gapup-mcp

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

Annotations only provide openWorldHint, but the description does not clarify behavior beyond 'returns a deliverable'. It does not disclose side effects, authentication needs, or what the 'audited deliverable' entails.

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

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

The description is concise (three sentences) and front-loaded with the main purpose, though the reference case adds some noise. It could omit the example and still be clear enough.

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

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

Given 8 undocumented parameters, no output schema, and sparse annotations, the description fails to provide sufficient context for correct invocation. The deliverable format and input requirements are unclear.

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 13% schema description coverage; the description does not explain any of the 8 parameters except async. It vaguely says 'send the documented case fields' without specifying what each field means or how to structure them.

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

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

The description states it is for ABM architecture targeting CMO and returns a structured deliverable, but it is vague and does not distinguish from siblings like 'customer_marketing' or 'marketing_roi_dashboard'. The reference case adds context but not clarity.

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

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

No guidance on when to use this tool versus alternatives. The description lacks context for selection among many marketing-related 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 only include openWorldHint: true. Description adds that inputs are validated server-side and returns an audited deliverable. Does not disclose auth needs, rate limits, or destructive behavior. Some value added but insufficient.

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

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

Description is three sentences but mixes languages and includes a specific reference case that adds length without immediately clarifying purpose. It is not extremely concise.

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

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

Tool has 5 parameters with nested objects and no output schema. Description lacks parameter meanings, return format, example usage, and completeness for an agent to correctly invoke it.

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

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

Schema coverage is 20% (only 'async' described). Description mentions 'documented case fields' but does not explain 'focus', 'company', 'accounts', or 'ownership'. Fails to compensate for low coverage.

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

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

The description states it performs account expansion mapping and returns a structured deliverable, referencing a specific case. It is clear about the general domain but could be more precise about the exact output format and scope. It somewhat distinguishes from siblings like 'upsell_hunter' but not explicitly.

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 'C-suite expertise (CRO)' hints at use case but does not provide clear context or exclusions.

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

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

The description does not add any behavioral information beyond the openWorldHint annotation. It does not disclose whether the tool is read-only, has side effects, or requires specific permissions.

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

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

The description is concise (3 sentences) and front-loads the purpose. However, it could be more structured by clearly listing input requirements or output format.

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 including nested objects) and lack of output schema, the description is insufficient. It does not explain required fields, output structure, or how to use the deliverable.

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

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

With only 13% schema description coverage, the description does little to explain the parameters. It mentions validated inputs and a reference case but does not describe the meaning or usage of 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 clearly states that the tool generates a structured ESG action plan deliverable, mentioning a reference case for context. However, it does not differentiate this tool from sibling ESG tools like 'esg_audit_multi' or 'sustainability_report'.

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

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

There is no explicit guidance on when to use this tool versus alternatives. The mention of 'Gapup agent-payable C-suite expertise' is vague, and no comparison to other ESG tools is provided.

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

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

No annotations exist, so the description must disclose behaviors. It mentions sources and encoding but omits performance, data freshness, error handling, or required permissions. Insufficient for a tool with multiple modes.

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

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

The description is concise (4 sentences) and front-loaded with core functions. It efficiently adds differentiator and sources without redundancy. Could be slightly more structured for 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 5 parameters and no output schema, the description provides a good overview of the tool's modes and purpose but lacks detail on return values or what each mode produces. Adequate but not comprehensive.

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

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

Schema coverage is 100%, so parameters are well-defined. The description adds contextual value (e.g., sources) but does not enhance per-parameter understanding beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly specifies the tool's function: preferential intelligence for AGOA and EBA/GSP programs. It details capabilities (country eligibility, product eligibility, trade opportunities, compliance rules) and differentiates with 39 AGOA + 47 LDCs encoding. This distinguishes it from sibling tools focused on other regions.

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

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

The description implies when to use (e.g., checking African trade preferences) but lacks explicit guidance on when not to use or alternatives. No comparisons with other trade tools are provided.

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

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

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

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

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

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

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

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

Given the output schema existence and full parameter descriptions, the description provides a comprehensive overview of the async workflow, caching, and recommendations. Missing idempotency details but otherwise complete for a complex async tool.

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

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

The input schema covers all parameters with descriptions (100% coverage), so the description adds minimal semantic value. It mentions required fields but does not explain their purpose beyond the schema.

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

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

The description clearly states the tool performs a full EU AI Act audit, classifying risk tiers and producing 7 specific outputs. It distinguishes from sibling tools by mentioning async submission and webhook usage, and references the ai_governance_full_report_result poller.

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

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

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

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

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

Adds significant behavioral context beyond annotations: enumerates all possible statuses (pending, completed with report details, failed, not_found), the TTL, and the timing hint. Annotations already declare readOnly and idempotent, so the description complements them well without contradiction.

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

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

Two sentences, front-loaded with purpose, succinctly covering return values and timing. Every sentence adds value, no fluff.

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

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

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

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

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

Input schema covers the sole parameter with a clear description (job_id returned by async tool, prefix aigfr_). The tool description adds no further meaning; schema coverage is 100%, so baseline 3 is appropriate.

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

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

The description clearly states 'Poll the result of an ai_governance_full_report_async job,' using a specific verb and resource. It distinguishes itself from the sibling async initiation tool by naming it explicitly and describing the polling nature.

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

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

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

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

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

The description mentions returning a 'structured, audited deliverable' and 'inputs are validated server-side', but does not disclose whether the tool is read-only, any mutation effects, authentication requirements, or rate limits. Annotations lack readOnlyHint, so the description should have provided this but did not.

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 and front-loaded with a French phrase, followed by an English explanation. It includes a reference case for context. While it could be more efficient, it is not overly verbose.

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

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

Given the tool has 5 parameters (including complex nested objects) and no output schema, the description is insufficient. It does not specify what each parameter should contain, expected outputs, or how to interpret results. The reference case provides some context but not enough for complete understanding.

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

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

Input schema has 5 parameters with only 'async' described (20% coverage). The description says 'send the documented case fields' but does not explain the other parameters (focus, company, aiUseCases, targetFrameworks). No additional semantic guidance is provided beyond what the schema already offers.

