gapup-mcp

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 and that it returns a deliverable, which aligns with read-only behavior. However, it does not disclose other traits like performance, auth requirements, or output structure beyond 'structured, audited deliverable'.

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

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

The description is short (three sentences including the reference case) and front-loaded with the core purpose. The reference case adds concrete context without excessive verbosity. However, the first sentence is a noun phrase rather than an imperative verb, slightly reducing clarity.

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

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

Given the tool's complexity (8 parameters, nested objects, no output schema, low schema coverage), the description omits essential details: what the deliverable contains, how to interpret results, and how to handle edge cases. The reference case is helpful but does not compensate for the lack of comprehensive guidance.

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

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

Schema description coverage is only 13%, meaning most parameters lack schema descriptions. The description does not explain the required fields (company, product, targetAccounts, icpCriteria) or their meaning; it merely says to send 'documented case fields'. The reference case provides an example but does not map to schema 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 identifies the tool as an ABM architect for C-suite expertise, returning a structured audited deliverable. The verb is implied ('architect') rather than explicit, and the reference case provides context. It distinguishes from siblings like 'abm_lookalike_account_finder' but could be clearer on the specific action (e.g., 'generates an ABM plan').

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

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

No explicit guidance on when to use this tool versus alternatives like 'abm_lookalike_account_finder' or 'account_expansion_mapper'. The reference case hints at typical usage but does not provide criteria for selection 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 (readOnlyHint, openWorldHint, idempotentHint) are consistent with a read-only, idempotent tool. The description adds behavioral details (analyzes public web data, returns ranked list) beyond annotations. No contradictions.

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

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

The description is concise (two sentences), front-loaded with the primary purpose, and avoids unnecessary details. Every sentence adds value.

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

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

The description covers the tool's function, inputs, data sources, and output. Given the presence of an output schema and annotations, it is fairly complete. However, it could briefly note the async option for slow operations.

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% (3 of 4 params described). The description adds meaning by linking tech_stack_keywords and firmographic_filters to the tool's purpose. The async parameter is not mentioned in the description, but its schema description is clear.

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

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

The description clearly states the tool's purpose: discovering 50 B2B lookalike accounts based on top customers' tech stacks and firmographics. It specifies the data sources (robots.txt, OpenGraph metadata) and output (ranked list). This distinguishes it from siblings like 'account_expansion_mapper' or 'competitive_deep_dive'.

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 ABM campaigns ('As a CMO...for targeted ABM campaigns') and specifies inputs (top customer domains, firmographic filters). However, it does not explicitly state when to prefer this tool over siblings, e.g., what scenarios warrant lookalike finding vs account expansion.

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 aligns with annotations (readOnlyHint, openWorldHint) by stating it returns a deliverable and referencing a case. It does not contradict, but also does not add significant behavioral context beyond what annotations convey.

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

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

The description is moderately concise but includes extraneous detail like the reference case (Notion, Snowflake, etc.). It front-loads the purpose, but could be more streamlined.

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

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

Given the tool's complexity (5 parameters, nested objects, no output schema), the description is insufficient. It does not describe the output format, how to use results, or any constraints. The openWorldHint is not explained.

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

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

Schema description coverage is only 20%, and the description does not explain any of the 5 parameters beyond 'send the documented case fields.' No value is added for understanding complex nested fields like company, accounts, or ownership.

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

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

The description states it returns a structured, audited deliverable for account expansion mapping, with a specific reference case (Notion B2B Enterprise). However, it mixes French and English and does not differentiate from sibling tools like growth_path_architect or upsell_hunter.

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 only says to send documented case fields and mentions server-side validation. It provides no guidance on when to use this tool versus alternatives or when not to use it.

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

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

Annotations include 'readOnlyHint: true' and 'openWorldHint: true', and the description's claim of returning a deliverable aligns with a read operation. However, the description does not elaborate on the 'agent-payable' aspect, side effects, or any limitations beyond server-side validation. No contradiction with annotations.

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

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

The description is concise (two sentences plus a reference case) and front-loaded with the tool's purpose. However, it could be more structured by separating the usage instruction or parameter hints.

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

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

Given the tool has 8 parameters, nested objects, and no output schema, the description is insufficient. It does not describe the output format, constraints, or prerequisites beyond validating inputs server-side. The reference case provides an example but does not cover general usage scenarios.

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

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

Schema description coverage is low (13%). The description does not explain any parameter semantics; it merely instructs to 'send the documented case fields' without detailing what those fields are. This forces the agent to rely solely on the schema, which lacks descriptions for many 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 returns a structured, audited ESG action plan. It uses specific language like 'Plan d'action ESG' and references a concrete case, but does not explicitly differentiate it from similar tools like 'esg_audit_multi' or 'carbon_roadmap'.

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 asking to 'send the documented case fields' but provides no explicit guidance on when to use this tool over alternatives. The reference case gives context but no when-not or comparative advice.

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

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

The description discloses key behavioral traits: the tool is asynchronous, requires async:true, and outputs status, warnings, and source references. Annotations already indicate readOnlyHint, openWorldHint, and idempotentHint, so the description adds value by specifying the NIST framework and red-teaming 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 concise with three sentences, each serving a distinct purpose: purpose and framework, target audience and async requirement, and output description. It is front-loaded with key information, avoiding unnecessary 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 presence of annotations (readOnlyHint, openWorldHint, idempotentHint) and an output schema, the description provides sufficient context about the tool's purpose, usage constraints, and outputs. It covers all essential aspects for an AI agent to understand and invoke the tool correctly.

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

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

With 100% schema description coverage, the input schema documents all parameters. The description adds meaningful context by highlighting the need for async:true, which complements the schema's description of the async parameter. No parameter details are repeated, fulfilling the role of adding value beyond the schema.

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

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

The description clearly identifies the tool as an asynchronous risk assessment tool for evaluating AI model resilience against adversarial inputs, following NIST AI RMF red-teaming protocols. It targets security and compliance personas, which distinguishes it from sibling tools like jailbreak_attempt_detector and 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 states that async:true is required to avoid timeout errors, providing a clear usage directive. However, it does not mention when not to use this tool or suggest alternative tools for related tasks, which would improve guidance for an AI agent.

