gapup-mcp

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

The description adds useful behavioral context: 'Returns a structured, audited deliverable' and 'Inputs are validated server-side.' Annotations already declare readOnlyHint and openWorldHint, so the description complements these 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 somewhat concise but mixes languages and includes a specific reference case that may not be universally relevant. It could be more focused and clear.

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 the output format beyond 'structured, audited deliverable,' leaving the agent without enough information to correctly invoke the tool.

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

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

Schema description coverage is very low (13%). The description does not elaborate on any parameter meanings, merely stating to 'send the documented case fields.' It fails to compensate for the schema's lack of descriptions.

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

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

The description states 'Architecte ABM' and 'Returns a structured, audited deliverable,' indicating the tool generates an ABM deliverable. However, the purpose is vague due to marketing language and lack of a clear verb+resource statement. It does not differentiate from siblings like 'ld_architect' or '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?

No guidance is provided on when to use this tool versus alternatives. The description only mentions to 'send the documented case fields,' but does not explain context or exclusions.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint. The description adds context about data sources (robots.txt, OpenGraph metadata) and mentions output ranking. No contradictions, but could elaborate on async behavior and 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?

Three sentences, each adding value: action, method, inputs/outputs. Front-loaded with the core purpose. 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 output schema exists, description adequately covers usage. It explains the tool's function and data sources, though it could mention the async parameter as an operational detail.

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 75%. Description adds meaning by naming the firmographic filters and stating the output count (50 accounts). It complements the schema without repeating it fully.

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 as discovering 50 B2B lookalike accounts based on tech stacks and firmographics. It specifies the input (top 10 customer domains) and output (ranked list), distinguishing it from siblings like account_expansion_mapper.

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 explains when to use the tool (for targeted ABM campaigns) and what inputs are needed. However, it does not explicitly mention when not to use it or compare with alternatives among the sibling tools.

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

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

Annotations declare readOnlyHint and openWorldHint, and the description adds that inputs are validated server-side and output is an audited deliverable. This provides useful behavioral context 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 concise with four sentences, front-loading the purpose. However, it mixes French and English, which slightly 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 mentions a deliverable but gives no detail on its structure or content, and the reference case is not comprehensive enough to guide 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 (20%), and the description adds no explanation of parameters. It only says 'send the documented case fields', which does not clarify the specific fields or their meaning.

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 performs expansion account mapping for CRO and returns a structured deliverable. It is clear but does not differentiate from siblings like 'abm_architect' or 'upsell_hunter', which are also about account growth.

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 includes a reference case but does not specify prerequisites or when to choose this over 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 (readOnlyHint: true, openWorldHint: true) already indicate a read-only, open-world operation. The description adds only that it returns a 'structured, audited deliverable' and mentions a reference case. No behavioral details like auth, rate limits, or side effects beyond the annotation-provided traits. Minimal added value.

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

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

The description is relatively concise but includes an extensive reference case ('TechCorp SAS...') that may be extraneous. The key information is front-loaded, but the example could be more succinct without losing 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?

No output schema is provided, and the description only says 'structured, audited deliverable' without detailing the return format, fields, or how to interpret results. For a tool with 8 parameters and nested objects, this is insufficient for an agent to use it correctly.

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

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

Schema description coverage is only 13%, leaving 87% undocumented. The description provides no parameter explanations, merely stating 'Inputs are validated server-side'. It fails to add meaning to any of the eight parameters, including nested objects like company, ambitions, etc.

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 creates an ESG action plan and returns a structured deliverable. It provides a reference case and mentions 'agent-payable C-suite expertise', giving specificity. However, it does not explicitly differentiate from siblings like esg_audit_multi or sustainability_report, which are ESG-related.

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 over alternatives. The description only says 'send the documented case fields' but lacks context on prerequisites, typical use cases, or exclusions. With many ESG siblings, this is a significant gap.

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 details: it is asynchronous, requires the async flag, and outputs status, warnings, and source references. It aligns with annotations (no contradiction) and adds context about timeout avoidance.

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

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

The description is three sentences, front-loaded with purpose, then persona, async requirement, and outputs. Every sentence adds value without redundancy or verbosity.

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

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

Given the tool's complexity (5 params, async, NIST RMF), the description covers purpose, usage hint, and output types. The existence of an output schema (has output schema: true) reduces the burden. It could elaborate on NIST RMF protocols, 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 covers all 5 parameters with descriptions (100% coverage). The description repeats the modelOutput parameter ('accepts model outputs or decision boundaries') and mentions the async flag requirement, but adds little beyond schema for maxTests, adversarialDataset, and sensitivityThreshold. 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 evaluates AI model resilience against adversarial inputs following NIST AI RMF protocols, specifying it accepts model outputs or decision boundaries and returns structured risk scores, failure modes, and adversarial examples. This distinguishes it from siblings like jailbreak_attempt_detector or safety_guardrail_breach_analyzer.

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 notes 'Requires async:true to avoid timeout errors', guiding usage for performance. It also mentions it is 'designed for security and compliance personas', providing context. However, it lacks explicit when-not-to-use or alternative tool references, though siblings exist.

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 detailing the output structure (fraud probability score, suspicious IP list, pattern analysis) and input requirements (affiliate network, date range). It is consistent with annotations and does not contradict 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 three sentences plus a keywords tag, each serving a purpose: purpose, audience/context, inputs/outputs. It is efficient and front-loaded with the core action. However, the keywords tag is redundant and could be omitted for even greater conciseness.

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

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

Given the tool has 4 parameters, nested objects, and an output schema (as per context signals), the description adequately covers purpose, inputs, and outputs. It mentions the data source (Common Crawl) and target audience. It does not discuss edge cases or failure modes, but this is acceptable given the read-only, idempotent nature.

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%, and the input schema already documents all parameters (affiliate_network, date_range, threshold, async). The description merely restates that inputs are 'affiliate network name and date range', adding no new meaning beyond the schema for these or the other parameters. Baseline of 3 is appropriate.

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

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

The description uses a specific verb ('Analyzes') and resource ('affiliate clickstream data from Common Crawl') and clearly states the goal ('flag potential fraud patterns'). It lists example fraud types (duplicate IPs, rapid clicks, device spoofing), distinguishing it from generic fraud detectors. The target audience (CMOs) and business value (prevent budget waste) are also provided.

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

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

The description implies usage context ('Designed for CMOs to validate affiliate traffic quality') but does not explicitly state when not to use the tool or compare it to sibling tools like 'fraud_detector' or 'web_search_multilang'. It lacks explicit exclusions or alternative recommendations.

