Spain Legal by Legal Fournier

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

Annotations already indicate read-only, idempotent, non-destructive behavior, and the description adds useful context about qualitative gatechecks and the output structure (rule trace, review level, follow-up resources). It does not contradict annotations.

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

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

A single, well-structured sentence that immediately states the tool's purpose and outputs, with zero wasted words. It is appropriately front-loaded.

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

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

The description covers purpose, method, and output hints, and the presence of an output schema reduces the need to explain return values. However, it could briefly mention when to use this tool relative to similar residency/tax 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 100%, so parameters are fully documented in the schema. The description adds no additional parameter semantics, but this is acceptable given the high coverage baseline.

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

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

The description uses a specific verb 'Screen' with a clear resource ('Spain's Beckham regime') and distinguishes the tool from siblings by focusing on eligibility gatechecks and return values (rule trace, review level, canonical MCP resources).

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 checking Beckham eligibility but does not explicitly state when to use it over sibling tools like get_residency_path or compare_tax_regimes, nor does it mention exclusions 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?

Annotations already establish the tool as read-only, idempotent, and non-destructive. The description adds value by disclosing the return nature ('reasoning, review level, and canonical MCP resources') and explicitly noting what it does not do ('instead of rate tables'). This gives the agent useful behavioral expectations beyond the annotation flags.

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

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

The description is a single, well-structured sentence that front-loads the core purpose and includes essential clarifications about the output and limitations. No filler or redundant phrasing; every element earns its place.

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

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

Given the presence of an output schema, annotations, and complete parameter descriptions, the description provides sufficient context for a conceptual comparison tool. It clarifies the conceptual scope and output format, though it could be improved by mentioning typical use cases or when to prefer this tool over siblings.

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

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

Schema covers all four parameters with descriptions, including an enum for employment_type. The description does not add parameter-specific detail but benefits from the high schema coverage. Since the description does not supplement the schema, the baseline score of 3 is appropriate.

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

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

The description clearly states a specific action ('Compare Beckham versus standard Spanish resident taxation') and distinguishes this tool from siblings by emphasizing the conceptual nature and output format ('reasoning, review level, and canonical MCP resources instead of rate tables'). It is immediately clear what the tool does and how it differs from related tools.

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

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

The description implies usage scenarios (comparing regimes conceptually) but does not explicitly state when to choose this tool over alternatives such as check_beckham_eligibility or get_residency_path. No exclusions or conditions are provided, leaving the agent to infer when this tool is appropriate.

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

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

Annotations already declare readOnlyHint=false, so the description needn't state it's a write. It adds meaningful context by warning: 'This is a consequential action,' and by specifying the consent prerequisite. This is valuable beyond the annotations.

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

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

Two sentences, no filler. The first sentence states the core action; the second delivers a critical warning. Ideal structure.

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 (21 params, 6 required) and the presence of an output schema, the description provides sufficient context: it identifies the action, the consent gate, and the workflow origin. It doesn't need to enumerate params or return values, as those are covered by schema.

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

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

Schema coverage is 100%, with descriptions on all 21 parameters, including the const:true consent fields. The description reinforces the consent requirement but adds little beyond what the schema already documents. Baseline 3 is appropriate.

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

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

The description clearly states the tool's function: 'Create an accepted Legal Fournier lead from an agent workflow after explicit user consent.' This is a specific verb+resource+context, distinguishing it from sibling tools like route_to_legal_fournier_help or find_legal_fournier_tool by focusing on actual lead creation.

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 an explicit condition: 'call only when the user has asked to contact Legal Fournier and has consented to follow-up.' It provides a clear when-to-use context but doesn't mention specific alternative tools or when-not-to-use, so it's slightly below the highest bar.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds context about the output being 'stable' and 'canonical,' which communicates reliability and authoritative sourcing beyond what annotations provide. 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, well-structured sentence that front-loads the purpose and adds meaningful detail (stable workflow, procedural distinctions, canonical resource). No wasted words.

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

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

For a parameterless tool with an output schema, the description fully covers what is being returned and why it's useful ('for agents'). The output schema handles return-value specifics, so the description doesn't need to enumerate them. The tool is simple enough that this is complete.

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

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

The tool has zero parameters, so the baseline is 4. The description doesn't need to explain parameter meaning, and it doesn't; it focuses on the output content instead. Schema coverage is 100% (no parameters), so no gaps exist.

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 stable NIE and TIE workflow, the key procedural distinctions, and the canonical process resource for agents.' This uses a specific verb ('Return') and distinct resource (NIE/TIE workflow), which differentiates it from sibling tools like get_visa_options or check_beckham_eligibility.

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 this tool: when an agent needs the canonical NIE/TIE workflow or procedural distinctions. It doesn't explicitly name alternatives or exclusions, but the context is clear enough for an agent to select this over related immigration/legal tools.

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

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

Annotations declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds context about the scope (Spain legal/tax/property/business/private-client) and output (public tool page and MCP workflow), but does not disclose matching logic, ranking criteria, or ambiguous query behavior. It does not contradict annotations.