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

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

Annotations already indicate readOnly, openWorld, and idempotent hints. The description adds context about data source (Common Crawl) and output specifics, but does not disclose behavioral traits like data freshness or processing delays. 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 three sentences, front-loaded with the main action, and efficient. The keyword list at the end is somewhat redundant but does not 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 presence of an output schema, the description adequately covers inputs, outputs, and purpose. Minor gaps: the role of the threshold parameter is not explained in relation to fraud detection sensitivity.

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

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

Schema coverage is high (75%+), so baseline is 3. The description mentions inputs (affiliate network, date range) but does not add meaning beyond what the schema provides for the threshold parameter. No additional constraints or formats clarified.

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

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

The description clearly states the tool analyzes affiliate clickstream data to flag fraud patterns like duplicate IPs and rapid clicks. It names the specific data source (Common Crawl), target users (CMOs), inputs and outputs, and distinguishes itself from siblings like the general fraud_detector.

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

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

The description implies use for validating affiliate traffic quality but does not explicitly state when not to use it or compare with sibling tools (e.g., fraud_detector). It lacks guidance on alternatives or exclusion criteria.

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

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

Annotations already indicate readOnly, openWorld, idempotent. The description adds value by mentioning the async option to avoid timeout and specifying the return of structured risk assessment and recommendations.

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 that efficiently convey purpose, data sources, inputs, outputs, and a behavioral note. Front-loaded and no fluff.

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

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

The description covers the main aspects: data sources, inputs, outputs, and async option. Given the complexity, it is reasonably complete, though could mention limitations or output schema details.

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

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

Schema coverage is 100% with descriptions for all parameters. The description does not add new meaning beyond what the schema provides, meeting the baseline.

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

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

The description clearly states the tool's function: analyzing non-tariff trade barriers using specific data sources (WITS, UNCTAD STAT). It distinguishes from siblings by focusing on NTBs and providing barrier mapping with severity scores and mitigation strategies.

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

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

The description provides context (COO, African trade corridors) and input requirements, but does not explicitly state when not to use this tool or mention alternatives among 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?

Description adds context beyond annotations: mentions async execution to avoid timeout, data sources used. Annotations already indicate read-only, open-world, idempotent. 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?

Four sentences, no wasted words. First sentence states purpose, second describes input/output, third gives use case, fourth mentions async. Front-loaded with 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 complexity (4 params, output schema exists, many siblings), description covers rating dimensions, data sources, async option, and use case. Lacks detailed output explanation but output schema compensates.

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

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

Schema has 100% description coverage. Description mentions institutionName as input, but doesn't add new meaning beyond schema. Baseline score of 3.

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

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

Description clearly states the tool evaluates ESG compliance of African trade finance providers using specific data sources (World Bank WITS, CDP), outputs an ESG rating with breakdown, and distinguishes from siblings like supplier_esg_audit by focus on African trade finance.

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?

States ideal for due diligence on trade partners or portfolio risk assessment, and mentions async:true to avoid timeout. Lacks explicit exclusions or comparison to similar ESG tools.

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

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

Annotations already mark the tool as readOnly, openWorld, and idempotent, providing good safety disclosure. However, the description contradicts the schema by stating 'pass async:true REQUIRED' when the async parameter is optional in the schema. This misleading requirement harms transparency.

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 mostly concise with three sentences, but the directive 'pass async:true REQUIRED' is misleading and adds unnecessary emphasis. Could be improved by removing the contradiction and streamlining the async guidance.

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 trade preference analysis, the description covers the tool's purpose, data sources, and output types (duty savings, market access, compliance). It adequately complements the existing output schema, but the async confusion detracts from 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 100%, so parameter details are fully provided. The description adds useful context about data sources (WITS, OECD) not in the schema, but does not significantly elaborate on individual parameters beyond the schema. 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 analyzes AGOA and EBA trade preference arbitrage opportunities for COOs, and details the outputs (tariff comparison, duty savings, etc.). It distinguishes from siblings like africa_trade_preference_optimizer and agoa_eba_intelligence by focusing on arbitrage, though it does not explicitly name them.

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

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

The description specifies the target user (COOs evaluating export strategies) and the general context, but does not provide explicit when-to-use vs. alternatives or exclusions. Usage is implied but not thoroughly guided.

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, which cover the key behavioral traits. The description adds context about data sources and outputs but does not disclose additional behaviors such as potential latency, data freshness, or prerequisites. Given the annotation coverage, the description provides moderate 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 a single paragraph that covers purpose, inputs, outputs, data sources, and use cases, but it includes a keyword list at the end that adds redundancy. It could be more concise by focusing on the unique value proposition and omitting the keyword list, which is already covered in the description.

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

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

Given the tool has an output schema (not shown but present) and the description mentions specific outputs and data sources, the contextual information is fairly complete for a trade optimization tool. However, it does not mention the async parameter defined in the input schema, which could be relevant for long-running queries. Still, the description provides sufficient context for a reasonable understanding of the tool's functionality.

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% parameter description coverage, so each parameter is already well-documented. The description only reiterates the main inputs at a high level without adding new semantic meaning or constraints beyond what the schema provides. Therefore, the description adds no significant value beyond the schema.

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

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

The description clearly states the tool's purpose: analyzing AGOA/EBA duty savings opportunities with HS code-level trade route optimization. It specifies the inputs (origin country, destination country, HS code) and outputs (duty savings estimates, optimal trade routes, recommendations). The mention of keywords and data sources (UN Comtrade, WCO, AU) helps distinguish it from siblings like 'africa_trade_barrier_breaker' or 'agoa_eba_intelligence'.

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 by targeting a COO role and listing ideal use cases (export market evaluation, supply chain optimization, compliance analysis). However, it does not explicitly state when to use this tool over siblings, nor does it provide exclusions or alternatives. The guidance is implied but not explicit.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=true, indicating a safe, read-only operation using external data. The description confirms this by mentioning external sources (AGOA.info, EU EBA) and describing read-only checks. It adds minimal 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 of four sentences, roughly 120 words. It is concise and front-loads the main action. Every sentence adds value (purpose, differentiator, sources). Minor improvement could be structuring with bullet points for the four 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?

The tool has no output schema, and the description does not explain what the tool returns (e.g., eligibility status, opportunity list, compliance rules). Given the complexity (5 parameters, 4 modes), the agent needs to know the output format to invoke and interpret results correctly. This is a significant gap.