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

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

Annotations declare readOnlyHint, openWorldHint, and idempotentHint. The description adds behavioral context by mentioning that passing async:true avoids timeout and describing the return format (structured risk assessment, etc.). This 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 four sentences, front-loading the core function, then detailing inputs and outputs. It is concise with no wasted words, and the async instruction is placed at the end for 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 complexity, the description covers key aspects: data sources, inputs, outputs, and async behavior. An output schema exists (though not shown), so the description doesn't need to detail returns. Slightly lacking in explaining severity scoring or mitigation strategy format, but overall sufficient.

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

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

All 5 parameters are fully described in the input schema (100% coverage). The description adds minimal additional meaning beyond highlighting origin/destination countries and HS codes. The async parameter usage note is helpful but not critical. Baseline 3 applies as schema carries the load.

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 trade corridors using specific data sources. It mentions the role (COO), inputs (origin/destination countries, HS codes), and outputs (barrier mapping with severity scores, mitigation strategies). This differentiates it from sibling tools that focus on other Africa-related trade analyses.

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 a COO for analyzing NTBs, but it does not explicitly state when to use this tool versus alternatives. It lacks guidance on when not to use it or how it compares to sibling tools like africa_trade_finance_esg_rater or africa_trade_preference_arbitrage.

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, openWorld, and idempotent hints. The description adds value by noting the 'async' parameter to avoid timeouts, which is a behavioral trait not covered by 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 relatively concise at two sentences plus an async note, but it could be better structured with bullet points or clearer separation of purpose vs. usage. Still efficient.

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

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

Given the tool has an output schema and 4 parameters, the description provides context on data sources and use cases. It adequately covers the core function, though more detail on output structure or parameter relationships could help.

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%, providing baseline of 3. The description adds meaning to the 'async' parameter with usage guidance and reiterates the primary input (institution name), improving 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 states the tool evaluates ESG compliance of African trade finance providers using specific data sources (World Bank WITS, CDP) and returns an ESG rating with dimensional breakdown. It distinguishes itself from siblings by its geographic and sector 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 mentions ideal use cases ('due diligence on trade partners or portfolio risk assessment') but does not explicitly state when to avoid this tool or mention alternative tools for similar tasks, leaving ambiguity.

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

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint. The description adds valuable behavioral context: it uses WITS and OECD data, returns structured analysis, and crucially warns about the async parameter to avoid timeouts. This exceeds annotation coverage.

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 with no redundancy: purpose, details, output, critical usage note. Could be slightly more structured (e.g., bullet points) but efficient overall.

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 and an output schema, the description covers purpose, data sources, output nature, and a critical usage note. It doesn't explain the output schema, but since one exists, the burden is lower. Largely complete.

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

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

Schema coverage is 100% with descriptions for all 6 parameters. The description adds no extra meaning beyond what schema provides, except the async timeout note which is already in the parameter description. 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 analyzes AGOA/EBA trade preference arbitrage opportunities for COOs, comparing tariff rates, trade volumes, etc. It distinguishes from siblings like 'africa_trade_preference_optimizer' only implicitly; explicit differentiation 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 targets 'COOs evaluating export strategies' but provides no guidance on when to use this tool versus alternatives (e.g., 'agoa_eba_intelligence' or 'tariff_arbitrage_finder'). No exclusions or context for siblings.

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

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

Annotations already indicate readOnlyHint, openWorldHint, and idempotentHint. The description adds useful behavioral context, such as using external data sources (UN Comtrade, WCO tariff schedules, African Union rules) and generating specific outputs (duty savings estimates, optimal trade routes, recommendations). This goes beyond the annotations.

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

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

The description is appropriately sized and front-loaded, with the main action in the first sentence. It lists inputs, outputs, data sources, and use cases efficiently. The inclusion of keywords at the end is slightly redundant but does not detract significantly from conciseness.

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

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

Given the presence of an output schema and annotations, the description provides adequate context: it explains the tool's purpose, inputs, outputs, and data sources. However, it does not mention the async behavior of the optional parameter, and there is no discussion of error conditions or data freshness, which would improve completeness for a data-dependent tool.

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

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

The input schema has 100% description coverage, so all parameters are documented in the schema. The description mentions the three required parameters (originCountry, destinationCountry, hsCode) but does not add extra semantics beyond what the schema provides for optional parameters like async, quantity, and valueUsd. Therefore, the description adds minimal value in this dimension.

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

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

The description clearly states that the tool analyzes AGOA/EBA duty savings opportunities with HS code-level trade route optimization, specifying inputs and outputs. However, it does not explicitly differentiate from sibling tools like africa_trade_preference_arbitrage or agoa_eba_intelligence, which may have overlapping purposes.

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 'Ideal for export market evaluation, supply chain optimization, and trade agreement compliance analysis,' providing context for when to use the tool. However, it does not include explicit guidance on when not to use it or any comparisons to alternative tools, which is a gap given the many sibling tools in the same domain.

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 does not need to restate safety. The description adds context about the tool's coverage (countries, sources) but does not disclose other behavioral traits like synchronous/asynchronous behavior or response format. 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 a single paragraph that efficiently conveys the tool's purpose and capabilities. It is front-loaded with the core function. While it includes some redundancy with the parameter descriptions, it remains concise for a tool with multiple modes.

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

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

Despite the tool's complexity (5 parameters, 4 modes), the description does not explain the output format or what the tool returns. Since there is no output schema, this is a significant gap. It also lacks information on error handling or response behavior. The description is incomplete for an agent to fully understand the tool's results.

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

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

All parameters are documented in the schema with descriptions (100% coverage). The tool description provides an overview but adds minimal additional meaning beyond listing the modes. The schema already explains mode options, hs_code examples, etc., so the description does not significantly enhance parameter understanding.

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

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

The description clearly states the tool's purpose: checking eligibility for AGOA/EBA/GSP programs, product eligibility by HS code, identifying trade opportunities, and providing compliance rules. It distinguishes itself from siblings by mentioning the specific scope of 39 AGOA countries and 47 LDC EBA countries, making it unique.

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 through its list of modes (country_eligibility, product_eligibility, etc.), but does not explicitly state when to use this tool versus related siblings like africa_trade_preference_arbitrage 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 value beyond annotations by detailing the async requirement and the types of outputs (playbook steps, notification drafts, compliance checklists). However, there is a slight contradiction with the readOnlyHint annotation, as 'generates' implies creation, but the annotation claims the tool is read-only. This reduces transparency slightly.