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

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

The description indicates the tool is about AI governance piloting, referencing a specific high-risk use case under EU AI Act. However, it lacks specificity on what the output contains beyond 'structured, audited deliverable' and does not clearly differentiate 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 sibling tools include more comprehensive report tools, but the description does not explain the differences or provide selection 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 include openWorldHint=true, which already indicates unspecified behaviors. The description adds no behavioral context beyond stating the output is a structured audited deliverable. It does not explain side effects, data handling, or auth requirements.

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

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

The description is three sentences but includes cryptic jargon and a vague reference case. It lacks front-loading of core purpose and could be much more concise and clear.

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

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

Given the tool's complexity (5 params, nested objects, no output schema, open-world annotation), the description is insufficient. It does not explain data models, usage patterns, or how the deliverable is structured, making it hard for an agent to invoke 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?

Of 5 parameters, only 'async' has a description in the schema (20% coverage). The description does not explain 'focus', 'company', 'signals', or 'employees', leaving the agent to guess their meaning and required format.

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 vaguely indicates the tool detects at-risk employees and returns a structured deliverable, but uses jargon ('Bouclier anti-démissions', 'Gapup agent-payable C-suite expertise') that obscures the purpose. The reference case provides context but the core action is not clearly stated.

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 siblings like 'churn_defender' or 'talent_intelligence'. The description only mentions server-side validation and sending case fields, but does not specify prerequisites or 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?

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

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

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

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

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

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

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

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

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

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

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

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

The description clearly states the tool's purpose: commercial arbitration intelligence with three specific modes (party_lookup, institution_index, clause_check). It distinguishes itself from sibling tools by focusing on arbitration awards and clause auditing, and no sibling tool duplicates this function.

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

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

The description provides clear guidance on when to use each mode and notes that most awards are confidential, only public ones are available. It also mentions cache duration and no API key needed. However, it does not explicitly state when not to use the tool or suggest alternatives.

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

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

Annotations only include openWorldHint: true, so the description must carry the burden. It mentions 'Returns a structured, audited deliverable' and 'Inputs are validated server-side,' but fails to disclose key behavioral traits: whether the tool is read-only, if it has side effects, requires authentication, or incurs costs (hinted by 'agent-payable'). No statement about idempotency or rate limits.

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

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

The description is a dense paragraph with a bullet list of questions and a case reference, making it hard to parse. It lacks structure (no sections, no parameter explanations). The examples are useful but could be shortened; the case reference adds noise without clear value.

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

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

Given the tool has 6 parameters, no output schema, and a broad scope, the description is insufficient. It does not explain output format, error handling, or how to interpret results. The 'documented case fields' reference is vague. The tool likely returns a report, but details are missing.

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

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

Schema coverage is only 17% (only async has a description). The description does not elaborate on the purpose of parameters like focus, domain, exclusions, scope_cidrs, or include_email_surface. Without this, the agent cannot understand how to use these parameters effectively, especially the domain and focus fields.

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 attack surface surveillance and returns a structured, audited deliverable. It lists specific example questions (e.g., assets with critical CVE, email phishing risk) that clarify scope. However, it does not explicitly differentiate from siblings like cyber_risk_auditor or cve_security_lookup, though the examples give some implicit distinction.

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

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

The description provides numerous example questions that imply when to use the tool (e.g., for ASM reports, email security assessment). However, it lacks explicit guidance on when not to use it, alternatives, or prerequisites. The phrase 'Inputs are validated server-side' is a minor guideline but insufficient.

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 openWorldHint: true, suggesting potential side effects or dynamic behavior, but the description does not elaborate. It mentions server-side validation and a structured deliverable but fails to disclose important behaviors like whether changes are persisted, rate limits, or auth requirements. Beyond the annotation, the description adds minimal 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?

The description is relatively short (two sentences plus a reference case), which is concise. However, it mixes French and English, and the reference case may be distracting. The structure is front-loaded with the core purpose but lacks necessary detail. It earns a 3 because it is not verbose but sacrifices completeness.

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

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

Given the complexity (6 parameters, nested objects, no output schema, open world hint), the description is woefully incomplete. It does not explain what the deliverable contains, how results are returned, or how to configure the nested objects. The reference case provides some context but not enough for reliable 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?

With only 17% schema description coverage, the description provides no meaningful detail about the six input parameters. The schema itself lacks descriptions for most fields (e.g., audit, company, systems). The description merely says "send the documented case fields," which is too vague to help the agent understand parameter semantics.

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 it is a pre-audit tool for accounting ("Pré-audit comptable") and returns a structured audited deliverable. It references a concrete case, which adds clarity. However, it does not explicitly differentiate from sibling tools like qa_pre_flight, but the purpose is clear enough to distinguish from most unrelated 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 lacks guidance on when to use this tool versus alternatives. It only instructs to "send the documented case fields" without explaining the context or prerequisites. No comparisons to sibling tools are provided, leaving the agent without decision support.

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

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

Annotations only include openWorldHint, no readOnly/destructive hints. Description adds that inputs are validated server-side and returns an audited deliverable, but does not disclose side effects, auth needs, or response structure. Adequate but not detailed.

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?

Short description but uses mixed languages (French/English) and jargon. Not front-loaded: key information about returns appears mid-description. 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 5 parameters with nested objects and no output schema, the description lacks detail on input formats, return structure, and behavior. Does not address the complexity, especially alongside many sibling tools. 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 coverage is 20% (only 'async' documented). Description does not explain 'ourOffer', 'competitor', 'dealContext', or 'knownWeaknesses' beyond vague 'documented case fields'. Fails 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 states it returns a 'structured, audited deliverable' for a 'Fiche de combat live' (battle card), which suggests competitive analysis. However, the mix of French and jargon obscures the exact action, and it fails to distinguish from sibling tools like competitive_deep_dive or 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?

No explicit guidance on when to use this tool versus alternatives. With many siblings, the description should provide cues, but only says 'send the documented case fields'. No when-not-to-use or alternatives mentioned.