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

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

Schema coverage is 100%, so baseline is 3. The description does not add parameter-specific details beyond what is already in the schema (e.g., examples for hs_code and country_iso are in the schema). It provides general context but no enrichment of parameter 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's purpose: verifying AGOA/EBA/GSP eligibility, identifying export opportunities, and providing compliance rules. It uses specific verbs and resources, and includes a differentiator (39 AGOA countries + 47 LDCs EBA). Though it does not explicitly differentiate from sibling tools, the purpose is unambiguous and 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 does not provide guidance on when to use this tool versus alternatives (e.g., other trade preference tools). There are no explicit 'when to use' or 'when not to use' statements, leaving the agent to infer usage context from the description alone.

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

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

Annotations already provide readOnlyHint, openWorldHint, and idempotentHint. The description adds information about timeout avoidance via async mode, which is not covered by annotations. However, it does not disclose other behavioral traits beyond what annotations imply. The description adds marginal value, resulting in a moderate score.

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

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

The description is concise, front-loading the purpose, and efficiently conveys essential information. The inclusion of keywords adds value without excessive verbosity. Minor redundancy (e.g., 'Keywords:' list) does not detract significantly.

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 sufficient context: purpose, usage guidance, parameter hints, and async requirement. It does not need to explain return values because the output schema exists. The description is complete for the tool's complexity.

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

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

Schema description coverage is low (17%), with only the 'async' parameter having a description. The tool description lists 'incident severity, AI system type, and affected stakeholders' as inputs, providing some semantic context for these parameters. However, it does not explain all parameters or their formats, only partially compensating for the schema gap.

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

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

The description clearly states the tool generates EU AI Act incident response playbooks with regulator notification templates, specifying verb, resource, and audience. It lists inputs and outputs, making the purpose unambiguous. However, it does not explicitly differentiate from sibling tools like 'incident_response_evidence_collector' or 'ai_act_sandbox_regulatory_sandbox', so it does not achieve a top score.

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 it is 'essential for high-risk AI system breaches requiring formal EU notification' and mandates 'pass async:true REQUIRED to avoid x402 timeout.' This provides clear context for when to use the tool. It does not include explicit exclusions or alternatives, but the guidance is sufficient for the intended use case.

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

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

Annotations already declare readOnlyHint and idempotentHint, so the description adds value by specifying that the tool returns a detailed assessment with warnings, citations, and recommendations. It discloses the nature of the output without contradictions. However, it could further clarify the behavioral scope (e.g., that it does not submit actual documents).

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

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

The description is two sentences long, front-loading the core purpose and then detailing inputs and outputs. Every sentence adds value, though it could be trimmed slightly (e.g., 'legal-focused tool' is redundant given the context). Still, it is efficient 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 explains the tool's purpose, inputs, and output type. However, it omits guidance on the 'async' parameter (which is part of the schema) and does not address potential prerequisites or usage constraints. With an output schema present, the description is moderately complete but has notable gaps.

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

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

Schema coverage is 100%, so the description adds minimal meaning beyond the schema. It rephrases the required parameters ('AI system descriptions, intended use cases, and technical specifications') but does not clarify enum values, optional fields like 'async', or provide format examples. For a 100% coverage scenario, a 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: simulating EU AI Act regulatory sandbox submissions. It specifies the verb 'simulating' and the resource 'regulatory sandbox submissions', and mentions inputs and outputs. However, it does not explicitly distinguish itself from sibling tools like 'ai_act_incident_response' or 'ai_act_training_data_audit', so it falls short of a perfect score.

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 ('for simulating EU AI Act regulatory sandbox submissions') but provides no explicit guidance on when to use this tool vs. alternatives, nor any exclusions or prerequisites. The agent must infer usage from the purpose alone.

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, idempotentHint. The description adds output details (compliance score, bias metrics, regulatory warnings, source references) and async behavior. 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?

Description is concise with two sentences, front-loading the purpose. Could be more structured (e.g., bullet lists) but is 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?

Covers input, output, usage context, and async tip. Output schema exists, so return values are partially handled. Lacks mention of prerequisites or authentication, but annotations cover safety.

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

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

Schema description coverage is 50% (missing descriptions for risk_threshold and include_bias_metrics). The description adds context for risk threshold and bias metrics but does not fully compensate for the gaps.

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, specifying input types and outputs. It distinguishes itself from siblings like ai_act_incident_response by focusing on pre-deployment audit.

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

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

Mentions 'Ideal for pre-deployment risk evaluation' and advises using async to avoid timeout, but does not explicitly exclude other use cases or compare with sibling tools like ai_act_incident_response.

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 declares it returns a job_id and creates an audit, implying mutation, but annotations set readOnlyHint: true. This is a clear contradiction, misleading the agent about 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 fairly concise given the complexity, but slightly verbose with French text. Front-loaded with main purpose and key usage details.

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

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

Covers all necessary context: async nature, polling/webhook alternatives, caching, disclaimer. The description is complete for the tool's complexity and provides actionable guidance.

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

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

Schema coverage is 100% with descriptions, so baseline is 3. The tool description does not add parameter-specific semantics beyond what the schema already provides.

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

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

The description clearly states it performs a full EU AI Act audit, classifying AI systems and producing comprehensive outputs. It distinguishes itself from sibling tools like ai_governance_full_report_result by noting async submission and 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 poll with the result tool or register a webhook for callbacks, giving clear alternatives for asynchronous workflow.

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, destructiveHint. Description adds status behavior, TTL of 24h, and job_id prefix, providing additional behavioral context.

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 concise paragraph with front-loaded purpose. 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?

Output schema exists, so description doesn't need full return details. Yet it provides a comprehensive list of report components, statuses, and TTL, making it very 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 has 100% coverage with description for job_id. Description reiterates the origin of job_id but adds minimal extra 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?

Description clearly states 'Poll the result of an ai_governance_full_report_async job' with specific verb and resource. Distinguishes from sibling tool ai_governance_full_report_async which is the async launcher.

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

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

Explicitly advises to call after the eta_seconds hint (~90s). Describes statuses (pending, completed, failed, not_found) to guide polling 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?

Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds that inputs are validated server-side and returns an audited deliverable, but does not disclose additional behaviors like rate limits or authentication needs.

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

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

The description is concise with two sentences and a reference case. However, the use of French may obscure meaning for some users, and the structure could be more 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 complex input schema with nested objects and no output schema, the description lacks information about the return format and how the deliverable is structured. It does not address gaps in schema documentation or provide a complete picture for correct invocation.