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

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

The description is a single sentence of 24 words, front-loaded with the core action ('Match') and resource, with no redundant or filler content. Every word contributes to the meaning.

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 routing purpose, the description states its function and scope but does not explain when to use it versus siblings or what kind of output to expect beyond 'public tool page and MCP workflow'. The rich schema and annotations reduce the burden, but the description still lacks explicit usage context. It is minimally 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%, with all six parameters documented in the input schema, including enums for 'area' and 'preferred_language'. The description adds no additional parameter-level meaning beyond the schema, so the baseline of 3 is appropriate.

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

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

The description uses a specific verb ('Match') with a clear resource ('a Spain legal, tax, property, business, or private-client case') and target ('best Legal Fournier public tool page and MCP workflow'). It clearly distinguishes this from sibling tools like check_beckham_eligibility or compare_tax_regimes, which are domain-specific, by positioning this as a routing/matching tool.

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

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

The description implies a usage scenario (when you have a legal case and need to find the right tool), but it does not explicitly state when to prefer this over directly using a specific sibling tool. There are no exclusions or alternatives mentioned, so guidance is only implicit.

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 destructiveHint false, but the description adds valuable context beyond that: it specifies the consultation length (45-minute), the payment model (agent-paid), and enumerates the exact operations it will not perform (quote, booking, order, payment). This gives the agent a precise behavioral contract without contradicting annotations.

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

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

The description is exceptionally concise: two sentences that front-load the core purpose and immediately follow with a side-effect guarantee. Every word earns its place, with no redundant filler or repetition of schema 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 simplicity (a read-only availability check), the description is fully complete. It states what it reads, the specific consultation type, and what it does not do. An output schema exists, so return values need not be described. The context signals and sibling tools indicate a decision tree where this tool is clearly the safe, non-committal availability check.

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 provides 100% coverage with descriptions for all three parameters (from, to, timezone). The tool description itself does not add parameter-specific information, but since the schema fully documents them, the baseline of 3 is appropriate. No extra meaning is needed.

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

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

The description clearly states the tool's function: 'Read live Cal.com availability for the 45-minute agent-paid Legal Fournier consultation.' This specifies the verb ('Read'), the resource ('live Cal.com availability'), and the scope, making it unmistakable. It also distinguishes itself from siblings by explicitly stating it never creates a quote, booking, order, or payment.

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

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

The description provides clear context for when to use the tool: checking availability without performing any side effects. It explicitly states 'This tool never creates a quote, booking, order, or payment,' which is a strong when-not usage signal. However, it does not name alternative tools for booking or creation, so it lacks the full 'alternatives' dimension.

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

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

Annotations already mark readOnlyHint=true and destructiveHint=false, and the description reinforces this by stating 'This MCP tool is read-only and never creates an order or submits payment itself.' It adds meaningful behavioral context by specifying that the agent must obtain explicit user authorization before using the returned endpoints, which is valuable beyond the annotations.

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

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

The description is three sentences, front-loaded with the main purpose in the first sentence. Every sentence contributes: first introduces the tool, second details the output, third clarifies the read-only nature and authorization requirement. There is no redundancy or 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 no parameters and output schema is present, the description covers the essential information: what options are returned, for which consultation types, and that user authorization is needed for subsequent actions. It does not mention edge cases like unavailability, but for a read-only informational tool, this is sufficient.

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

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

With zero parameters, the description does not need to explain parameter semantics. It adds value by describing the return content (live options, prices, terms, schemas, endpoints) which gives the agent a sense of what the tool provides. This aligns with the baseline of 4 for 0-parameter tools.

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

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

The description clearly states the tool's function: 'Discover and arrange paid consultations' and then specifies it returns live options for a written email consultation and a 45-minute call. It distinguishes itself from siblings like get_paid_call_availability by listing the exact consultation types and the rich set of returned data (prices, terms, schemas, endpoints).

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 clear context on when to use the tool: when needing options for a written email consultation or a 45-minute call. It also explains that the returned endpoints should only be used after explicit user authorization, providing practical guidance. However, it does not explicitly name alternative tools or exclusions, so it falls short of 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 already declare readOnlyHint and idempotentHint, signaling safe read-only behavior. The description adds value beyond annotations by disclosing that the tool generates 'explicit caution flags for counting issues,' which is a behavioral characteristic not present in annotations. This is useful context for the agent to anticipate extra guidance in responses.

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

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

The description is a single, well-structured sentence that packs the verb, resource, inputs, and output characteristics. It is front-loaded, immediately stating the tool's purpose, and contains no fluff or redundant information.

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

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

With a rich schema (5 params, enums, descriptions) and an output schema present, the description does not need to explain return values or parameter details. It provides the high-level purpose and alerts to caution flags, which is sufficient for an agent to select the tool. A slight deduction is made because the description does not mention the various nationality tracks or exceptions, but those are well-covered in the schema.