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

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

Annotations include openWorldHint:true, indicating possible external calls, but the description does not clarify any side effects, authentication needs, or data handling. It only states 'Inputs are validated server-side,' which is minimal.

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

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

The description is relatively short (4 sentences) but mixes languages (French and English) and includes specific example details that may not be generalizable. Lacks clear front-loading of essential 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 11 parameters, no output schema, and complex semantics, the description is insufficient. It does not specify output format, how return values are structured, or how the plan is generated, leaving the agent underinformed.

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

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

Schema description coverage is only 9% (only 'async' has a description). The description covers none of the 11 parameters individually, just advising to 'send the documented case fields' without detailing field semantics.

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 marketing planning, but the French phrasing and reference case make it somewhat unclear. It does not distinguish this tool from many sibling strategy tools like 'market_entry_strategist' 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 provides a single reference case (Gapup Hub Q3 2026) but no explicit guidance on when to use this tool versus alternatives. No when-not conditions or alternative tool mentions are 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 only include openWorldHint, and the description adds little behavioral context. It mentions server-side validation and return of a deliverable, but lacks details on read-only/destructive nature, rate limits, or async handling (though async parameter is documented in schema).

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

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

The description is relatively concise (3 sentences, 64 words) but contains some less essential details (e.g., reference case). It could be more front-loaded with the core function.

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 large set of sibling tools (200+), the description fails to differentiate bp_narratif. No output schema exists, and the description does not specify what the structured deliverable contains. The tool has 4 parameters with nested objects, yet the description provides insufficient context 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 low (25%), with only the async parameter described. The description does not elaborate on the other three parameters (raise, company, keyMetrics), offering no additional meaning beyond the schema field 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 mentions a 'Business Plan narratif' and 'structured, audited deliverable' with a reference to Stripe Series A, but the purpose remains vague. It doesn't clearly state what the tool does beyond generating a business plan narrative, and sibling differentiation is absent.

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

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

No guidance is provided on when to use this tool versus alternatives. The description only instructs to 'send the documented case fields' without explaining the context or prerequisites.

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

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

With minimal annotations (only openWorldHint), the description should disclose behavioral traits. It mentions validation and async support but lacks details on side effects, authorization needs, or output nature.

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

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

The description is brief, three sentences, and efficiently conveys the tool's essence. The reference case adds context without excessive length.

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

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

Given 5 nested object parameters and no output schema, the description is incomplete. It fails to explain input structure or return format, and does not relate to sibling tools.

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

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

Schema description coverage is only 20% (only async is described). The description does not clarify meaning of brand, target, founder, existingAssets, leaving significant ambiguity.

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

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

The description states it is a brand architect providing C-suite expertise and returns a structured deliverable, with a reference case. This makes the purpose clear but does not distinguish from sibling tools like positioning_strategist.

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

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

No guidance on when to use this tool versus alternatives. It only mentions that inputs are validated server-side, but does not provide use cases 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 reveals that inputs are validated server-side and that the output is a structured audit deliverable. It also hints at AI-driven analysis. However, beyond the openWorldHint annotation, no additional behavioral traits (e.g., side effects, rate limits) are disclosed, leaving agents with gaps.

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

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

The description is lengthy, containing a block of example questions that could be condensed or moved to a separate documentation. The core purpose is stated at the beginning, but the overall verbosity reduces quick 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 has 4 parameters, no output schema, and an openWorldHint, the description should provide more guidance on expected input structure (beyond 'documented case fields') and output format. It lacks completeness for an AI-driven analytical 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 25% (only async is documented). The description does not explain the other three parameters (focus, entity, budgetContext) or mention their relationship to the 'documented case fields'. This forces agents to guess or rely on schema defaults.

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 and returns structured, audited deliverables. Multiple example queries further clarify its purpose. However, it lacks a concise single-sentence summary that distinguishes it from similar finance tools.

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

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

The description provides example questions that imply use cases but does not explicitly state when to use this tool versus siblings (e.g., margin_doctor, financial_model_3statement). There is no guidance on when not to use it or what prerequisites are needed.

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

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

The description goes far beyond annotations (readOnlyHint=true, idempotentHint=true, destructiveHint=false) by detailing local processing, no external API calls, instant response, criteria weights, diversity signals with ethical warning, red/green flags, and interview questions. It is fully transparent with no contradictions.

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

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

The description is dense but well-structured, front-loading purpose, then listing criteria, output details, and warnings. Every sentence adds value without redundancy.

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

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

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

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

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

Schema description coverage is 100%, so the schema already documents all 5 parameters clearly. The description adds context about criteria weights and output fields but does not significantly enhance parameter understanding beyond what the schema provides.

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

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

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

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

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

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

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

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

Annotations only include openWorldHint, no readOnly or destructive hints. The description mentions 'returns a structured, audited deliverable' and 'inputs validated server-side' but does not disclose whether the tool is read-only, if it has side effects, or any rate limits. The async parameter is not mentioned in the description.

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

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

The description is short but mixes French and English, and uses jargon ('Gapup agent-payable C-suite expertise') that may not be universally clear. It could be more concise and front-loaded with key information.

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

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

Given the complexity (7 parameters, nested objects, no output schema, minimal annotations), the description is insufficient. It does not explain the expected input format, output structure, or how to construct a valid request.

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 1 of 7 parameters (async) has a schema description. The description does not explain the meaning or usage of company, benchmarks, financials, constraints, currentTeam, or hiringBudgetEur. It only says 'send the documented case fields', which is vague and does not compensate for the low schema coverage.

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

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

The description clearly states it performs capacity planning for HR/CHRO, returning a structured audited deliverable, with a specific reference case. This distinguishes it from sibling tools like talent_intelligence or recruiting_architect.

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

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

The description gives a reference case example but does not explicitly state when to use this tool versus alternatives. No exclusion criteria or when-not-to guidance is provided.

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

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

Annotations only include openWorldHint=true. The description mentions server-side validation and returns a structured deliverable, but does not disclose side effects, latency, auth needs, or what additional properties are allowed under openWorldHint.