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

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

Schema description coverage is only 20%, and the description does not explain any parameters or their constraints. The generic statement 'send the documented case fields' does not compensate for the lack of parameter documentation.

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 for AI governance piloting, with a reference case about high-risk AI systems. However, it does not clearly distinguish from sibling tools like ai_governance_full_report_async.

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

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

No explicit guidance on when to use this tool versus alternatives. The mention of 'agent-payable' and 'RISK' hints at cost or risk context, but no direct when-to-use or when-not-to-use advice.

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

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

Annotations include readOnlyHint=true and openWorldHint=true, so the description adds limited behavioral context beyond stating that it returns an 'audited deliverable' and that inputs are validated server-side. This is some additional value but not substantial.

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 (4 sentences) but includes cryptic jargon ('Gapup agent-payable C-suite expertise') and a reference case that may not be universally understood. It is front-loaded but could be clearer.

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 input schema and lack of output schema, the description is incomplete. It does not explain the deliverable structure, async behavior, or how to interpret results, leaving significant gaps for the agent.

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

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

The description does not add any meaning to the parameters beyond what is in the schema. With only 20% schema description coverage, the description should compensate but fails to mention fields like company, signals, or employees.

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 mentions 'Bouclier anti-démissions' and a reference case about detecting at-risk employees, which hints at HR attrition analysis. However, the purpose is vague and not clearly distinguished from sibling tools like churn_defender or talent_poaching_risk. The name 'anti_demissions_hr' is cryptic.

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 does not mention prerequisites, scenarios, or exclusions. It simply describes the tool's function and input requirements.

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, idempotentHint=true, destructiveHint=false; the description confirms read-only behavior and adds caching (24h) and the fact that private awards are inaccessible. No contradictions and useful extra detail beyond annotations.

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

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

The description is well-structured with clear sections for modes and notes. It is front-loaded with the purpose. However, it is somewhat lengthy and could be more concise without losing important context.

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

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

Given the tool's complexity (multiple modes, institutional coverage, data limitations) and the presence of an output schema, the description covers all necessary aspects: data sources, mode behavior, caching, and API requirements. It is thorough and sufficient for an agent to use 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 coverage is 100% with parameter descriptions. The description adds significant value by explaining each mode's purpose in detail, especially clause_check (audit missing elements). The async parameter is also described. This goes beyond the schema alone.

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

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

The description clearly defines the tool as 'commercial arbitration intelligence' for specific user roles, lists three distinct modes with examples, and covers 8 major institutions. It is specific and distinguishes from the many sibling tools which are unrelated to arbitration.

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

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

The description provides explicit guidance on when to use each mode (party_lookup, institution_index, clause_check) and notes limitations (most awards confidential, only public awards). Caching and API key info are included. However, it does not explicitly state when not to use the tool.

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

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

Annotations (readOnlyHint=true, openWorldHint=true) are consistent. The description adds that it returns a structured deliverable and that inputs are validated server-side. However, it doesn't discuss performance, rate limits, or what happens with async mode beyond the parameter hint.

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

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

The description is verbose, mixing French jargon with a long list of example questions. It lacks a clear, front-loaded summary. The examples are useful but make the description bloated and unstructured.

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, so the description should explain return values, but only says 'structured, audited deliverable'. With 6 parameters and multiple use cases (assets, email, M&A), the description is incomplete and does not address all dimensions adequately.

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

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

Schema description coverage is only 17%, yet the description does not explain most parameters (domain, focus, exclusions, scope_cidrs, include_email_surface). Only async gets indirect mention via example. The description 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 states it's about attack surface surveillance and returns a structured deliverable, but it lists multiple disparate questions (e.g., assets, phishing risk, M&A due diligence) without a single clear verb+resource. The title 'Surveillance surface d'attaque' helps, but the purpose is scattered across various use cases.

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

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

The description provides example queries but no explicit guidance on when to use this tool over its many siblings (e.g., cve_security_lookup, domain_tech_fingerprint). No 'when not to use' or comparison to alternative 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 set readOnlyHint=true and openWorldHint=true. The description adds minimal behavioral context beyond stating inputs are validated server-side. It does not disclose side effects, authentication needs, rate limits, or what 'audited deliverable' entails beyond the annotation hints.

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

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

The description is two sentences plus a reference case. The first sentence is dense with jargon, and the reference case, while illustrative, adds length without core functional explanation. A cleaner structure focusing on verb+resource would improve efficiency.

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 describe the output format, error handling, or how to interpret the 'audited deliverable'. The lack of detail undermines correct invocation.

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

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

Schema coverage is only 17% and the description provides no parameter explanations, despite 6 complex parameters (5 required) including nested objects. The description only mentions 'documented case fields' without detailing what fields are needed, leaving the agent without critical 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 states it performs a 'Pré-audit comptable' and returns a 'structured, audited deliverable', which clearly indicates a pre-audit assessment. The reference case adds concrete context. However, the jargon 'Gapup agent-payable C-suite expertise (CFO)' is distracting and could be clearer.

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 a reference case but no conditions, exclusions, or mentions of sibling tools like 'qa_pre_flight'. The agent has no direction on appropriate use cases.

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 and idempotent behavior. The description adds relevant context about using external data sources (World Bank, ECB SDW) and returning benchmark comparisons and levers. No contradiction with annotations.

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

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

The description is concise at ~70 words, front-loading the purpose and followed by use case and keywords. It is efficient but could be slightly more compact 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?

Given the tool has 7 parameters and an output schema, the description adequately covers the main functionality and context. It mentions benchmark comparisons and levers, which aligns with the existence of an output schema. Minor omission: no mention of async parameter handling.

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 each parameter is well-documented in the schema. The description mentions the main fee types and bank_country, but does not add significant new semantics beyond what the schema already provides.

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

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

The description clearly states the tool's purpose: analyzing bank fee structures and providing data-driven negotiation recommendations. It specifies the resource (bank fees) and distinguishes itself from siblings by being CFO-focused and using World Bank and ECB SDW benchmarks.

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

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

The description provides context for when to use the tool (optimizing treasury operations, improving financial efficiency) and implies the target user (CFOs). However, it does not explicitly state when not to use it or name alternative tools among the siblings.

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

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

Annotations already declare readOnlyHint=true, indicating a safe read operation. Description adds that inputs are validated server-side and returns an audited deliverable, but lacks details on auth needs, rate limits, or async behavior beyond the parameter.