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 and front-loaded, starting with the tool's purpose and followed by inputs, outputs, and a critical usage note about async. The keywords at the end are slightly redundant but not detrimental.

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

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

Given the presence of an output schema, the description does not need to explain return values. It covers the core purpose, inputs, and outputs, along with the async requirement. It could be more explicit about the playbook structure or compliance standards, but overall it provides sufficient context for an agent to use the tool effectively.

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

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

With only 17% schema description coverage, the description partially compensates by listing three parameters (incident severity, AI system type, affected stakeholders) and their role in generating outputs. However, it does not cover all parameters (e.g., async, incident_description) or provide format details 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 generates EU AI Act incident response playbooks with regulator notification templates, specifying inputs and outputs. However, it does not explicitly differentiate from sibling tools like ai_act_sandbox_regulatory_sandbox or incident_response_evidence_collector, leaving some ambiguity about when to use this specific tool.

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

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

The description implies usage for high-risk AI system breaches requiring formal EU notification and advises passing async:true to avoid timeout, but it does not explicitly state when not to use this tool or mention alternatives. The guidance is implicit rather than explicit.

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

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

Annotations already indicate read-only, open-world, and idempotent behavior. The description adds value by detailing the nature of feedback (warnings, citations, recommendations) and the sources used (EUR-Lex, OECD). 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 four sentences, each contributing essential information. It is front-loaded with the core purpose and efficiently covers inputs, outputs, and target audience without waste.

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 params, output schema exists), the description adequately covers the tool's purpose, inputs, and output nature. The return value is described ('detailed assessment with warnings, citations, and actionable recommendations'), and the output schema addresses formal structure.

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

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

Schema coverage is 100%, so the schema already documents parameters well. The description provides general context (e.g., 'technical specifications') but does not add significant meaning beyond what the parameter descriptions offer. Baseline of 3 is appropriate.

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

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

The description clearly states the tool's purpose: simulating EU AI Act regulatory sandbox submissions. The verb 'simulating' and resource 'regulatory sandbox submissions' are specific. It distinguishes from sibling AI Act tools (e.g., ai_act_incident_response) 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 usage for legal teams and AI developers preparing sandbox submissions, but it does not explicitly state when to use this tool versus related alternatives (e.g., ai_act_incident_response). No exclusions or when-not guidance is provided.

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

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint, indicating a safe, read-only, idempotent operation. The description adds extra context: it advises passing async:true to avoid timeout, implying the tool can be long-running. It also specifies the return includes a job_id when async is true. The description does not contradict annotations and provides useful behavioral guidance 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 three concise sentences. The first sentence defines purpose and outputs. The second lists inputs and outputs. The third gives use case and a crucial tip about async. Every sentence earns its place with no fluff or repetition of schema content.

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

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

The description, combined with the full input schema and annotations (readOnlyHint, openWorldHint, idempotentHint), provides a complete picture. The tool has an output schema (not shown but confirmed), so return values are documented. The description covers inputs, outputs, use case, and async behavior. It comprehensively sets expectations for a pre-deployment audit tool.

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

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

The input schema already provides descriptions for all four parameters (dataset_id, risk_threshold, include_bias_metrics, async). The tool description adds minimal value: it specifies that dataset_id is a Hugging Face ID or URL (already in schema) and that risk_threshold is optional (also implied by default). The async parameter is mentioned in context, but the schema already explains its behavior. With high schema coverage, the description's contribution is limited.

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

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

The description clearly states the tool audits AI training datasets for EU AI Act compliance with bias detection and risk assessment. It specifies exact inputs (dataset identifier and optional risk thresholds) and outputs (compliance score, bias metrics, etc.). This distinguishes it from sibling tools like ai_act_incident_response or ai_act_sandbox_regulatory_sandbox, which focus on other aspects of AI Act compliance.

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

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

The description explicitly says 'Ideal for pre-deployment risk evaluation' and advises using async:true to avoid timeout. It provides context on when to use (before deployment) but does not explicitly say when not to use or compare to alternatives like ai_governance_pilot or the full report tools. A clear use case is given, but exclusions are missing.

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 async nature (<300ms return), caching duration (7 days), and disclaimer. No contradiction with annotations; readOnlyHint aligns with non-mutating audit.

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 with bullets but slightly verbose. Could be more concise while retaining key info.

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 7 parameters and output schema, description thoroughly explains async workflow, caching, webhooks, and disclaimer. No gaps.

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

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

Schema coverage is 100% with descriptions; description adds no extra parameter detail. Baseline of 3 is appropriate.

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

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

The description clearly states it audits EU AI Act, classifies risk tiers, and lists 7 specific outputs. It distinguishes from sibling tool ai_governance_full_report_result by noting async polling.

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

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

Explicitly explains when to use: submit async and poll with result tool or register webhook. Provides alternatives (polling vs callback) and mentions caching behavior.

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 discloses all statuses (pending, completed, failed, not_found), TTL 24h, and details of the report content. Annotations already indicate read-only and idempotent; description adds significant behavioral 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?

Single paragraph containing all necessary information without redundancy. Could be slightly more structured but is efficient and front-loaded.

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

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

Given the complexity of polling an async job with multiple statuses and a detailed report, the description covers all aspects: statuses, TTL, usage timing, and report contents. Output schema exists, so return values are documented.

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?

Only one parameter (job_id) with schema description covering prefix constraint. Schema coverage is 100%, so description adds minimal extra value 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?

Description clearly states verb 'Poll' and resource 'result of an ai_governance_full_report_async job'. Distinguishes from sibling tools by specifying it's for polling results after async initiation.

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 when to call: after the eta_seconds hint (~90s). Does not explicitly mention when not to use, but context implies it should only be called after the async job.

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

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

Annotations already declare readOnlyHint and openWorldHint. The description adds that inputs are validated server-side and that the tool returns a structured deliverable, which is consistent. It does not disclose potential costs, rate limits, or details about the deliverable's format, but the added context is moderate.

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 concise with four sentences, but it includes unnecessary jargon ('Gapup agent-payable C-suite expertise (RISK)') that reduces clarity. The key information is front-loaded, but the structure 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?

The tool has complex nested inputs (5 parameters, 3 required) and no output schema. The description only specifies a 'structured, audited deliverable' without detailing its content or response format. For the complexity, the description is insufficient for an agent to correctly invoke the tool and interpret results.