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

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

The description is short but mixes French and English, and is not well-structured. It conveys minimal information but could be more organized and front-load key 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?

Given 5 complex object parameters, no output schema, and minimal annotations, the description is incomplete. It does not explain what constitutes valid inputs, what the output format is, or how to interpret the deliverable.

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

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

Schema description coverage is only 20% (only async parameter described). The description does not explain the purpose or content of the four object parameters (company, growthPlan, financialPosition, founderConstraints), relying on vague 'documented case fields' without specifying.

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

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

The description states it is a financing strategy tool that returns a structured, audited deliverable, and provides a reference case. However, it is not specific about the exact output or how it differentiates from similar tools like funding_hunter, ma_deal_screener, etc.

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

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

There is no guidance on when to use this tool versus alternatives, nor any exclusions or prerequisites. The description only hints at its use via a reference case but lacks explicit context.

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

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

Annotations only provide openWorldHint, so the description carries the burden. It states inputs are validated server-side and returns a structured deliverable, but does not disclose error handling, rate limits, or side effects. Adequate but not comprehensive.

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

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

The description is brief and front-loaded, but could be more structured. No unnecessary words, though the French/English mix might reduce clarity for some users.

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

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

Given the tool's complexity (6 params, nested objects, no output schema), the description lacks detail on return format, scenario inputs, and parameter interactions. 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 coverage is only 17% (only 'async' described). The description does not explain individual parameters beyond a high-level reference case. It 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 indicates the tool is for cap table strategy related to fundraising, mentioning 'FUNDRAISING' and a reference case. However, it does not differentiate from the sibling tool 'capital_strategy', which could cause confusion.

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

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

The description lacks explicit guidance on when to use this tool versus siblings or when not to use it. No prerequisites or alternative tools are mentioned.

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

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

Annotations already indicate readOnlyHint=true, idempotentHint=true, and destructiveHint=false, making the tool non-mutating and safe. The description adds behavioral context: accuracy ±20%, tier-2, and returns breakdown, hotspot, levers, trajectory, and CSRD readiness. It also mentions that if async=true, it returns a job_id. No contradiction with annotations.

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

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

The description is relatively long but well-structured, front-loading the core function and deliverables, then providing usage guidance and inputs. Every sentence adds value without redundancy. However, it could be slightly more concise by removing the 'Delivered by Émilie' line, which is not essential for tool selection.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Minimal disclosure beyond annotations. openWorldHint is set, but description adds little about behavior, side effects, or return format. No mention of async behavior despite having an async 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?

Three sentences; clear but uses unnecessary French terms. Could be more concise but not overly verbose.

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

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

Insufficient for a tool with many parameters and relations to many siblings. Lacks details on required inputs, expected output, and integration with other tools.

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

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

Only 13% of parameters have schema descriptions. The description does not elaborate on any parameter, leaving the agent to interpret undocumented fields like focus, company, perimeter, 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 states it returns a structured audited deliverable for sustainability, but uses ambiguous French terms and does not differentiate from sibling tools like carbon_footprint_calculator or sustainability_report.

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 lacks context about specific situations or prerequisites.

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

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

Annotations provide openWorldHint but no destructive/read-only hints. Description does not disclose side effects, auth requirements, or what 'structured, audited deliverable' entails. With sparse annotations, the description should provide more behavioral context but fails to do so.

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 contains mixed-language content and jargon, making it confusing. It is not front-loaded effectively and does not earn its place with clear, actionable information.

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

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

Given the tool's complexity (4 params, no output schema, many siblings), the description is severely incomplete. It omits output format, input structure, and usage instructions, leaving a critical gap 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 coverage is only 25% and parameter descriptions are minimal. The description says 'send the documented case fields' but does not enumerate or explain the fields, leaving the agent with no clarity on input requirements.

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 is vague: 'Cartographie du champion' (French) and jargon 'Gapup agent-payable C-suite expertise (CRO)' do not clearly state the tool's function. It mentions returning a structured deliverable but lacks a straightforward verb-resource description.

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 versus alternatives. The reference case (Spendesk × Decathlon) is tangential and does not clarify usage context or exclusions.

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

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

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds behavioral details: live data requirement, proxy dependency, async mode (job_id), and data quality notes. No contradictions.

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

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

The description is front-loaded with purpose and well-structured into overview, mode list, and notes. While somewhat lengthy, every section adds necessary context. Could be slightly more concise, but appropriate given complexity.

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

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

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

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

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

Schema description coverage is 100%, but the description adds valuable context beyond schema: explains the five modes in detail, provides input format examples for seller_profile and price_history, and clarifies the async flag. This enriches the schema descriptions.

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

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

The description clearly states the tool provides Chinese e-commerce intelligence and enumerates five distinct modes (product_search, seller_profile, price_history, brand_monitoring, market_intel), which sets it apart from siblings like 'china_market_data'. The verb+resource is specific.

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

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

The description identifies target users (ZH diaspora, import-export teams, brand IP enforcement, etc.) and specifies when to use each mode. It also notes proxy requirements for some platforms. However, it does not explicitly compare to sibling tools or state when not to use it.

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

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

Beyond annotations (readOnlyHint, openWorldHint), the description details data source dependencies (Yahoo Finance, Eastmoney via proxy) and async behavior (returns job_id when async=true). This adds valuable behavioral context not captured in annotations.

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

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

The description is well-structured with clear sections and bullet points, front-loading the core purpose. It is somewhat lengthy but efficient given the tool's complexity. Every sentence adds value; no redundancy.

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

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

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

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

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

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

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

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

The description clearly states the tool provides Chinese capital market intelligence, specifies four modes (company, market_quote, sector_overview, regulatory_filing), and distinguishes it from siblings like india_market_data by geographic focus. The verb 'covers' and resource detail are specific.

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

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

The description provides context on when to use different modes and input formats, but does not explicitly guide when not to use this tool versus alternatives (e.g., india_market_data, sec_filing_decoder). No exclusion criteria or direct comparisons to other tools are provided.

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

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

Annotations only include openWorldHint: true. Description does not reveal whether tool is read-only, destructive, or has side effects. No mention of authentication or costs despite hint of agent-payable.