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

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

Description is relatively short (two sentences plus reference) but mixes languages and includes jargon. Could be more structured and front-load 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?

Given 5 parameters with nested objects and no output schema, the description lacks completeness. Does not describe output structure, async polling, or parameter constraints. Inadequate for a tool of this complexity.

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

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

With only 20% schema coverage (async described), description does not compensate. Does not explain key parameters (competitor, dealContext, ourOffer) beyond 'send the documented case fields'. Adds no meaning to 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?

Description states it returns a structured, audited deliverable for competitive battle cards, with a reference case. Purpose is generally clear but muddled by jargon (e.g., 'Gapup agent-payable C-suite expertise') and French terms. Could be more direct.

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. siblings like 'competitive_deep_dive' or 'battle_plan'. Does not specify prerequisites, when not to use, or compare with 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 include readOnlyHint=true, so description adds 'audited deliverable' and 'inputs validated server-side', which are useful but don't contradict annotations. Missing details on auth requirements, rate limits, or side effects. Transparency is adequate but not enhanced 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?

Three sentences, mixed French and English, with a reference case that may be specific to a single scenario. Could be more concise by removing the example and focusing on general use. Acceptable but not optimized.

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

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

With 11 parameters, no output schema, and no description of return structure, the description is insufficient. It does not explain what the deliverable contains, how to interpret results, or the significance of the reference case. Annotations add minimal context.

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

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

Schema description coverage is only 9%, and the description does not explain any parameters beyond enumerating 'companyName' implicitly via the case. No parameter meaning or constraints are clarified, and the description 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 uses 'marketing battle plan' and 'returns a structured, audited deliverable', which indicates a strategic marketing plan generator. However, it's vague on the specific outputs and doesn't differentiate from siblings like 'bp_narratif' or 'brand_builder'. The reference case adds context but is not a clear purpose statement.

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 provides a specific reference case but no context about prerequisites, exclusions, or when not to use. Sibling tools are not mentioned or differentiated.

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 operation. The description adds that it accepts model identifiers or output samples and returns structured bias metrics, providing useful 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?

Three sentences, highly efficient, with no redundant information. Core purpose, usage context, and output are front-loaded.

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

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

With output schema present and full input schema coverage, the description adequately covers the tool's purpose and use cases. Could briefly mention async option but not critical.

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

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

Schema coverage is 100%, so baseline is 3. The description mentions accepting model identifiers and output samples, which maps to schema parameters, but does not add significant new meaning beyond the existing parameter descriptions.

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

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

Description clearly states the tool tracks bias amplification in LLM outputs using HuggingFace fairness metrics, and specifies it is for risk assessment personas. 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 indicates it is designed for risk assessment personas but does not explicitly state when not to use it or provide alternatives. Usage context is implied but not contrasted with 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 already declare readOnlyHint, openWorldHint, and idempotentHint. The description adds 'quickly assess' and 'structured compliance insights with source references' but lacks details on rate limits, authorization needs, or behavior under invalid inputs.

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

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

Two sentences, front-loaded with purpose, no superfluous words. Each sentence earns its place: first states the main action, second explains how and output.

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

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

Given the tool has 6 parameters with full schema coverage and an output schema, the description is complete enough. It covers when to use and what to expect without missing critical context.

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

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

With 100% schema description coverage, baseline is 3. The description adds value by mentioning specific ESG dimensions (carbon, governance, social impact) that help prioritize parameters like issuerSector and jurisdiction, providing contextual 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: assessing bond covenant ESG compliance against BIS and ECB standards, analyzing covenant text for risks in carbon emissions, governance, and social impact. It distinguishes from sibling 'bond_covenant_monitor' by focusing on ESG compliance and regulatory benchmarks.

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

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

The description mentions use cases: pre-issuance due diligence and ongoing monitoring. It provides clear context but does not explicitly exclude alternative use cases or name competing tools like 'bond_covenant_monitor' or other ESG checkers.

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

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint as true. The description adds useful context: it uses real-time financial data and outputs compliance status, key metrics, and SEC filing references. This goes beyond annotations by clarifying the output structure and data source. No contradictions.

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

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

The description is a single, well-structured paragraph with four sentences. It front-loads the core purpose and includes actionable keywords. Though slightly verbose, every sentence contributes meaning and there is no redundant content.

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

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

Given the tool's complexity (3 params, nested object, output schema existing), the description sufficiently covers inputs, outputs (compliance status, metrics, SEC references), and purpose. It does not need to detail return values as an output schema exists, making it contextually 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 67% (top-level params described, but nested thresholds have descriptions too). The description adds meaning by stating 'optional covenant thresholds' and explaining their role in compliance analysis. However, the schema already documents parameters well, so the description adds modest value.

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

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

The description clearly defines the tool's purpose as monitoring bond covenant compliance by analyzing leverage and interest coverage ratios. It specifies the verb (monitor/analyze) and resource (bond covenants). However, it does not differentiate from sibling tools like 'bond_covenant_esg_compliance_checker' or 'syndicated_loan_covenant_breach_alert', which overlap in functionality.

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 suggests use cases (proactive debt management, regulatory compliance tracking) but lacks explicit guidance on when not to use this tool or how it differs from alternatives. It does not provide exclusions or prerequisites, which limits its utility 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 declare readOnlyHint=true and openWorldHint=true. The description adds context about CFO expertise and a reference case, which is consistent and adds value beyond annotations.

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

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

The description is three sentences, front-loading the purpose. It is reasonably concise, though it could be shorter by omitting the 'Inputs are validated' sentence.

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 parameters and no output schema. The description does not explain the output format ('structured, audited deliverable') or provide enough context 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?

Only 25% of parameters have descriptions in the schema (async). The description does not explain the nested objects (company, raise, keyMetrics) or how to structure them, failing to compensate for 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 the tool returns a 'structured, audited deliverable' for a business plan narrative, targeting CFO-level expertise. However, it does not differentiate from similar sibling tools like 'ftg_business_plan' or 'pitch_deck_storyline'.

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

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

The description provides a reference case (Stripe Series A 2012) and mentions inputs are validated server-side, implying how to use it. But it lacks explicit guidance on when to use this tool vs alternatives or when not to use it.

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

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

Annotations already provide readOnlyHint and openWorldHint. Description adds that inputs are validated server-side and returns a structured deliverable, but lacks details on auth, rate limits, or what 'audited' means.

