblings like 'bond_covenant_esg_compliance_checker' and 'syndicated_loan_covenant_breach_alert'.

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 it's 'ideal for proactive debt management and regulatory compliance tracking,' implying usage context. However, it does not explicitly state when to use this tool over siblings or when not to use it, leaving the agent without clear decision 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 already provide readOnlyHint and openWorldHint. The description adds that it returns a structured, audited deliverable and that inputs are validated server-side, which provides some additional context 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 two sentences and a reference case. Every sentence adds information, though the reference case could be considered secondary. Front-loaded with 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 complexity (nested objects, no output schema, 4 parameters), the description is too minimal. It lacks details on return format, execution time, and how to use the async parameter. Despite a large sibling list, it offers no help in tool 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 description coverage is only 25%, and the description adds no parameter-specific meaning. It does not explain the required fields (company, raise, keyMetrics) beyond the schema 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 clearly states the tool produces a narrative business plan with CFO-level expertise, naming the verb 'Returns' and referencing a specific case (Stripe Series A 2012). However, it does not differentiate from sibling tools like ftg_business_plan or financial_model_3statement.

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 only says to 'send the documented case fields' but does not specify when not to use it or point to other 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 value beyond annotations by stating it returns a structured, audited deliverable and that inputs are validated server-side. Annotations already declare readOnlyHint=true and openWorldHint=true, so the behavioral profile is partially covered. However, it does not mention async behavior or response format.

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 (3 sentences) but lacks structure. The first sentence conveys the purpose, but the second sentence (case reference) is less actionable. Could be more front-loaded with core behavior.

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 (5 parameters with nested objects, no output schema), the description is insufficient. It does not explain the deliverable structure, processing time, or error handling. The async parameter is mentioned in schema but not in description, which is a gap.

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

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

Schema coverage is only 20% (only async has a description). The description does not explain the purpose or constraints of the required objects (brand, founder, target). Users get no guidance on how to structure these inputs 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 it is a brand architect tool that returns a structured, audited deliverable. It references a case (Pennylane) to illustrate its purpose. While not extremely detailed, it distinguishes itself from siblings by focusing on brand architecture for startups.

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 some guidance: 'send the documented case fields' and mentions server-side validation. However, it does not explicitly state when to use this tool versus alternatives like positioning_strategist or content_audience_profile. No exclusion criteria.

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

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

Beyond annotations (readOnly, openWorld, idempotent), it describes data sources (500K+ articles/forums) and async behavior. This adds meaningful behavioral context. 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?

Four sentences, front-loaded with the main purpose. Efficiently conveys key information without unnecessary words.

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

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

Describes outputs and main inputs but omits include_forums parameter. Output schema exists so return values are covered elsewhere, but the description could be more complete regarding all parameters.

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

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

Schema description coverage is low (20% - only async described). Description lists brand, competitors, time range but omits include_forums. Does not fully compensate for 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?

Clearly states the action ('calculates brand equity voice share'), the target audience (CMOs), and the data sources (Common Crawl, Wayback Machine). It distinguishes itself from siblings by focusing on voice share analysis, a specific metric.

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

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

Provides context for use ('competitive benchmarking and brand visibility tracking') and mentions async for timeout avoidance. Does not explicitly state when not to use, but the description implies the appropriate scenarios.

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

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

Annotations indicate readOnlyHint=true and openWorldHint=true, which are consistent with a read-only analysis tool. The description adds that it returns a 'structured, audited deliverable' and validates inputs server-side, but does not mention async behavior (despite an async parameter in the schema) or other operational details like 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 paragraph that includes a list of example questions. It front-loads the core purpose, but the inclusion of multiple examples makes it longer than necessary. It is adequately structured but 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?

The tool has nested objects and 4 parameters, but the description does not explain the return format beyond 'structured, audited deliverable', nor does it clarify the async functionality or the required fields. Given the absence of an output schema, the description should provide more detail about what the agent can expect as a result.

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 the async parameter has a description). The description mentions placeholders like <company> and <period> in example questions, which hint at entity fields, but it does not formally explain the structure or required fields of entity and budgetContext. This leaves the agent with insufficient guidance for constructing valid inputs.

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 'Analyse d'écart budgétaire' (budget variance analysis) and provides specific example questions that illustrate its purpose. However, it does not explicitly differentiate itself from sibling tools like margin_doctor_finance or financial_model_3statement, which may have overlapping 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 provides several example queries that imply when to use the tool (e.g., explaining drivers, revising forecasts, producing board memos). However, it offers no explicit guidance on when not to use it or which alternative tools might be better suited for other scenarios.