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

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

Schema description coverage is only 20%, so the description should compensate, but it fails to explain parameters beyond mentioning 'send the documented case fields'. The reference case hints at what inputs like company, aiUseCases, and targetFrameworks mean, but it is vague and in French. The async parameter and focus parameter are ignored.

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 pilots AI governance and returns a structured, audited deliverable. It includes a reference case that clarifies the domain (high-risk AI use in HR). However, it does not distinguish this tool from siblings like ai_governance_full_report_async or vertical_ai_agent_governance, which may have overlapping purposes.

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

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

The description provides no explicit guidance on when to use this tool versus alternatives. It does not mention prerequisites, limitations, or comparison to sibling tools. The openWorldHint annotation suggests flexibility, but the description lacks actionable 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?

Annotations already provide readOnlyHint and openWorldHint. Description adds that inputs are validated server-side, but no further behavioral traits beyond that.

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

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

Description is short but includes jargon (Gapup agent-payable C-suite expertise) that may confuse. Structure is acceptable but not optimal.

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

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

No output schema, so description should explain return value. It only mentions 'structured, audited deliverable' without details on format or interpretation. Complex nested inputs are not compensated for.

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%). Description does not explain any parameters, leaving the agent without additional meaning for the complex nested 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 analyzes employee data to detect attrition risk and returns a structured deliverable. However, it does not differentiate from siblings 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?

No guidance on when to use this tool versus alternatives. No explicit context or exclusions provided.

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

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

Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds valuable context: confidentiality of awards, 24-hour cache stability, and no API key requirement. 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 clear sections: audience, covered institutions, three modes, confidentiality note, and caching info. It is front-loaded with essential purpose and concise with no waste.

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, 3 modes, 8 institutions, confidentiality constraints) and the presence of an output schema, the description covers all necessary context: modes, institutions, confidentiality, caching, and authentication.

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 baseline is 3. The description adds mode-specific guidance for the 'query' parameter and explains how each mode uses it, providing additional meaning beyond the schema.

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

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

The description clearly states the tool's purpose: commercial arbitration intelligence with three distinct modes (party_lookup, institution_index, clause_check). It specifies the target audience and covers 8 major institutions, distinguishing it from sibling tools like 'legal_clause_extractor'.

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 implicit guidance by explaining that most awards are confidential and that the tool only surfaces public awards and redacted statistics. It mentions caching and lack of API key, but does not explicitly compare to alternative tools for when not to use.

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

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

Annotations readOnlyHint=true and openWorldHint=true are consistent with the description, which says it returns a report and mentions external domain analysis. The description adds context that the deliverable is 'audited' and mentions a 14-day fix suggestion, but does not cover rate limits, authentication needs, 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 overly long, contains marketing fluff ('Gapup agent-payable C-suite expertise (RISK)'), and lacks a concise, front-loaded summary. It includes multiple example questions that could be streamlined.

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

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

The tool has 6 parameters (2 required) and no output schema. The description gives examples of outputs but does not explain the structured deliverable's format, handling of async, or how parameters like 'focus' and 'exclusions' affect results. It is insufficient for an agent to fully understand the tool's capabilities.

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

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

Schema description coverage is only 17% (only 'async' has a description). The tool description does not explain the meaning or usage of parameters like 'focus', 'exclusions', 'scope_cidrs', or 'include_email_surface' beyond implying email surface through an example. This 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 it performs attack surface monitoring for a domain, returning a structured audit deliverable. It gives clear examples of what it can do (find critical CVEs, email phishing risk, etc.). However, it does not differentiate from similar sibling tools like 'cve_security_lookup' or 'cyber_risk_auditor'.

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

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

The description provides explicit example queries that serve as usage guidelines, such as 'Which Internet-facing assets of <domain> combine a critical CVE...' and 'During M&A due diligence...'. It does not explicitly state when not to use the tool or mention alternatives.

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

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

Annotations already declare readOnlyHint=true, so the description adds minimal value beyond confirming a read operation. It mentions server-side validation but lacks detail on side effects or data access patterns.

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 includes jargon ('Gapup agent-payable C-suite expertise') that reduces clarity. It is front-loaded but could be more structured.

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

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

The description does not cover return format or interpretation of the deliverable, and there is no output schema. Given the tool's complexity (6 params, nested objects), more detail is needed.

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%), and the description only vaguely says 'send the documented case fields' without explaining any parameter semantics. The schema itself provides limited descriptions for nested properties.

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

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

The description clearly states the tool performs a pre-audit readiness assessment and returns a structured deliverable, referencing a concrete example. However, it does not distinguish this tool from sibling audit-related tools.

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

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

No explicit guidance on when to use this tool versus alternatives like esg_audit_multi or privacy_compliance_audit. The reference case provides an example but not contextual 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 already indicate readOnlyHint, openWorldHint, and idempotentHint. The description adds that the tool uses World Bank and ECB SDW data for benchmarks and provides specific cost-reduction levers, which is consistent and adds valuable behavioral context 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 a single paragraph, front-loaded with the core purpose. It is efficient but could be slightly more concise by removing redundant marketing language. Overall, it is well-structured and clear.

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 need not detail return values. It covers the tool's purpose, input requirements, data sources, and use case. For a read-only analytical tool, this 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%, so the input schema already fully describes each parameter. The description repeats some parameter names (account maintenance, wire transfers, credit lines) but does not add significant new meaning beyond what the schema provides. Baseline score of 3 is appropriate.

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

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

The description clearly states the tool is a CFO-focused analyzer for bank fee structures, providing negotiation recommendations. It uses specific verbs ('analyzes', 'provides') and distinguishes itself from siblings by being narrowly focused on bank fees and treasury 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 explains when to use the tool (input current fees and bank details to get benchmark comparisons and negotiation levers). It doesn't explicitly state when not to use it, but the niche scope implies it's for bank fee negotiation, which is clear given no closely related sibling tools.

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

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

Annotations provide readOnlyHint and openWorldHint, but the description adds that inputs are validated server-side and output is audited. However, it doesn't disclose any behavioral traits beyond what annotations imply, such as rate limits or side effects. The bar is low due to annotations, but the description adds marginal value.

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

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

The description is short but mixes French and English, includes an unnecessary specific reference case, and uses jargon ('Gapup agent-payable C-suite expertise'). It could be more concise and standardized.

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 5 parameters with nested objects and no output schema, the description is insufficient. It doesn't explain what the deliverable contains, how to interpret results, or prerequisites beyond vague reference to 'documented case fields'. The reference case is specific but not generally instructive.