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 uses jargon ('Gapup agent-payable C-suite expertise') that may confuse. Front-loading is decent but lacks 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 complex nested parameters and no output schema, the description is incomplete. It doesn't explain the deliverable's content, output format, or behavior beyond returning a result.

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

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

Description provides no semantics for the 5 parameters (3 required). Schema coverage is 20%, and the description only says 'send the documented case fields', offering 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?

Description clearly states it's a 'Brand Architect' tool that returns a structured, audited deliverable, with a reference case for context. However, it doesn't explicitly differentiate from sibling tools like positioning_strategist or brand_equity_voice_share_calculator.

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 implies C-suite/CMO use but doesn't specify when to choose it over 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?

Beyond the annotations (readOnlyHint, idempotentHint, openWorldHint), the description adds details about data sources, output types, and timeout behavior with async support. This enriches the agent's understanding of tool behavior.

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

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

The description is just four sentences, front-loading the primary action, then inputs, outputs, use case, and a practical 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?

The description covers purpose, key inputs, outputs, ideal use case, and async behavior. Given the presence of an output schema, the output summary is sufficient. It could mention the async result polling mechanism, but overall it is contextually complete for a tool of moderate complexity.

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

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

With only 20% schema description coverage, the description partially compensates by listing 'brand name, competitors, and time range' as inputs. It mentions forums, hinting at the include_forums parameter, but does not explain time_range format or other details. The async parameter description is covered in 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 calculates 'brand equity voice share' using specific data sources (Common Crawl, Wayback Machine). It targets CMOs and mentions competitive benchmarking, which gives a precise purpose. However, it does not explicitly differentiate it from sibling tools like brand_builder or competitive_deep_dive.

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 notes it is 'ideal for competitive benchmarking and brand visibility tracking' and advises using async:true for timeouts. This provides context but lacks explicit guidance on when to use this tool versus alternatives, such as sentiment analysis or other brand 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=true and openWorldHint=true, which the description aligns with by noting it returns a 'structured, audited deliverable'. It adds that inputs are validated server-side, but no additional behavioral traits (e.g., rate limits, destruction) are disclosed 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 verbose with a long list of example questions and a reference case, making it harder to scan. Key details (purpose, input requirements) are buried; it could be significantly shortened while retaining 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?

For a tool with nested objects, low schema coverage, and no output schema, the description lacks comprehensiveness. It provides usage examples but does not explain the required input structure or the format of the returned deliverable.

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

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

Schema description coverage is 25% (low). The description provides no explanation of any parameters (e.g., 'entity', 'budgetContext'), only stating to 'send the documented case fields'. This fails to compensate for the lack of schema documentation.

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 budget variance analysis ('Analyse d'écart budgétaire') and provides specific example queries (e.g., top 10 narrative explanations, corrective actions, forecast scenarios). This differentiates it from siblings by focusing on financial analysis deliverable.

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

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

The description implies usage scenarios through example queries (e.g., 'Explain the key drivers') but does not explicitly state when to use or avoid this tool versus alternatives. No exclusions or competitor tool mentions 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 declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds critical behavioral context: all processing is local, no external API calls, instant response, privacy-preserving. It also details the five weighted criteria and output elements (scores, flags, interview questions), significantly expanding on 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 but dense paragraph that conveys all necessary information. It front-loads the main purpose and then provides details on criteria, output, and privacy. While structured, it could benefit from bullet points for readability, but each sentence earns its place.

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

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

Given the tool's complexity (5 parameters, nested objects, output schema), the description covers inputs, outputs, criteria, flags, and ethical warnings (diversity signals). It is complete enough for an AI agent to understand and invoke correctly, even without seeing the output schema.

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

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

Schema coverage is 100%, with each parameter described. However, the description adds meaning by explaining the five criteria (skills_match, experience_match, etc.) and how they are computed, which goes beyond the schema's descriptions. The description also clarifies the optional nature and defaults for criteria_weights.

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

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

The description specifies the verb 'screening and ranking', the resource 'candidates for a job description', and provides details on inputs (JD, 1-50 resumes) and outputs (ranked shortlist with score breakdowns). It clearly distinguishes this tool from siblings by focusing on AI-powered recruitment screening, which is unique among the listed 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 states it is for recruiters, hiring managers, ATS providers, and recruitment AI agents, and indicates when to use it (ingesting a job description and candidate resumes). It does not explicitly exclude scenarios or mention alternatives, but the context is sufficient for correct selection.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds that inputs are validated server-side and returns a structured deliverable, but does not disclose additional behavioral traits like cost or latency.

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 and front-loaded, but includes a specific reference case that may not be universally relevant. Every sentence adds some value.

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

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

Given the tool's complexity (7 parameters, nested objects, no output schema), the description is insufficient. It does not explain the output format, required data, or how the tool fits into broader workflows.

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 (14%), and the description provides no parameter specifics beyond 'send the documented case fields'. It does not compensate for the lack of schema documentation.

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 it's for capacity planning, targeting CHRO/HR roles, with a reference case. However, the use of French and 'Gapup agent-payable' jargon may obscure purpose for some agents.

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

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

No explicit guidance on when to use this tool versus alternatives. The description implies it's for HR capacity planning but does not exclude other scenarios or specify 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, which implies a safe read operation. The description notes server-side validation but adds little beyond that. No contradiction with annotations.

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

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

The description is concise with two sentences, but includes a reference case that may not be essential for an AI agent. Information is front-loaded.

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

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

The tool has complex nested inputs and no output schema. The description fails to clarify the return format or how the deliverable is structured, making it incomplete for an agent to fully grasp the tool's 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 only 20% (only 'async' parameter described). The description does not explain the meaning of any other parameters or nested fields, leaving the agent to rely solely on property names.

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

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

The description clearly states the tool's purpose: a financing strategy tool that returns a structured audited deliverable, with a reference case to illustrate. However, it does not distinguish from sibling tools like 'funding_hunter' or 'cap_table_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 this tool versus alternatives. The description only mentions that inputs are validated server-side and to send documented case fields, but does not provide context for selection.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the tool is read-only with non-deterministic output. The description adds that it returns an 'audited deliverable' and inputs are validated server-side. This provides some context but does not fully disclose behavioral traits like rate limits, authentication, or specific output structure.