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

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

With no annotations, the description carries the full burden. It discloses key behavioral traits: 'Deterministic database lookup — no LLM, no generation, sub-second.' This reassures the agent about predictability and speed. It does not explicitly state read-only behavior, but 'database lookup' implies it. Additional details like coverage counts further clarify the nature of the operation.

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-loaded with the primary verb and resource. It packs essential info (data types, coverage, performance) without unnecessary words. Every element 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 (multi-country, multiple data types, legal references), the description is complete enough to set expectations. It covers scope, coverage, and performance. It does not detail edge cases or coverage limitations, but an output schema exists, and the schema itself documents async behavior, so the description is sufficient for initial 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?

The schema has 100% param coverage, so the description need not repeat parameter details. It does add context that the location parameters relate to buildings and parking, and mentions units (m²) which align with minAreaM2. However, this does not go beyond what the schema already communicates, so it earns the baseline score.

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: 'Enrich a location with European building intelligence' and lists specific data types (roof surfaces, parking areas, solar-obligation status, existing solar installations). It also differentiates from siblings by specifying the geographic coverage and deterministic 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?

The description implies when to use the tool: when needing building-related data for a European location. It provides context about the database coverage and speed ('sub-second'), which helps the agent judge suitability. However, it does not explicitly mention alternatives or exclusion conditions, so it falls short of full 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 destructiveHint=false, indicating safe read operation. Description adds valuable behavioral context: local processing, no external API calls, instant response, privacy-preserving. Also details output structure and ethical warning about diversity 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?

Description is comprehensive yet concise, front-loaded with the main purpose and target audience. Every sentence adds value: listing criteria, output fields, diversity warning, and processing characteristics. No redundant or 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 complexity of the tool (5 parameters, nested objects, output schema exists), the description is fully complete. It explains input requirements (job description, candidates), output structure (overall score, matched/missing skills, red/green flags, interview questions, fit_summary), and important ethical caveats (diversity signal proxy warnings). The existence of output schema helps but description adds necessary 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 100% with descriptions for all parameters, providing baseline of 3. Description adds significant meaning beyond schema by explaining the five weighted criteria (skills_match, experience_match, etc.) and their definitions, which are not detailed in the schema. Also describes per-candidate output fields like red_flags, green_flags, and fit_summary.

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

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

Clearly states it is an AI-powered candidate screening and ranking tool for recruiters, hiring managers, ATS providers, and recruitment AI agents. Specifies exactly what it does: ingests job description and resumes, returns ranked shortlist with score breakdowns across five criteria. Differentiates from siblings by targeting recruitment use case, though not explicitly naming alternatives.

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?

Implied usage through description of functionality and emphasis on local processing, instant response, and privacy-preservation, suggesting it's suitable for sensitive data scenarios. However, no explicit guidance on when to use versus alternatives, nor when not to use this tool.

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 description adds moderate value by noting inputs are validated and output is a deliverable. However, it does not elaborate on validation behavior or response 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 brief (3 sentences) and includes a reference case. It is front-loaded with the tool's purpose. However, it could be more structured with explicit sections.

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, nested objects, no output schema), the description is insufficient. It does not explain return values, async behavior, or how to interpret the 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 14%, and the description fails to explain any parameters beyond vague references to 'case fields'. The async parameter is not mentioned. The description does not compensate for the low schema 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 capacity planning for HR/CHRO, returning a structured audited deliverable. The reference case provides concrete context. Among many sibling tools, this stands out as a specific planning tool.

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

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

No explicit guidance on when to use or alternatives. The description only mentions inputs are validated server-side, but fails to instruct the agent on when to invoke this tool versus other planning 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 the description adds that inputs are validated server-side and that the deliverable is 'audited'. It does not mention the async parameter or timing behavior. The description adds moderate context beyond the annotations but does not fully disclose behavioral traits.

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

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

The description is short (three sentences) and front-loaded with the tool's purpose. However, the mix of French and English and the vague reference to 'documented case fields' could be more 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 complexity (5 top-level parameters, deep nesting, no output schema, many siblings), the description is insufficient. It omits details about the deliverable format, how to use the async parameter, and how this tool differs from similar finance strategy 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?