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

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

Description is short but includes French and a reference case. Could be more structured with clearer action-verb first. Not overly verbose but somewhat fragmented.

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, description does not explain deliverable contents or how to interpret result. For a tool with 5 parameters and no output schema, more detail is needed.

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

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

Only 20% of parameters described in schema (async). Description does not explain parameters like company, accounts, csrContext, analysisWindowDays. 'Case fields' is vague, failing to compensate low coverage.

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

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

Description uses 'Bouclier anti-churn' and mentions a reference case, indicating churn detection. It states returns a structured deliverable. However, it does not explicitly differentiate from siblings like save_plays 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 versus alternatives. No prerequisites or context provided beyond 'send the documented case fields.'

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

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

The description adds significant behavioral context beyond annotations: async capability, cache duration (7j), SLA (≤20s), and details on multi-scenario comparison, probability, and business impact signals. No contradiction with annotations.

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

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

The description is a single dense paragraph covering all key aspects without redundancy. It could be slightly more structured with bullet points, but it is efficient and front-loaded.

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

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

Given the tool's complexity (multiple scenarios, metrics, a nested location object), the description is very complete. It explains output types, sources, and use cases, and notes that an output schema exists. The description fully compensates for the lack of separate output schema text.

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

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

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

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

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

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

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

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

The description explicitly lists use cases (TCFD/CSRD physical risk, due diligence, etc.), giving clear context. However, it does not explicitly state when not to use or mention alternative tools, but the specificity makes usage clear.

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

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

Annotations only specify openWorldHint=true, indicating the tool may create new entities. The description mentions 'returns a structured, audited deliverable' and 'Inputs are validated server-side', but does not clarify if the tool has side effects, modifies data, or requires specific permissions. 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 verbose and poorly structured, mixing a French title, a list of questions, a reference case, and server-side notes. It lacks clear sectioning or front-loading of key information, and contains repetitive elements.

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

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

Given 8 unrequired parameters, no output schema, and numerous sibling tools, the description is incomplete. It does not describe the output format, how to use async effectively, or the relationship between parameters. The reference case helps but is insufficient for comprehensive understanding.

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

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

With only 13% schema description coverage (only 'async' documented), the description fails to explain the other 7 parameters (topic, max_studies, intervention, etc.). While the example questions hint at usage, they do not formally define parameter meaning or constraints, leaving the agent under-informed.

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

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

The description clearly states the tool provides clinical evidence briefs (GRADE ratings, key trials, safety signals) and lists several example questions. However, it uses French phrasing 'Brief évidence clinique' and includes a reference case that may be confusing. Overall, the purpose is well-defined despite some clutter.

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

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

The description implies usage through example questions (e.g., 'Review the clinical evidence for <drug/intervention> in <indication>'), but does not explicitly state when to use this tool versus related siblings like clinical_pharma_intel or sci_literature_search. No exclusions or alternatives are mentioned.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Beyond the annotations (readOnlyHint, openWorldHint, destructiveHint), the description adds significant behavioral context: the tool aggregates from multiple keyless sources, returns agent-shaped JSON with sourced fields (no hallucination), specifies SLA times and quality scores for different target types, and describes the depth parameter's impact on sources and time. This fully discloses behavior and constraints.

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

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

The description is dense and front-loaded with key information in the first sentence. However, it forms a single continuous paragraph that might benefit from slight structural separation (e.g., between usage guidance and output details). The length is justified by the amount of necessary context, but a touch more formatting could improve scannability.

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

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

Given the tool's complexity—multiple data sources, optional parameters, synchronous vs asynchronous usage, and a rich output schema—the description covers all essential aspects: purpose, input details, output format (agent-shaped JSON with specific fields), SLA, quality expectations, and pair tools. No gaps are evident; it is fully complete for an agent to select and invoke 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 every parameter documented in the schema. The description adds further meaning by explaining the implications of the depth parameter (e.g., 'easy' = Wikipedia + DDG, fast ~15s; 'medium' = + Yahoo Finance + EDGAR + Wayback, default ~45s), the required company field (name or domain), and the optional competitor list (max 5). This enriches the schema's raw descriptions.

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

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

The description clearly identifies the tool as a 'gold-standard competitive deep dive' that aggregates multi-source data and returns structured JSON with KPIs, signals, and a comparison matrix. It explicitly distinguishes itself from the sibling tool `competitor_intel` by noting the latter provides an 'LLM-narrated board briefing + slide script', thus differentiating their 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 explicit guidance on when to use this tool versus alternatives: 'Use sync for batch agents (≤30s tolerance). Use `competitive_deep_dive_async` + `competitive_deep_dive_result(job_id)` for conversational agents.' It also identifies the pair tool `competitor_intel` for narrative versions and details SLA times and quality scores, fully addressing usage context.

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

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

Discloses return timing (<200ms), background execution times (p50≈25s, p95≈30s for medium depth), and how to retrieve results via polling or webhooks. Annotations include readOnlyHint:true which is not contradicted as the tool initiates computation without modifying existing data.

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 tightly written with no wasted words. Each sentence adds value: purpose, timing, usage, parameter summary, and webhook option. Front-loaded with key information.

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

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

Given the existence of an output schema, the description covers all necessary aspects: return type, retrieval mechanism, timing, and alternative callback method. No gaps identified.

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

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

Schema coverage is 100%, so the description does not need to add much. It restates parameters but provides minimal extra context beyond the schema, such as default depth and max competitors. Baseline 3 is appropriate.

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

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

The description clearly states it is the async variant of competitive_deep_dive, returns a job_id, and runs research in background. It differentiates from the sync sibling and mentions the result retrieval method, making its purpose unambiguous.

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

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

Explicitly says 'Use this instead of competitive_deep_dive when the agent cannot wait >15s for a response.' Also provides webhook registration as an alternative, giving clear when-to-use guidance.

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

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

Annotations already indicate readOnlyHint, idempotentHint, destructiveHint; description adds status details and TTL expiration without contradicting any annotations.