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

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

The description is relatively short (two sentences) and front-loaded with the purpose. The reference case adds useful context but could be omitted or shortened. Overall, it is reasonably concise, though the mix of French and English may reduce clarity for some agents.

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, 6 parameters, 4 required, no output schema) and low schema coverage, the description is insufficient. It does not explain the output format, how to structure the nested inputs, or what 'audited deliverable' means. The reference case provides some context but leaves 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?

Schema description coverage is only 17% (only 'async' parameter described). The description does not explain any parameters, nor does it compensate for the low coverage. It says 'send the documented case fields' but does not list or clarify them, leaving the agent with insufficient guidance on how to populate required fields like company, currentCapTable, 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 indicates the tool is a cap table strategist for fundraising, returning a structured deliverable. It mentions a reference case (Aleph AI Series B) giving concrete examples of functionality. However, it is in French and does not explicitly differentiate from sibling tools like financial_model_3statement 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?

The description provides no explicit guidance on when to use this tool versus alternatives. It does not specify prerequisites, contexts, or exclusions. The only hint is the 'FUNDRAISING' tag, which implies usage, but no direct comparison 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?

Annotations already provide readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds useful details: tier-2 accuracy ±20%, output specifics (breakdown, levers, trajectory), and mentions it's delivered by a named persona. 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?

Description is 3-4 sentences, front-loaded with core function and outputs. Efficient but could omit the persona line. No 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?

Tool is complex with many parameters and output schema. Description provides high-level purpose and output but lacks guidance on parameter usage and async support. Output schema exists, so return values are covered, but parameter semantics gap makes it 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 13%, but description only vaguely mentions 'Inputs: the company profile and its activity data', failing to explain the 8 parameters including async, focus, perimeter, and nested objects. Almost no value added 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 the tool calculates GHG footprint under GHG Protocol, specifying scopes, unit, accuracy, and detailed outputs. It distinguishes from siblings by mentioning specific use cases like CSRD compliance and supplier ESG scorecards.

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 use: for CSRD compliance pre-audit, green-finance access, or supplier ESG scorecards. Does not provide exclusions or alternatives, but the context is clear.

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

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

Annotations already indicate readOnlyHint=true, so the description's mention of 'returns a structured, audited deliverable' adds minimal behavioral context. It notes server-side validation but omits error handling or output format details.

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

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

The description is short (3 sentences) and front-loaded with the name, but includes confusing jargon ('Gapup agent-payable') and lacks clarity. It could be more concise and structured.

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

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

Given the tool's complexity (8 parameters, nested objects, no output schema), the description is severely incomplete. It does not explain how to structure inputs, the return value, or handle the async option.

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

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

Schema description coverage is only 13%, yet the description adds no parameter-specific information beyond 'send the documented case fields.' It fails to compensate for the lack of schema descriptions for most 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 states it returns a 'structured, audited deliverable' for carbon roadmap, indicating a report output. However, it does not clearly differentiate from siblings like carbon_footprint_calculator and uses jargon ('Gapup agent-payable C-suite expertise') that may obscure purpose.

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

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

The description provides no guidance on when to use this tool versus alternatives. It only mentions a reference case but no context for selection 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 indicate readOnlyHint=true and openWorldHint=true. The description adds that the tool returns a 'structured, audited deliverable' and inputs are validated server-side. No behavioral contradictions; it clearly indicates a non-destructive operation.

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

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

Three sentences: purpose, reference case, instruction. Front-loaded and efficient, but the reference case example could be shortened or separated 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 the complexity (nested objects, 4 parameters, no output schema), the description is adequate but incomplete. It provides an example and states the deliverable is structured and audited, but lacks details on output format and field-level semantics.

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 very low (25%), only the 'async' parameter has a description. The description does not explain the other parameters (deal, knownContacts, sellerContext) beyond a vague reference to 'send the documented case fields', failing to add 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 it's about 'Cartographie du champion' (champion mapping) for C-suite expertise (CRO) and returns a structured deliverable. The reference case clarifies the output, but the scope is not fully differentiated from similar tools like deal_coach.

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 a reference case and instructs to 'send the documented case fields', implying use when you have deal data, but does not explicitly state when to use this tool instead of siblings like battle_cards_live or win_loss_decoder.

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, and idempotentHint, which the description complements by explaining data sources (GitHub, Snyk) and output format (confidence scores). There is no contradiction; the description adds 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?

The description is three sentences, front-loading purpose, then data sources, then input/output and use case. No unnecessary words; every sentence adds value.

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

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

With an output schema present, the description does not need to detail return values. It covers purpose, inputs, data sources, and ideal use case. However, it omits prerequisites (e.g., GitHub/Snyk access) and does not explain the async pattern or all parameters fully.

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

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

Schema description coverage is only 20% (async has a description). The description adds meaning for pr_url and incident_id by stating they are inputs, but does not explain snyk_org_id or time_range_days. It partially compensates for the low coverage but is not comprehensive.

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

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

The description clearly states the tool classifies root causes of change failures for CTO-level incident analysis. It specifies inputs (GitHub PR URL or incident ID), data sources (GitHub PR metadata and Snyk vulnerability data), and outputs (structured root cause categories with confidence scores). This distinguishes it from siblings like dependency_vulnerability_scan by focusing on root cause categorization for post-mortem analysis.

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

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

The description mentions 'Ideal for post-mortem analysis and change risk assessment' but does not explicitly state when not to use this tool or suggest alternatives. No comparison to sibling tools is provided, so the agent lacks clear boundaries for selection.

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

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

The description adds significant behavioral context beyond annotations: it notes that data is live, explains proxy dependencies, describes what each mode returns, and mentions data quality considerations. No contradiction with annotations (readOnlyHint=true, destructiveHint=false).

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 a clear header, bulleted mode list, data quality note, and input format examples. It is comprehensive but not verbose, with every sentence serving a purpose. The most important information (purpose, modes) is front-loaded.

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

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

Given the tool's complexity (5 modes, 5 parameters, proxy requirements, multiple platforms) and the presence of an output schema (mentioned in context), the description is complete. It covers all modes, input formats, data quality, and preconditions, leaving no major gaps for an agent to understand correct invocation.

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

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

The input schema has 100% coverage with descriptions, but the description greatly expands on parameter meaning. It explains the five modes in detail, provides input format examples for query (e.g., 'platform:id' for seller_profile), and clarifies platform and region behavior. This adds substantial value beyond the schema.