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

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

Schema description coverage is only 20% (only 'async' has a description). The description does not elaborate on other parameters like competitor, dealContext, or ourOffer. It says 'send the documented case fields' but doesn't map to parameters. With low coverage, description should compensate, but it doesn't.

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 returns a structured, audited deliverable referred to as a 'Fiche de combat live' (live battle card). It implies a competitive intelligence output but doesn't explicitly differentiate from siblings like competitive_deep_dive or competitor_intel, limiting clarity in a crowded toolset.

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 instruction to 'send the documented case fields' is vague and doesn't help an agent decide between this and other competitive analysis tools. Missing when-not or 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 indicate readOnlyHint=true and openWorldHint=true, which align with the tool generating a deliverable without side effects. The description adds that inputs are validated server-side, but does not explain the async parameter behavior or the nature of the return value beyond being 'structured, audited'. The added context is minimal and does not disclose potential behaviors like processing time or polling.

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 (three sentences) but includes a fragmented first sentence and a reference case that may not be universally relevant. It could be more concise by front-loading the core function (marketing plan generation) and removing the cryptic 'Gapup agent-payable C-suite expertise (CMO)' phrase.

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 11 parameters, no output schema, and minimal description. The description does not explain the deliverable's structure, the role of the 'async' parameter, or how to interpret results. This leaves significant gaps for the agent to infer or fail on execution.

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 9% schema description coverage, the description should compensate by explaining key parameters like companyName, arrCurrent, etc. Instead, it only provides a reference case example, which implies parameter meanings but does not explicitly define them. The schema itself lacks descriptions for most fields, so the tool definition fails to guide the agent on what values to provide.

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

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

The description clearly states that the tool returns a structured, audited marketing deliverable, referencing a 'Plan de bataille marketing' for CMO expertise. However, it does not differentiate from marketing-related siblings like brand_builder or positioning_strategist, leaving ambiguity about when to choose this tool over others.

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. The description lacks explicit use cases, prerequisites, or exclusions, leaving the agent without decision-making support.

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, idempotentHint, so no destructive actions. The description adds that it accepts model identifiers or output samples and returns structured bias metrics and amplification trends, aligning with annotations and providing useful context beyond 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 two sentences with no fluff: first sentence defines purpose and data source, second sentence specifies users and outputs. Every part is necessary and efficient.

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

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

Given the tool's 4 parameters and presence of an output schema, the description covers purpose, inputs, outputs, and target audience. It lacks detail on specific metrics computed, but the output schema likely provides that, so it is reasonably complete.

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

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

Schema description coverage is 100%, so the schema already explains all 4 parameters well. The description adds context by summarizing accepted inputs (modelId, outputSamples, demographicGroups) 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 states the tool tracks bias amplification in LLM outputs using HuggingFace model leaderboard, with a specific verb and resource. It distinguishes itself from sibling tools like model_safety_certification_checker or 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 targets 'risk assessment personas' but does not explicitly state when to use this tool over alternatives or provide exclusions. Usage is implied by its focus on bias amplification, but no direct comparison with sibling tools is given.

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

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

Annotations already declare readOnlyHint=true and idempotentHint=true, indicating safe, idempotent behavior. The description adds value by specifying that the output includes 'structured compliance insights with source references,' providing additional clarity on what the agent can expect 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 three sentences long, front-loaded with the primary purpose, and contains no filler. Every sentence adds value: context (CFO role), mechanism (analyzes against benchmarks), and use cases (pre-issuance/monitoring). It is concise and well-structured.

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

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

The description covers the tool's purpose, usage context, and output nature. Given that the input schema and output schema exist, the description provides sufficient high-level context. It omits details on specific output fields, but the output schema handles that, so completeness is solid.

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 schema already documents all 6 parameters adequately. The description does not add significant extra meaning about the parameters beyond what is in the schema, so baseline score of 3 is appropriate.

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

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

The description clearly states the tool's purpose: to assess bond covenant compliance against BIS and ECB ESG standards. It specifies the verb 'assess' and the resource 'bond covenant text' with regulatory benchmarks. However, it does not explicitly distinguish itself from similar sibling tools like bond_covenant_monitor or esg_audit_multi, preventing a score of 5.

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, stating it is 'Ideal for pre-issuance due diligence or ongoing monitoring.' This gives general guidance but lacks explicit when-to-use/when-not-to-use instructions or comparisons to alternative tools, earning a score of 3.

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

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

Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds context about using real-time data and SEC references but does not contradict annotations. It provides some behavioral context beyond the structured fields.

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

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

The description is three sentences plus a keyword list. It is moderately concise but contains marketing fluff ('As a CFO', 'Ideal for proactive debt management') that 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 presence of an output schema, the description appropriately notes the returned items (compliance status, metrics, SEC refs). It covers input, output, and use case, leaving minimal gaps for its complexity level.

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 (67% with descriptions for all parameters and nested property descriptions). The description mentions 'debt-to-EBITDA' which is not a parameter, adding slight confusion. It does not significantly add 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 the tool monitors bond covenant compliance using leverage and interest coverage ratios. However, it does not explicitly distinguish itself from the sibling 'bond_covenant_esg_compliance_checker', which could cause confusion.

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 input requirements (ticker, optional thresholds) and use case (debt management, compliance tracking), but lacks explicit guidance on when not to use this tool or mention of alternatives like the ESG-focused sibling.

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

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

Annotations provide readOnlyHint and openWorldHint, but the description adds minimal behavioral context. It does not describe output format, size limits, or side effects beyond stating it returns a 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?

Three sentences, front-loaded with purpose and reference case. Efficient but could add more useful detail without becoming verbose.

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

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

Given 4 parameters, nested objects, no output schema, and many siblings, the description lacks explanation of output, async behavior, and differentiation from similar tools like ftg_business_plan.

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 (25%) with only async described. The description says inputs are validated server-side but provides no additional meaning for the complex nested parameters (company, raise, keyMetrics).

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 business plan narrative with CFO expertise, and gives a reference case. However, it does not differentiate from the sibling tool ftg_business_plan, which may have similar 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?

No guidance on when to use this tool vs alternatives. The description only mentions server-side validation and to 'send the documented case fields', without context or exclusions.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds only that inputs are validated server-side and returns a deliverable, but does not detail behavioral aspects like auth needs, rate limits, or processing time.

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 mixes French and English, includes a specific reference case that is not immediately helpful, and is not front-loaded with the most critical information. The space is not fully justified.

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

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

With no output schema and complex nested parameters, the description should clarify the return format and expected output. It merely says 'returns a structured, audited deliverable' without specifics, leaving the agent under-informed.