Schema description coverage is very low (20%), with only the async parameter having a description. The overall description says 'send the documented case fields' but does not explain the extensive nested objects or required constraints, leaving parameters mostly 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 identifies the tool as a financing strategy ('Stratégie de financement') and states it returns a structured, audited deliverable, with a reference case for context. However, the verb is implicit ('returns') and the exact output (e.g., a report, recommendations) is ambiguous, making it less precise than ideal.

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 its many siblings (e.g., cap_table_strategist, term_sheet_negotiation, funding_hunter). The description mentions 'C-suite expertise' but does not clarify when an agent should select this tool over others.

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, so the description's mention of returning a structured deliverable adds moderate value. However, it does not disclose other behavioral aspects like authentication needs, rate limits, or what happens with complex inputs.

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 moderately concise with four sentences, including a reference case that adds context without excessive verbosity. It is front-loaded with the main purpose but could be slightly shorter without the case study.

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, nested objects, no output schema), the description is insufficient. It does not explain the deliverable's structure, how to interpret results, or provide usage examples beyond the reference case. Critical details for effective use are missing.

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 description coverage is very low (17%), and the description adds no individual parameter explanations beyond 'send the documented case fields.' It fails to compensate for the lack of schema documentation, leaving parameters like 'focus' and nested objects underspecified.

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 C-suite fundraising expertise. It mentions returning a structured, audited deliverable and provides a reference case. However, it does not explicitly distinguish it from sibling tools like 'capital_strategy', and the French language may limit accessibility for some agents.

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

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

The description implies use for fundraising cap table strategies and mentions server-side input validation, but it lacks explicit guidance on when to use versus alternatives or when not to use it. No exclusions or alternative tool references 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, idempotentHint, destructiveHint=false. The description adds behavioral details: output includes emissions breakdown, hotspot identification, reduction levers, SBTi trajectory, Scope-3 categories, and CSRD readiness. No contradictions with annotations.

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

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

The description is concise (a few sentences) with front-loaded core purpose. Every sentence adds value: purpose, scope, outputs, use cases, inputs, and persona. 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?

Given complexity (3 scopes, many outputs, many input parameters) and existence of output schema, the description covers outputs well but inputs vaguely. With low schema coverage, it's incomplete regarding parameter details.

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 13%, meaning most parameters lack descriptions. The description only vaguely mentions 'company profile and its activity data' without detailing individual parameters. With such 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 the verb 'calculate' and the resource 'greenhouse-gas footprint' with specifics: GHG Protocol, Scopes 1-3, unit tCO2eq, tier-2 accuracy ±20%. It distinguishes from siblings like carbon_roadmap by focusing on comprehensive assessment.

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

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

Provides explicit when-to-use scenarios: CSRD compliance pre-audit, green-finance access, supplier ESG scorecards. Does not explicitly state when not to use or mention alternative tools, but the scenarios are clear enough.

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

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

Annotations already provide readOnlyHint=true (no state change) and openWorldHint=true (uses external data). The description adds that it returns a 'structured, audited deliverable,' which gives some idea of output nature 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 short (3 sentences) and front-loaded with the purpose. However, it includes branding language ('Gapup agent-payable C-suite expertise') that adds little value, reducing conciseness slightly.

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, no output schema), the description is insufficient. It does not explain return value format, how to interpret the deliverable, or usage of most parameters. Significant gaps remain.

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%, yet the description adds no parameter-level details. Only the 'async' parameter has a meaningful description in the schema; other parameters lack explanation. The description does not compensate for this gap.

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

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

The description states it 'returns a structured, audited deliverable' for a 'Roadmap carbone,' clearly indicating it generates a carbon roadmap. However, it does not differentiate from similar tools like carbon_footprint_calculator, leaving some ambiguity.

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 or when not to use it. Only mentions server-side validation and sending documented case fields, which is more about input preparation than usage 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?

The annotations (readOnlyHint: true, openWorldHint: true) already indicate no mutations and flexible inputs. The description adds 'inputs are validated server-side' and confirms a deliverable is returned, but does not detail any potential side effects, authorization requirements, or return format beyond 'structured, audited deliverable.'

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 substantive sentences and a reference case, which is concise. However, the specific example may not be universally helpful and could be shortened. The purpose is front-loaded effectively.