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

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

Two sentences, front-loaded purpose, every sentence adds value: first states action, second details outcomes and usage 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?

With output schema present, description fully explains polling behavior, status meanings, and TTL; no gaps for a simple poll 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?

One parameter (job_id) has full schema coverage with description; description adds no new meaning beyond schema, meeting baseline for 100% 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?

Description clearly states it polls the result of a competitive_deep_dive_async job, listing specific statuses (pending, completed, failed, not_found) and TTL. It distinguishes from the async submitter sibling tool.

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

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

Explicitly says 'Call this after the eta_seconds hint returned by competitive_deep_dive_async', providing clear usage timing and context.

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

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive. The description adds behavioral details: it returns a narrative briefing with specific outputs (competitor moves, severity, recommendations, presenter script) and mentions async parameter behavior (returns job_id immediately if async=true). This adds value beyond annotations without contradiction.

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

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

The description is concise at about 4 sentences, front-loaded with core purpose and target audience, then lists outputs and inputs efficiently. Every sentence provides essential information without fluff.

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

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

Given the tool has 4 parameters (2 required), nested objects, and an output schema (though not shown here), the description covers inputs and outputs adequately. It lists key output elements (moves, severity, recommendations, script). Could mention pricing-radar explicitly but overall complete for the complexity level.

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

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

Schema coverage is 100% with descriptions for all parameters. The description adds meaning beyond the schema: explains purpose of 'async' (polling via job_result), clarifies 'focus' is optional for tracking specific moves, and emphasizes that 'selfCompany' requires a one-paragraph pitch. It also introduces the delivery persona 'Manue'. Adds moderate value.

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

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

The description clearly states it's an LLM-narrated competitive-intelligence briefing for human consumption, distinguishing it from the sibling tool 'competitive_deep_dive' which provides raw structured data. Specific verb (briefing) and resource (competitive intelligence) with concrete use cases: board meeting, pitch prep.

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 'Use when the buyer wants a narrative briefing or a deck' and contrasts with the paired tool 'competitive_deep_dive' for raw data. It provides clear context on when to use this tool, though it doesn't explicitly state when not to use it beyond the alternative.

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 declares it returns a 'structured, audited deliverable' and mentions server-side validation, but fails to disclose behavioral traits beyond annotations (only openWorldHint). It does not explain processing time, API calls, costs, or what 'audited' entails. More context is needed given the openWorldHint annotation.

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

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

The description is concise with 4 sentences, front-loading the value proposition. It uses bullet-like questions and a reference case. While efficient, it could be more structured by separating usage hints from the example.

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

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

Given the complexity (4 parameters, nested objects, openWorldHint, no output schema), the description is incomplete. It does not specify the deliverable's format, the expected structure of competitors/selfCompany, or how async interacts with the tool. More details are necessary 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 25% (only async is described). The description says 'send the documented case fields' but does not explain focus, competitors, or selfCompany parameters. It adds minimal semantic value beyond the schema, leaving agents to guess the expected formats for nested objects.

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

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

The description clearly states the tool returns a structured deliverable about competitor moves, answering specific questions about releases, pricing, hires, etc. It uses specific verbs like 'returns' and 'answers' and provides a reference case. However, it does not explicitly distinguish itself from sibling tools like competitor_pricing_radar or competitor_profiles, which slightly reduces clarity.

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

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

The description does not provide explicit guidance on when or when not to use this tool versus alternatives. It implies usage when needing recent competitor moves and urgent signals, but there is no comparison with sibling tools or mention of prerequisites. The reference case is not sufficient to guide usage decisions.

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 async behavior and server-side validation, adding beyond the openWorldHint annotation. However, it does not detail auth requirements, rate limits, or the nature of the '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 relatively short but starts with a confusing, jargon-laden phrase that wastes space. The later sentences are clearer and front-load key information, but the opening reduces overall conciseness.

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

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

The tool has 4 parameters, no output schema, and moderate complexity. The description covers the tool's purpose and gives an example, but does not explain output format or all parameters, leaving gaps.

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

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

With only 25% schema description coverage, only the 'async' parameter is explained in the schema. The description fails to clarify 'focus', 'competitors', and 'selfCompany', stating only that inputs are validated server-side.

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 provides a structured deliverable comparing competitor pricing, answering specific questions, and gives a reference case. However, the opening sentence is confusing and mixed-language, and it does not explicitly differentiate from siblings like 'competitor_pricing_scrape'.

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

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

The description mentions that inputs are validated server-side and that async mode can be used for slow operations, implying context for use. However, it does not explicitly state when to use this tool over 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 indicate readOnly and non-destructive nature. The description adds value by detailing proxy-awareness, cache TTL, and failure modes (degraded result with warnings). No contradictions.

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

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

The description is well-structured, front-loading the main action and then covering details. While comprehensive, it remains fairly concise, though some minor redundancy (e.g., 'proxy-aware' mentioned twice) could be trimmed.

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 (scraping, multiple fallbacks, degraded results), the description covers all key aspects: fetching strategy, extraction details, failure handling, proxy, caching, ICP. Output schema exists, so return values are implicitly covered. Complete.

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

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

Schema coverage is 100% with descriptions for both 'url' and 'async'. The description expands on the 'url' parameter (accepts domain or full URL) and 'async' (returns job_id immediately), providing more meaning than the schema alone.

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

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

The description clearly states the verb ('Scrape and parse') and resource ('competitor pricing page'), specifies the extracted data (plan names, prices, etc.), and distinguishes it from sibling tools like 'competitive_deep_dive' by focusing on pricing extraction.

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

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

The description provides clear usage context: proxy-aware fetching, fallback behavior, and caching. It mentions ICP and best practices for URL input, but does not explicitly contrast with alternative tools for when not to use it.

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

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

The description mentions server-side validation and documented case fields, hinting at input constraints. The annotation openWorldHint:true indicates arbitrary inputs are allowed, and the description does not contradict this. However, no additional behavioral context (e.g., side effects, data persistence) is provided.

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

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

The description is multi-sentence and contains some redundancy (e.g., 'Returns a structured, audited deliverable' and then example questions). It mixes French and English, which may reduce clarity. Could be more concise and focused.