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%). The description does not explain the parameters beyond 'send the documented case fields', failing to add meaning for the complex nested objects.

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

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

The description clearly states it is a brand architect tool providing C-suite expertise and returns a structured deliverable. It references a specific case (Pennylane) but does not explicitly differentiate from siblings like brand_equity_voice_share_calculator or positioning_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?

No guidance on when to use vs. alternatives or when not to use. The description mentions 'send the documented case fields' but lacks exclusions or context for decision-making.

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 context about data sources (Common Crawl, Wayback Machine) and potential timeouts, and outlines outputs (voice share percentage, sentiment distribution, top sources). 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 extremely concise: three short sentences that front-load the primary purpose and quickly cover key inputs, outputs, and a usage tip. Every sentence adds value without redundancy.

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

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

Given the tool has 5 parameters (2 required), nested objects, and an output schema, the description provides a high-level overview but lacks detail on parameter semantics and behavior for optional parameters. The existence of an output schema helps, but the description does not fully equip an agent to correctly populate all inputs.

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

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

Schema description coverage is low (20%), so the description should compensate. While it mentions brand, competitors, time range, and async flag, it fails to explain the 'competitors' array, 'include_forums' boolean, or the structure of the 'time_range' nested object. This leaves significant gaps for an agent.

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

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

The description clearly states the tool calculates brand equity voice share for CMOs by analyzing mentions across 500K+ news articles and forums. It specifies inputs, outputs, and data sources, distinguishing it from sibling tools like 'brand_builder' or 'content_engine'.

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 guidance on when to use the tool ('Ideal for competitive benchmarking and brand visibility tracking') and hints at timeout behavior ('Pass async:true to avoid timeout'). However, it does not explicitly state when not to use or mention 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?

Beyond the annotations (readOnlyHint, openWorldHint), the description adds that inputs are validated server-side and that it returns a structured, audited deliverable. This provides useful behavioral context 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?

The description is moderately concise but includes a verbose list of example queries and mixes French and English. It could be more structured by separating core function from 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?

Given the tool's complexity (4 parameters, nested objects, no output schema) and many siblings, the description lacks details on return format, error handling, async behavior documentation (only in schema), and how to interpret results. It feels incomplete.

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

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

Schema description coverage is only 25% (only async parameter documented). The description does not explain the meaning of entity or budgetContext parameters, despite their nested structure and required fields. Users must infer from the example queries.

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 budget variance analysis tool that returns a structured deliverable, with example queries specifying its capabilities. However, it does not sharply distinguish it from similar finance tools like earnings_reviewer 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 explicit guidance on when to use this tool versus alternatives. The description provides example queries but lacks context about prerequisites, limitations, or comparisons with sibling tools.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly states 'Deterministic database lookup — no LLM, no generation, sub-second,' which conveys predictable, fast, non-generative behavior. It also provides coverage statistics, giving a sense of limitations. However, it does not describe behavior for unmatched locations or potential error modes, which would make it fully transparent.

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

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

The description is compact and well-structured, with the main purpose stated in the first sentence, followed by coverage data and behavioral characteristics. Every sentence adds value: purpose, scope/coverage, and performance/reliability. No fluff or repetition.

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

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

Despite having 5 parameters and no annotations, the description is sufficiently complete for a database lookup tool. It covers purpose, geographic scope (6 EU countries), specific data categories, legal context, and performance characteristics. The existence of an output schema means return values need not be described in text. The only minor gap is explicit usage alternatives, but that is already covered under usage guidelines.

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

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

The input schema has 100% description coverage for all 5 parameters, including lat/lng, async, radiusM, and minAreaM2. The tool description adds context like 'roof surfaces (m²)' and 'parking areas' that aligns with minAreaM2, but it does not substantially exceed what the schema already documents. Baseline of 3 is appropriate.

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

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

The description begins with a specific action verb ('Enrich') and identifies a clear resource ('a location with European building intelligence'), then lists concrete data types (roof surfaces, parking areas, solar-obligation status, existing solar installations). This clearly distinguishes it from broader sibling tools like real_estate_intel or geo_logistics_intel by specifying the exact 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 implies usage context through mentions of French solar obligations and EU coverage, but does not explicitly state when to use this tool versus alternatives or when not to use it. There are no exclusions or alternative tool references, leaving usage guidance implicit rather than direct.

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

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

The description goes beyond annotations by explicitly stating that all processing is local, privacy-preserving, instant, and that diversity signals are first-name proxies only with a mandatory ethical warning. It also describes the scoring breakdown per criterion and the output features (red flags, green flags, interview questions). This adds significant behavioral context that annotations alone do not 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 information-dense yet well-structured. It front-loads the purpose, then lists criteria, output format, and caveats. Every sentence adds value without redundancy, achieving conciseness despite the complexity of the tool.

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

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

Given the tool's complexity (5 parameters, nested objects, output schema), the description covers all necessary aspects: input requirements, processing details, scoring criteria, output fields (including ethical warnings), and performance characteristics. It leaves no obvious gaps for an agent to misunderstand usage or expectations.

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 baseline is 3, but the description adds substantial meaning beyond the schema by explaining the five weighted criteria in detail (e.g., skills_match extracts tech stack and soft skills, experience_match compares years to seniority level). This extra context helps the agent understand how to effectively set criteria_weights and interpret results.

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: AI-powered candidate screening and ranking that ingests a job description and 1-50 resumes to return a ranked shortlist with score breakdowns across five weighted criteria. It uses specific verbs and resources ('screening and ranking', 'ingests', 'returning') and provides detailed criteria, effectively distinguishing it from sibling tools like 'talent_intelligence' or 'recruiting_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?

The description implicitly defines usage by detailing the tool's input and output, making it clear that it is intended for screening and ranking candidates. However, it does not explicitly state when to use this tool versus alternatives, nor does it mention exclusion criteria or prerequisites.

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 inputs are validated server-side, implying error handling behavior. It does not contradict annotations, but it adds minimal behavioral context beyond what annotations provide.

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

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

The description is brief (3 sentences) but mixes French and English, which may reduce clarity for some agents. It front-loads the purpose and includes a reference case, but the language mixing is a minor structural flaw.

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

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

Given the tool has no output schema, complex nested inputs, and 7 parameters, the description is insufficient. It doesn't describe the deliverable's structure, how to interpret results, or any expected response format. The reference case hints at outcomes but not structure.