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

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

The tool has 4 parameters including complex nested objects and no output schema. The description does not clarify the return structure (e.g., champion name, plan timeline), how to handle the `async` parameter, or what constitutes a successful result. The vagueness of 'structured, audited deliverable' leaves 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 schema description coverage at 25%, the description should clarify parameter usage but only says 'send the documented case fields' and mentions validation. It does not explain the `async` parameter (which has a schema description) or the nested fields in `deal`, `knownContacts`, and `sellerContext`. The reference case is illustrative but does not map to 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's purpose: mapping champions for CRO-level deals, returning a structured deliverable. It includes a specific reference case (Spendesk × Decathlon) which adds concreteness and helps distinguish 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 implies usage for upmarket CRO deals with the phrase 'Gapup agent-payable C-suite expertise (CRO)', but it does not explicitly state when to use this tool versus alternative sales tools like competitive_deep_dive or deal_coach. The context of champion mapping is implicit rather than prescriptive.

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 behavioral context beyond the annotations by specifying that the tool uses GitHub PR and Snyk data to produce confidence scores. However, it does not disclose potential limitations like model update frequency (given openWorldHint), authentication requirements, or performance characteristics. The annotations already indicate the tool is read-only, idempotent, and open-world, so the bar is lower, but the description could still elaborate on these aspects.

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

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

The description is concise at four sentences, with the main purpose front-loaded. It efficiently covers the tool's function, data sources, inputs, outputs, and ideal use case without unnecessary detail. Minor improvement could be to structure information more obviously (e.g., separate inputs and outputs), but it is already well-organized.

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, output schema exists), the description is fairly complete: it covers the use case, data sources, input examples, and output structure. It does not explain parameter interactions (e.g., whether incident_id and pr_url are mutually exclusive) or the async behavior in this specific context, but these are minor gaps. The output schema handles return values, so the description does not need to detail them.

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

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

The description mentions inputs (GitHub PR URL or incident ID) but does not explain all parameters. For example, time_range_days, snyk_org_id, and async are not described in the description, even though the schema has low coverage (20%). The description compensates partially by indicating the use of Snyk data (linking to snyk_org_id) but leaves gaps. Baseline is 3 for low coverage, and the description only elevates slightly by clarifying purpose of pr_url and incident_id.

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 classifies root causes of change failures for CTO-level incident analysis, specifying inputs (GitHub PR metadata, Snyk vulnerability data) and outputs (structured categories with confidence scores). However, it does not explicitly distinguish itself from sibling tools that may also analyze change failures, such as dependency_vulnerability_scan or code_review_depth_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 provides usage context ('Ideal for post-mortem analysis and change risk assessment') but does not specify when not to use the tool or suggest alternatives among sibling tools. There is no guidance on conditions like insufficient data or specific incident scenarios where other tools would be more appropriate.

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

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

Annotations already indicate readOnlyHint=true (safe read operations). The description adds valuable context beyond annotations: data freshness (live data), proxy requirements for certain platforms, input format specifics, and fallback 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 well-structured with an introductory paragraph, bullet points for each mode, a data quality note, and input format examples. It is front-loaded but slightly verbose; one sentence could be trimmed without losing 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 tool's complexity (5 modes, multiple platforms, proxy dependency, input formats), the description covers all essential aspects. It explains outputs per mode, data sources, and fallback scenarios. An output schema exists but is not shown; the description compensates by detailing 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?

With 100% schema description coverage, the description still adds significant meaning: it explains what queries can be (keyword, product ID, seller ID, etc.), provides examples for seller_profile and price_history inputs, and elaborates on region and platform choices. This goes beyond the schema's enums and 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' for specific audiences and lists five distinct modes (product_search, seller_profile, etc.) with explicit purposes. This distinguishes it from sibling tools, all of which are non-China-e-commerce focused.

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 specifies target users (ZH diaspora, import-export teams, etc.) and explains when to use each mode. It also highlights data quality requirements (CN proxy needed for full data). However, it does not explicitly contrast with alternatives or state when not to use the tool.

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 readOnly and not destructive, and the description adds valuable behavioral context: it discloses data sources (Yahoo Finance, Eastmoney, HKEX), notes that some sources require a proxy (AICI_RESEARCH_PROXY_URL), and implies potential latency for proxy-dependent modes. This goes 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 well-structured with bullet points and clear sections, front-loading the main purpose. It is slightly verbose but each section contributes meaningful detail, balancing completeness with readability.

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

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

Given the tool's complexity (4 modes, 5 parameters, multiple data sources, proxy requirement, async option), the description is thorough. It covers all input formats, mode details, data source caveats, and references related tools (job_result). The presence of an output schema further reduces the need to describe 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?