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 nested objects, no output schema, and low parameter coverage, the description is insufficient. It does not explain the return format (aside from 'structured deliverable'), nor provides examples. An agent lacks critical 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?

Schema coverage is only 25% (only 'async' has a description). The description does not explain the meaning or expected format of the other three parameters (focus, competitors, selfCompany) beyond a vague reference to 'documented case fields'. It adds little value beyond the schema.

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

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

The description clearly states the tool returns competitor profiles (strengths, weaknesses, positioning, SWOT). It references a specific use case (Notion case) and mentions it yields a structured, audited deliverable. However, it does not distinguish from sibling tools like competitor_intel 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 includes example questions that indicate when to use (e.g., 'What are the strengths, weaknesses and positioning of each of my competitors?') but lacks explicit guidance on when not to use or alternatives. Usage is implied rather than clearly stated.

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 only include openWorldHint: true. The description mentions 'audited deliverable' and 'validated server-side' but does not disclose async behavior (the async parameter exists) or cost implications from 'agent-payable'. No contradiction with annotations.

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

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

The description is relatively concise with two sentences and bullet points. The reference case (Notion) adds value. Could be slightly more streamlined 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?

Despite no output schema and nested objects, the description lacks guidance on input structure, async processing, and output format. The reference to 'documented case fields' suggests missing documentation, leaving gaps for the agent.

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

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

Schema description coverage is only 25% (only async param described). The description adds that focus, competitors, and selfCompany are expected but gives no details on format or constraints. 'Send the documented case fields' is vague.

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 competitive recommendations and a structured 7/30/90/180-day response plan. It distinguishes from sibling tools like competitor_intel and competitor_moves by focusing on strategic actions and order, not just 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 implies usage for strategic competitive planning (C-suite, CMO) but does not explicitly state when to use vs alternatives like competitor_intel or competitive_deep_dive. No exclusions or alternative tools are mentioned.

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

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

Beyond the openWorldHint annotation, the description adds little behavioral context. It mentions 'returns a structured deliverable' but does not disclose whether the tool is synchronous or async, authentication needs, or error behavior. The async parameter in the schema is not referenced in the description, creating potential confusion.

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 plus a reference case) and gets to the point. However, the reference case may add unnecessary detail for some users, and the mix of languages could be streamlined.

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

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

Given the complexity (7 parameters, nested objects, no output schema), the description is incomplete. It does not explain output structure, async handling via job_result, or how inputs relate to the deliverable. Essential context is missing.

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

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

With only 14% schema description coverage, the description should explain the meaning of the six undocumented parameters (company, targets, geography, etc.). It merely says 'send the documented case fields' without elaboration, leaving the agent without semantic understanding of key inputs.

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

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

The description clearly states the tool produces a structured, audited deliverable for compensation plan architecture, mentioning a reference case with specific numbers. However, it does not explicitly differentiate from similar sibling tools like revops_architect or sales_enablement_architect.

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

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

The description lacks guidance on when to use this tool versus alternatives. It only says 'send the documented case fields' without specifying prerequisites, context, or exclusions. Given many similar sibling tools, this is a significant gap.

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

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

Annotations already indicate readOnlyHint=true and openWorldHint=true. The description adds significant behavioral context: it returns 'enrichment tags reframed as audience facets with confidence, corroboration and full provenance' and includes an 'entity-level provenance block (average confidence, data freshness)'. This goes beyond what annotations provide, with no contradictions.

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

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

The description is two sentences plus a usage note. It is concise and front-loaded with the main purpose. Slightly more structure could be beneficial, but it is efficient and to the point.

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

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

Given the tool has an output schema and annotations, the description is fairly complete: it explains what is returned (audience facets with provenance), when to use it, and the inputs. It does not mention async behavior, but that is covered in the schema. Overall, it provides enough context for an agent to use the tool effectively.

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

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

Schema coverage is 67% (2 of 3 parameters described in schema). The description only mentions 'Input: an entity_id and its type', which adds no meaning beyond the schema's descriptions for those parameters. The 'async' parameter is not mentioned in the description but is well-described in the schema. With high coverage, baseline is 3, and the description does not improve it.

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

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

The description clearly states the tool returns 'the audience targeting profile of a content entity' with specific elements like 'audience facets with confidence, corroboration and full provenance'. The verb 'return' and resource 'audience profile' are specific, and the mention of 'audience facets' and 'provenance' distinguishes it from sibling enrichment tools like 'content_enrichment'.

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

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

The description explicitly says 'When to use this tool: an ad-tech or marketing agent needs a machine-readable, verifiable audience descriptor for a franchise or work.' This provides clear context, but does not mention when not to use it or alternatives, making it a 4 rather than a 5.

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

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

Annotations indicate readOnlyHint and openWorldHint, which the description aligns with by describing a read-only browsing operation. It adds context about the domain scope and output structure, though it doesn't detail pagination behavior beyond the limit 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?

Three sentences: first states purpose, second gives usage guidance, third lists inputs. Front-loaded with the most important information, no redundant words.

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

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

Given an output schema exists, the description covers purpose, usage context, input semantics, and links to a related tool. It provides sufficient information for an agent to decide when and how to invoke this tool.

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

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

With 100% schema coverage, the description adds value by explaining the name filter is case-insensitive substring and enumerating the domain values. It doesn't repeat the entire schema, but provides practical hints beyond the bare 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 browses a curated catalog of video games, films, TV series, and music, returning franchises with their works. It distinguishes this from other tools by noting that franchise IDs can be used with content_enrichment for fine-grained tags.

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: when structured audited metadata is needed, to resolve a title to canonical entity, or to browse before enrichment. This provides clear context and differentiates from 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=true and openWorldHint=true, so the agent knows this is a safe read operation that may be slow. The description adds detail on the output (Jaccard score, shared tags, etc.) but does not disclose additional behavioral traits like rate limits or potential side effects beyond what annotations provide.

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

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

The description is concise (about 100 words) and well-structured: first sentence specifies purpose and output, followed by a clear 'When to use' section. Every sentence adds value without redundancy or filler.