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

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

Schema description coverage is 100%, so the schema already fully documents all parameters. The description merely repeats the two required inputs (current status and time in Spain) without adding deeper semantic meaning. According to the rubric, a baseline of 3 is appropriate when schema does the heavy lifting.

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 explains the next permanent-residency or nationality milestone based on current status and time in Spain. The verb 'explain' and specific resource make it distinct from sibling tools like get_visa_options or explain_nie_process. It also highlights a unique output trait (caution flags) that further differentiates it.

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

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

The description implies when to use the tool: when a user needs to know the next residency or nationality milestone. It provides clear context by specifying inputs (current status, time in Spain) and outputs (caution flags). However, it does not explicitly mention alternatives or exclude any sibling tools, so it stops short of full when/when-not 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?

The annotations already declare read-only, idempotent, and non-destructive behavior. The description adds valuable detail about what is returned: decision traces, next actions, and canonical MCP resources. It does not mention edge cases or limitations, but given the strong annotations, this is sufficient.

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 sentence that front-loads the primary action and enumerates the three key outputs. Every word earns its place; 'evergreen logic' is slightly idiomatic but not wasteful.

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

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

With 8 parameters, full schema coverage, and a rich output schema, the description provides enough context for an agent to invoke the tool correctly: it names the purpose, the outputs, and implicitly the inputs via the schema. It could further distinguish itself from get_residency_path, but that is a minor gap.

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

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

The input schema covers 100% of parameters with descriptions, so the baseline is 3. The tool description itself does not add any parameter-specific meaning beyond what the schema already provides, so no higher score is warranted.

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 core action: 'Rank Spain residence routes' and specifies exact outputs (decision traces, next actions, canonical MCP resources). This distinguishes it from siblings like get_residency_path, which likely returns a single path rather than a ranked set of options.

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 makes it evident the tool is for ranking Spain residence routes based on inputs like nationality, income source, and intent. However, it does not explicitly describe when not to use it or name alternatives, though the purpose strongly implies its use case.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the operation's safety profile. The description adds a meaningful behavioral nuance: the output is a 'ready-to-send handoff message,' implying the tool does not actually send the message but prepares it. This is beyond what annotations provide, so a score of 4 is appropriate.

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, concise sentence that front-loads the primary action ('Decide whether...'), then lists the three outputs. There is no filler or redundancy; every phrase adds value.

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

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

Given that the tool has an output schema (so return values need not be detailed in the description), annotations that clarify safety, and a moderate number of parameters (all schema-documented), the description covers the core purpose and outcome. It doesn't mention explicit edge cases or prerequisites, but it's sufficient for an agent to understand the tool's role within the sibling set.

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%, with all five parameters (area, urgency, blockers, already_filed, preferred_language) fully documented in the input schema. The description does not add any additional parameter-level meaning beyond what the schema already provides, so the baseline 3 applies.

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

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

The description clearly states the tool's function: 'Decide whether a Spain legal matter should be escalated to Legal Fournier' and specifies the deliverables ('service match, preparation checklist, and ready-to-send handoff message'). This distinguishes it from siblings like run_legal_fournier_tool and create_legal_fournier_contact_request, which focus on execution or contact creation rather than the escalation decision.

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

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

The description provides clear context: it is used when a Spain legal matter may require escalation to Legal Fournier. It doesn't explicitly state when not to use it or reference alternative sibling tools, but the core scenario is well implied, earning 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 already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is well covered. The description adds valuable behavioral context by enumerating the output categories (intake screening, missing facts, complexity flags, handoff guidance), which clarifies what the agent can expect from the run. There is 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, front-loaded sentence that immediately states the action and outcome. Every word earns its place; there is no fluff, repetition, or unnecessary detail. It is appropriately sized for the tool's 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 rich input schema (6 params, 2 enums, 100% coverage), output schema, and strong annotations, the description is mostly complete for selecting and invoking the tool. It explains the purpose and return types. However, it lacks contextual pointers about how to choose between this generic runner and the specialized sibling tools, which is a minor gap in 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 input schema has 100% coverage with descriptions for every parameter, so the schema already handles parameter semantics. The description adds minimal information about parameters, only implicitly referencing facts/user_goal through 'intake screening.' With high schema coverage, a baseline of 3 is appropriate; the description does not significantly enhance parameter understanding.

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

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

The description clearly states a specific verb and resource: 'Run a Legal Fournier public calculator or screener directly inside MCP.' It also lists the outputs ('intake screening, missing facts, complexity flags, and handoff guidance'), making the tool's role unambiguous. This distinguishes it from siblings like find_legal_fournier_tool (which locates tools) and specific-purpose tools like check_beckham_eligibility.

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

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

The description provides no guidance on when to use this tool versus the many sibling tools. It does not mention alternatives, exclusions, or decision criteria such as 'use for any of the listed public calculators; for specialized checks use the dedicated tool.' The only implication is that it runs calculators, but no explicit when-to-use or when-not-to-use context is given.

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