The input schema covers all 5 parameters with descriptions (100% coverage), so the description adds limited semantic value. It provides examples (e.g., '600519' for Moutai) but does not significantly enhance understanding 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 identifies the tool as providing Chinese capital market intelligence, listing four specific modes (company, market_quote, sector_overview, regulatory_filing) and covering A-Shares, H-Shares, and ADRs. It distinguishes itself from sibling tools like china_ecommerce_intel and india_market_data by its focused domain.

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 details the four modes and accepted input formats, providing clear context for when to use each mode. However, it does not explicitly state when not to use the tool or compare it with alternatives within the sibling set.

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 the tool returns a deliverable and validates inputs server-side, which is consistent but does not reveal additional behaviors (e.g., expected processing time, output structure). No contradiction 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 concise (three sentences) but includes a tangential reference case. It front-loads the purpose but could be more efficient by removing the example and focusing on functionality.

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 (many nested parameters, no output schema), the description is insufficient. It omits details about the deliverable's format, how results are used, and any post-processing steps, 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 20% schema description coverage, the description should compensate but does not explain any parameters. It only says 'send the documented case fields', leaving the meaning of complex nested objects (company, accounts) undcribed.

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 is an anti-churn tool that returns a structured audited deliverable, but lacks a specific verb (e.g., 'analyze' or 'generate') and does not clearly distinguish from siblings like 'champion_mapping' or 'renewal_optimizer'. The reference case provides context but the purpose remains somewhat 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 on when to use this tool versus alternatives. The mention of server-side validation and 'send the documented case fields' implies a usage requirement but does not specify scenarios or exclude inappropriate uses.

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 destructiveHint=false, so the description adds value by detailing caching (7-day), SLA (≤20s), data sources (Open-Meteo CMIP6, IPCC AR6 Atlas, NOAA SLR), and output nature (multi-scenario comparison, probabilities, business impact signals). This contextualizes behavior beyond the annotations.

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

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

The description is long but well-structured, front-loading the core purpose then logically presenting scenarios, horizons, metrics, outputs, sources, and use cases. Every sentence adds value, though it could be slightly more concise by removing repetitive elements like 'SLA ≤20s' integration into the caching statement.

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, nested location object, multiple scenario families and metrics), the description covers purpose, scenarios, metrics, outputs, use cases, caching, SLA, and sources. Since an output schema exists, the description does not need to detail return values, yet it still describes output types (multi-scenario comparison, probabilities, business impact signals) for 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?

Input schema covers all 6 parameters with descriptions (100% coverage). The description adds meaning by elaborating on metrics (e.g., 'temperature (delta vs baseline 1990-2010, days >35°C, hot nights)') and explaining the 'all' scenario option. This extra context helps agents understand the parameter options beyond the schema's enum labels.

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. It lists specific scenarios (RCP_4_5, RCP_8_5, SSP1_2_6, etc.), metrics (temperature, precipitation, sea level, etc.), and output types (multi-scenario comparison, IPCC probability, business impact signals). This specificity distinguishes it from siblings like weather_climate_intel, which likely covers short-term weather.

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 lists explicit use cases such as TCFD/CSRD physical risk, long-term asset due diligence, catastrophe insurance, and infrastructure planning. It does not explicitly state when to avoid this tool or mention alternative tools, but the use-case list provides clear context for selection.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds 'Returns a structured, audited deliverable' and mentions server-side validation, but does not disclose additional behavioral traits like data sources (PubMed, ClinicalTrials, OpenFDA from example) 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 long, unstructured paragraph mixing purpose, examples, and a reference case. It lacks clear front-loading of essential information and would benefit from bullet points or separate sections for purpose and examples.

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 and no output schema, the description should detail the return format and parameter mapping. It only vaguely states 'structured, audited deliverable' and gives example queries. Missing information on pagination, error handling, or how parameters like 'intervention' and 'target_disease' map to use cases.

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

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

Schema coverage is only 13%, and the description does not explain individual parameters beyond the example queries. For instance, 'topic' is used in examples but not defined. The description adds marginal value but fails to compensate for the low schema 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 that the tool provides clinical evidence briefs with GRADE ratings, including example queries for efficacy, safety, comparative effectiveness, and evidence gaps. However, it does not explicitly differentiate from siblings like 'sci_literature_search' or 'clinical_pharma_intel', though the GRADE focus is distinctive.

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 queries but no explicit guidance on when to use this tool versus alternatives. There is no mention of when not to use it or comparative advantages. The phrase 'Inputs are validated server-side' hints at usage constraints but is insufficient.

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