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

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

Schema description coverage is only 14%, and the description contributes no parameter-level meaning. It vaguely says 'send the documented case fields' but doesn't list or explain parameters. The schema's own descriptions for nested objects are sparse, leaving the agent with little guidance.

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

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

The description clearly indicates the tool is for capacity planning for CHROs, mentioning it returns a structured, audited deliverable and providing a reference case. The French title and description reinforce the specific domain, though it lacks a single verb+resource phrase. The purpose is distinct from sibling tools.

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

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

The description gives no guidance on when to use this tool versus alternatives like 'capital_strategy' or 'growth_path_architect'. It only asserts inputs are validated server-side, which is a technical note, not usage context. No exclusions or examples of when not to use.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds little beyond stating it returns a structured deliverable and that inputs are validated server-side. No disclosure of side effects or constraints.

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

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

The description is relatively concise at 4 sentences, front-loaded with the purpose. However, it could be more informative without adding length.

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

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

Given the complexity (nested objects, 5 parameters, no output schema), the description is incomplete. It does not explain the deliverable's contents, how to interpret results, or mention the async parameter. The reference case provides some context but insufficient detail.

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 tool description does not describe any parameters. It only says 'send the documented case fields' without detailing the required fields, leaving the agent without sufficient context to populate inputs correctly.

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

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

The description clearly states that the tool returns a structured, audited deliverable for financing strategy, and provides a reference case. However, it does not explicitly differentiate from sibling tools, though the purpose is clear.

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

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

The description lacks explicit guidance on when to use this tool versus alternatives. It does not mention when not to use it or provide comparison with siblings, only stating that inputs are validated server-side.

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 minimal behavioral context beyond stating it returns a deliverable. No contradictions, but no rich elaboration on processes 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?

The description is two sentences in French plus a reference case. It is somewhat concise but could be more structured, and the mix of French and English context may hinder 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 tool's complexity (6 parameters, nested objects, no output schema), the description is insufficient. It does not explain the deliverable's content, interpretation, or prerequisites, leaving many gaps.

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

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

With only 17% schema description coverage, the description should compensate by explaining key parameters, but it does not. It only says 'send the documented case fields', providing no additional meaning.

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

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

The description clearly states the tool is a cap table strategist for fundraising, returning a structured audited deliverable. It mentions a reference case, which aids understanding, but does not explicitly differentiate it from sibling tools like 'capital_strategy' or 'deal_structurer'.

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. The description lacks context about appropriate scenarios, exclusions, or comparisons to sibling tools.

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

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

Annotations (readOnlyHint, idempotentHint, destructiveHint) already cover safety and idempotency. The description adds accuracy (±20%) and output details but does not disclose behavioral traits like execution time or side effects beyond the schema.

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

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

The description is a single coherent paragraph with logical flow: what it does, outputs, when to use, inputs. However, the 'Delivered by Émilie' line is extraneous and adds no value, preventing a perfect score.

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 and low schema coverage, the description provides a good overview of outputs and purpose but lacks detail on input parameters and their relationships. The existence of an output schema helps, but 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%, and the description only vaguely mentions 'company profile and its activity data.' It fails to elaborate on the eight parameters, including nested objects like company, perimeter, and emission sources, leaving the agent with insufficient guidance.

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

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

The description clearly states the tool calculates a company's greenhouse-gas footprint under the GHG Protocol, specifying scope, accuracy, and outputs. It distinguishes from siblings like 'esg_audit_multi' by focusing on carbon footprint with precise protocol and tiers.

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 provides use cases: 'CSRD compliance pre-audit, green-finance access, or supplier ESG scorecards.' It offers clear context but does not specify when not to use or mention alternative tools, which slightly reduces the score.

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

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

Annotations declare readOnlyHint=true and openWorldHint=true. The description adds no additional behavioral context, such as authentication requirements, rate limits, or what happens to existing data. It merely restates that it returns a deliverable, which is already implied by 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 short (two sentences) but includes unnecessary jargon ('Gapup agent-payable C-suite expertise') that does not aid understanding. The second sentence about validation and reference case is useful but could be more concise. Overall adequate but not 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 (8 parameters, nested objects, no output schema), the description is severely lacking. It does not mention return format, output structure, or any examples. The absence of output schema increases the need for description completeness, which is not met.

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 provides zero explanation of parameters or their meaning. It only says 'send the documented case fields,' which adds no value. The parameters are complex nested objects, and without description assistance, agents cannot understand usage.

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' related to a carbon roadmap, but lacks a clear verb-resource pairing (e.g., 'Generates a carbon reduction roadmap'). The jargon 'Gapup agent-payable C-suite expertise' obscures the purpose. Among sibling tools like 'carbon_footprint_calculator' and 'sustainability_report', this description does not differentiate clearly.

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

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

No explicit guidance on when to use this tool versus alternatives. The description mentions input validation and a reference case but does not specify when it is appropriate or not appropriate to invoke this tool compared to siblings.

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

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

Annotations indicate readOnlyHint and openWorldHint. Description adds that inputs are validated server-side and output is a structured audited deliverable, which is consistent. No additional behavioral traits disclosed.

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, but the reference case adds length without essential information. Could be trimmed to 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?

No output schema and complex nested parameters; the description does not explain the output format or the purpose of individual fields, leaving 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?

Schema description coverage is only 25%, and the description does not explain the parameters beyond 'send the documented case fields'. It fails to add semantic value for the 4 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 it performs champion mapping for C-suite expertise and returns a structured deliverable. The reference case helps illustrate, though the exact scope is slightly 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 guidance on when to use this tool versus alternative tools. Sibling tools suggest many analysis options, but no differentiation is provided.

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

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

Annotations declare readOnlyHint, openWorldHint, and idempotentHint, indicating safe, non-destructive behavior. The description adds that it uses specific data sources and produces categories with confidence, but does not disclose additional behavioral traits like failure modes 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?

Three concise sentences with no redundancies. The description is well-structured and front-loaded with the core 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?

The description covers the main purpose and general inputs but omits details on optional parameters like snyk_org_id and time_range_days. Given the tool has 5 parameters and an output schema, more parameter context would improve completeness.

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

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

Schema description coverage is low (20%), and the description does not mention any parameter details, such as required fields or valid values. It fails to compensate for the schema's lack of descriptions.

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

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

The description clearly states the tool classifies root causes of change failures using GitHub PR metadata and Snyk vulnerability data. It specifies the output as structured categories with confidence scores, making the purpose distinct from sibling tools.