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

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

The description covers all key aspects: purpose, usage context, output components (Jaccard score, shared tags, unique tags, breakdown by facet), and domain neutrality. With an output schema present and 100% parameter coverage, the description adds sufficient context for correct tool selection and invocation.

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

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

Schema coverage is 100%, and the description adds value by providing concrete examples (e.g., 'game-dark-souls', 'music-daft-punk') and embedding usage context (e.g., comparing Dark Souls and Elden Ring). These examples clarify the expected format and domain applicability 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 compares tag profiles of two content entities and returns similarity metrics. It uses specific verbs ('Compare', 'measure') and resource ('tag profiles of two content entities'), and distinguishes from siblings like content_similar by focusing on tag-based Jaccard similarity.

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 when-to-use scenarios (e.g., 'an agent needs to compare two franchises or works', 'find positioning overlap', 'answer if you liked X you might like Y questions'). It does not explicitly mention when not to use or name alternatives, but the guidance is strong and covers key 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?

The description adds significant behavioral context beyond the annotations (readOnlyHint, openWorldHint): it explains the two search modes, result verifiability (confidence/similarity scores), entity freshness, and async behavior. 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 yet comprehensive, with each sentence serving a purpose. It is front-loaded with the core purpose, then details modes, results, usage, and inputs. No unnecessary words.

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

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

Given the tool's complexity (two modes, five parameters, async, output schema), the description covers all essential aspects: purpose, inputs, behavior, results, and usage guidance. The agent has enough information to use 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?

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the mutual exclusivity of tag and query, giving examples for both, and clarifying the async parameter's purpose. This elevates the score to 4.

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

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

The description clearly states the tool's purpose: 'Discover content franchises within a domain.' It distinguishes two modes (tag and query) with concrete examples, making the tool's function unambiguous and differentiating it from potential 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 includes a 'When to use' section: 'an agent wants a domain-scoped shortlist by tag or by intent.' It provides clear context but does not explicitly state when not to use this tool or suggest alternatives, which would merit a 5.

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

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

Annotations only include openWorldHint. The description adds no behavioral transparency beyond that, such as side effects, performance expectations, or authentication requirements. The mention of server-side validation is minimal.

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

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

The description is short but includes an unnecessary French phrase. It conveys the core function but could be more precise and front-loaded.

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

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

Given the tool's complexity (5 parameters, nested objects, no output schema), the description is insufficient. It does not explain the output format or provide examples of expected input fields.

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?

Despite low schema documentation coverage (20%), the description provides no additional parameter semantics. Agents must guess the meaning of brand, months, cluster, and maxArticlesPerMonth without hints.

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

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

The description states the tool returns a structured, audited deliverable, but it does not specify what kind of content or deliverable. The French phrase and reference to Notion 2026 are not instructive. It fails to differentiate from sibling tools like content_audience_profile or content_catalog.

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 usage guidance is provided. The description does not indicate when to use this tool over alternatives, nor any 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 declare readOnlyHint=true and openWorldHint=true. The description adds behavioral details beyond that: it returns tag-level and entity-level provenance, confidence scores, freshness, and confirms it's a read operation. No contradiction.

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

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

The description is concise: a clear purpose statement, a use-case sentence, and input summary. No wasted words, front-loaded with key 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?

The tool has an output schema (not shown but present), and the description explains what the response contains: tag-level details (facet, confidence, corroboration, provenance) and entity-level provenance. This is sufficient for an agent to understand what to expect.

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

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

Schema coverage is 100%, so parameters are well-documented in the schema. The description adds value by providing example entity_id values and clarifying default for entity_type (franchise), which helps the agent understand how to form inputs correctly.

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

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

The description clearly states it returns an enriched tag profile for a content entity, specifying what tags include (facet, confidence, corroboration, provenance). It distinguishes from siblings by mentioning 'content_catalog' and 'fine-grained characterization', giving it a unique identity among many 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 explicitly says when to use: 'an agent has a franchise or work id (from content_catalog) and needs... for matching, recommendation, contextual targeting or analysis.' It provides clear context but does not mention when not to use or list alternatives, slightly reducing the score.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is covered. The description adds useful context about return contents (enrichment tags, freshness summary) but does not mention the async option or potential latency, which are relevant for an audit tool.

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: the first nails purpose and output, the second gives usage context, the third lists inputs. No wasted words, front-loaded with key 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?

With output schema present and annotations covering safety, the description is largely adequate. It lacks mention of the async parameter and possible latency, but given the complexity and existing schema detail, it remains fairly complete.

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

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

Schema description coverage is 100%; the description merely restates 'Inputs: entity_id (required) and entity_type (franchise or work)' without adding examples, constraints, or usage tips 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 specifies 'Audit the full data provenance of a content entity' with detailed output elements (extraction source, corroboration score, etc.). It clearly distinguishes from sibling tools like content_enrichment and content_catalog by focusing on provenance rather than enrichment or listing.

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 when to use: 'before citing or relying on enriched content data in a high-stakes context (ad targeting, editorial, analysis).' This provides clear context, though it does not name alternative tools for lower-stakes scenarios.

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

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

Annotations already declare readOnlyHint and openWorldHint. The description adds behavioral details like 'every result carries its score, price and source', which goes beyond annotations without contradicting them.

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

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

The description is front-loaded with purpose and examples, every sentence contributes meaning. It is slightly long but remains focused and efficient.

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

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

With 9 parameters, output schema present, and high schema coverage, the description covers core behavior, return structure, and use cases comprehensively, leaving no critical 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%, baseline 3. The description adds context for parameters like criterion options, direction defaults, and available_only, providing value beyond the schema's enum descriptions.

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

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

The description starts with a specific verb 'Return the TOP-ranked content entities', provides concrete examples like 'best video games', and distinguishes itself from sibling tools by focusing on ranking for decision support.

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: an agent must produce a ranked shortlist to support a recommendation, a purchase or a 'what is the best X' decision.' Could be improved by mentioning when not to use it, but the guidance is clear.

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