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

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

The description clearly states the tool's purpose as Chinese e-commerce intelligence, lists supported platforms and five distinct modes (product_search, seller_profile, price_history, brand_monitoring, market_intel). It specifies the target audience and differentiates from sibling tools by focusing on specific Chinese platforms.

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

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

The description identifies intended users (ZH diaspora, import-export teams, etc.) and provides context on when to use each mode. It includes a data quality note about proxy requirements for certain platforms and fallback behavior. However, it does not explicitly state when not to use this tool or name alternatives among siblings.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. Description adds context on data sources (Yahoo Finance, Eastmoney, HKEX) and critical access constraint that Eastmoney/CSRC/SSE are blocked from datacenter IPs without proxy. This goes beyond annotations.

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

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

Description is well-structured with bullet lists for modes, input formats, and data sources. Front-loaded with core purpose. Every sentence adds value, though slightly lengthy. 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 complexity (5 parameters, 4 modes, multiple data sources) and presence of output schema, description covers modes, inputs, sources, and access restrictions comprehensively. No major gaps for agent use.

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

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

Schema covers all 5 parameters with 100% coverage. Description elaborates on mode parameter with detailed outputs (e.g., company profile includes USCC, registered capital) and input format examples (e.g., '600519' for Moutai). Adds meaningful context beyond enum labels.

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

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

Clearly states tool provides Chinese capital market intelligence across four specific modes (company, market_quote, sector_overview, regulatory_filing) covering A-Shares, H-Shares, and ADRs. Distinct from sibling tools like india_market_data by explicit geographic 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?

Description details four modes and accepted input formats, implicitly guiding usage. Does not explicitly state when to use alternatives or exclude certain cases. Lacks explicit when-to-use vs. when-not-to-use guidance.

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

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

Annotations include readOnlyHint=true, indicating no side effects. The description adds that the tool returns an audited deliverable, but does not detail what the deliverable contains or any processing behavior beyond input validation.

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 uses jargon ('Bouclier', 'CRO', 'Gapup agent-payable') and includes a reference case that may not be helpful to the agent. The key purpose is not front-loaded clearly.

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

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

Given the complexity of nested objects and 5 parameters, the description fails to explain the return value, the structure of the deliverable, or how to interpret results. It is incomplete for effective use.

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

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

Schema description coverage is only 20%, yet the description provides no explanation of parameters beyond 'send the documented case fields'. No parameter details are given, leaving the agent to rely entirely on 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 mentions 'anti-churn' and 'returns a structured, audited deliverable', but it lacks a clear verb indicating the exact action (e.g., 'analyze churn risk'). It does not differentiate from sibling tools like upsell_hunter or renewal_optimizer.

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

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

No guidance on when to use this tool over alternatives. The description only says to send documented case fields, without providing context for proper invocation 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 discloses read-only nature (projections), cache behavior (7 days), SLA (≤20s), async option, and data sources. It adds detail beyond the annotations (readOnlyHint, destructiveHint), which already indicate safety. No contradiction with annotations.

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

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

The description is a single dense paragraph that front-loads the core purpose. It is concise for the amount of information conveyed, though could be structured with bullet points for improved readability.

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

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

Given the tool's complexity, the description covers purpose, scenarios, metrics, outputs, sources, usage contexts, caching, SLA, and async behavior. With full schema coverage and output schema present, it is complete for effective agent use.

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

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

Schema coverage is 100% with descriptions for all parameters. The description adds context by explaining scenarios and metrics in narrative form, but the schema already covers the semantics. The description enhances understanding but does not add critical missing info.

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 by IPCC scenario for any location. It specifies scenarios, horizons, metrics, outputs, and sources, distinguishing it from sibling tools which are unrelated to climate projections.

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

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

The description lists explicit use cases (TCFD/CSRD physical risk, due diligence, insurance, infrastructure planning) and mentions caching and SLA. It does not explicitly state when not to use it or list alternatives, but the use cases are clear and sufficient for an agent.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds that the tool returns a 'structured, audited deliverable' and mentions data sources like FAERS and trial data. This is consistent but adds only moderate 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?

The description is overly long and cluttered with multiple example questions and a reference case. It lacks clear structure; the information is packed into a single block of text. Not concise, and the front-loading is confusing (jargon like 'Gapup agent-payable C-suite expertise (RISK)').

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 8 parameters, no output schema, and no nested objects, the description should provide more guidance on input usage and return format. It gives examples but does not explain parameter semantics or expected output structure. Incomplete for an effective 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 only 13%, yet the description does not explain any of the 8 parameters. It lists example questions but does not map them to parameters like topic, max_studies, evidence_focus, or date_range_years. The description fails to compensate for the low schema coverage.

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

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

The description clearly states that the tool provides clinical evidence briefs with GRADE ratings, key trials, and safety signals. It gives specific verb-resource pairs like 'Review the clinical evidence for <drug/intervention> in <indication>' and 'Scan safety signals for <molecule> in <population>'. However, it does not differentiate from sibling tools like sci_literature_search or clinical_pharma_intel, which may have similar 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 example questions but no explicit guidance on when to use this tool versus alternatives. It does not state conditions when not to use or mention any prerequisites. The examples are generic and do not help an agent decide between this and sibling tools.

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

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

The description goes beyond annotations by disclosing important behavioral traits: all sources are public and keyless, an optional API key increases quota, SLA ≤16s p95, and cache durations per mode (6h trials, 24h approvals, etc.). No contradictions with annotations (readOnlyHint=true, openWorldHint=true).

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, starting with purpose, then listing modes with bullet points, then signal detection, then technical details. Every sentence adds value. It is appropriately sized for the tool's complexity (7 parameters, multiple modes). No redundant phrasing.

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 high complexity (7 parameters, 100% schema coverage, output schema present), the description is complete. It covers target users, modes, signal detection, data sources, performance (SLA), caching, and optional API key. No gaps in context for effective tool invocation.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds value by explaining the modes in detail, including signal detection logic (P0/P1/P2) and the scope of each mode (e.g., 'trials — active/completed clinical trials'). This enriches what the schema alone 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's purpose: providing clinical and pharmaceutical intelligence for specific user groups (biotech analysts, healthcare fund managers, etc.). It lists five distinct modes (trials, pipeline, approvals, recalls, adverse_events) and explicitly describes what each mode does. The scope is well-defined and distinguishes this tool from sibling tools which cover diverse domains.

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