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

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

The description says 'Ideal for post-mortem analysis and change risk assessment,' implying usage context but not explicitly stating when to use or alternatives. 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 provide readOnlyHint, openWorldHint. Description adds valuable behavioral context: live data requires CN proxy, otherwise fallback to AliExpress; async mode described. 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 well-structured with bullet points for modes and notes. It's slightly long but every sentence adds value. Could be more concise, but not overly verbose.

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

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

Given high schema coverage, presence of output schema, and annotations, description covers modes, inputs, data sources, and limitations. Missing error handling details, but satisfactory overall.

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% (baseline 3). Description adds meaning beyond schema: input formats for seller_profile and price_history, region and platform notes, and query maxLength. Enhances clarity.

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

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

The description clearly states the tool's purpose: 'Chinese e-commerce intelligence for the ZH diaspora...' and lists five specific modes with brief explanations. It distinguishes from siblings like 'china_market_data' by being more comprehensive.

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 guidance on when to use each mode, data quality notes (proxy requirement), and input formats. However, it lacks explicit when-not-to-use instructions or alternatives for similar tasks.

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

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

The description adds significant behavioral context beyond annotations: it details data sources (Yahoo Finance, Eastmoney, HKEX), proxy requirements for full coverage, and the asynchronous polling mechanism via job_result. No contradiction with annotations (readOnlyHint=true, destructiveHint=false) is present.

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 and bullet points, making complex information scannable. It is slightly verbose but every sentence adds clarity given the tool's breadth. No redundancy is present, earning a high score for its informational density.

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, 2 required, 2 enums, output schema exists), the description covers all essential aspects: modes, input formats, data sources, proxy requirements, and the async flow. It is complete without needing to describe return values, as the output schema handles that.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by elaborating on the modes (e.g., 'company — full company profile...'), clarifying input formats (ticker, name, sector), and explaining the async parameter’s purpose. This extra context raises the score above baseline.

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

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

The description clearly states it provides Chinese capital market intelligence, covering A-Shares, H-Shares, and ADRs across four distinct modes (company, market_quote, sector_overview, regulatory_filing). Each mode is briefly defined with specific output details, making the tool's purpose highly specific and distinguishable from siblings like india_market_data or historical_price_series.

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 (for Chinese market data) but does not explicitly state when not to use it or name alternative tools. It lacks explicit guidance on distinguishing from similar tools (e.g., india_market_data, realtime_data_streams), leaving the agent to infer usage boundaries.

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

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

Annotations already declare readOnlyHint=true, indicating no side effects. The description adds that the tool returns a 'structured, audited deliverable' and mentions server-side validation, which is consistent. However, it does not elaborate on what the deliverable contains, authentication needs, or rate limits. The behavioral insight is adequate but not enriched beyond the annotation.

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

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

The description is three sentences, reasonably concise and front-loaded with the tool's identity. The reference case adds some length but is relevant. It could omit the reference case or integrate it more efficiently, but overall it wastes little space.

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

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

Given the tool's complexity (nested objects, 5 parameters, no output schema) and the presence of many related sibling tools, the description is insufficient. It does not explain the deliverable's structure, how results are audited, or how to handle errors. The absence of output schema and the high parameter count demand a richer description to guide the agent effectively.

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

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

The description does not explain any parameters beyond the schema; it merely says 'send the documented case fields.' With schema coverage at only 20% (only async described), the description fails to compensate by clarifying the purpose of the many nested fields (e.g., company properties, account signals). A tool with complex inputs requires more parameter guidance.

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

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

The description clearly identifies the tool as an anti-churn analysis tool that returns a structured deliverable for C-suite (CRO). The reference case adds specificity. However, it does not explicitly differentiate from sibling tools like renewal_optimizer, save_plays, or upsell_hunter, which operate in adjacent spaces.

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 concrete reference case (Spendesk) indicating a typical use case, but lacks explicit guidance on when to use this tool versus alternatives, or any exclusion criteria. The phrase 'send the documented case fields' implies a specific input format but does not define the context of use relative 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?

Annotations show readOnlyHint=true, destructiveHint=false, idempotentHint=false. The description adds behavioral details: caching (7 days), SLA (≤20s), and async behavior. This is consistent and provides useful operational 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 a single paragraph that front-loads the main purpose and uses efficient, information-dense sentences. Could benefit from bullet points for readability, but every sentence adds value without redundancy.

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

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

Given the complexity (6 parameters, nested objects, enums, output schema), the description covers all necessary aspects: scenarios, metrics, horizon, location, use cases, data sources, caching, and SLA. The output schema exists so return value details are not required.

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 description adds value by explaining parameter context: what 'all' scenario does, metric descriptions (temperature delta vs baseline, extreme events), and location flexibility (city/country or lat/lon). This enriches 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 states the tool's purpose: long-term climate projections under IPCC scenarios (RCP and SSP) for any location. It lists specific scenarios, metrics, horizons, and output types, distinguishing it from sibling tools like weather_climate_intel by focusing on IPCC-based projections for TCFD/CSRD risk 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?

The description provides explicit use cases (TCFD/CSRD physical risk, due diligence, insurance, infrastructure planning) and mentions multi-scenario comparison. However, it does not explicitly exclude or compare to other tools like weather_climate_intel, leaving room for ambiguity about alternatives.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds context: inputs are validated server-side, it returns a structured/audited deliverable, and async usage is explained. No contradictions; the extra detail on validation and async behavior is helpful 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 front-loaded with the core purpose but becomes verbose with multiple example questions and a reference case. It could be more concise by focusing on the tool's role and key constraints.

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

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

No output schema is provided, and the description does not detail the return value structure beyond 'structured, audited deliverable'. With 8 parameters and 4 required, the description omits critical info like output fields or how results are formatted.

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

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

Schema description coverage is low (13%). The description does not explain key parameters like topic, max_studies, evidence_focus, or intervention_type. It only vaguely mentions 'send the documented case fields'. With 8 parameters and minimal schema descriptions, 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 clearly states it produces a clinical evidence briefing with GRADE ratings, and gives specific example queries (e.g., 'Review the clinical evidence for <drug/intervention> in <indication>'). It distinguishes from siblings like clinical_pharma_intel by focusing on GRADE and structured deliverables. However, jargon ('Gapup agent-payable C-suite expertise') slightly muddies clarity.

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

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

The description implies usage via example questions (e.g., safety scanning, comparative effectiveness) but does not explicitly state when to use this tool versus alternatives like sci_literature_search or clinical_pharma_intel. No when-not-to-use guidance is provided.

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