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

No annotations are provided, so the description carries the burden, and it does disclose the return shape (a comparison table with metrics such as fees, profit split, payout speed and rules). It does not confirm read-only behavior, authentication requirements, rate limits, or what happens with invalid or duplicate slugs, so meaningful gaps remain for a tool with zero annotation coverage.

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

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

Three tight sentences: purpose and scope first, return content second, the optional parameter last. The metric examples (fees, profit split, payout speed, rules) do double duty by hinting at valid metrics values, so no sentence is wasted.

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

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

With no output schema and no annotations, the description correctly summarizes what is returned, which covers most of the agent's needs for a read-only comparison call. However, it omits how to discover valid metric identifiers and any error/edge-case behavior, leaving the metrics parameter under-specified for a 2-parameter 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 coverage is 50%: 'slugs' is documented with an example array, while 'metrics' has no schema description. The description partially compensates by stating 'Optionally specify which metrics to compare', clarifying that metrics is optional and filters the output, but it never enumerates valid metric names, leaving that array effectively opaque.

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 names a specific verb (Compare) and resource (futures prop trading firms) and constrains cardinality to 2-4 entities side-by-side, which separates it from get_firm (single firm) and list_firms/search_firms. An agent can see at a glance that this tool produces a multi-entity comparison rather than a lookup.

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 phrase 'Compare 2-4 firms side-by-side' implies the situation in which it applies, but there is no explicit when-to-use versus when-not-to-use guidance and no sibling is named as an alternative (e.g., use get_firm for a single firm). Usage is inferable but not 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?

With no annotations, the description carries the full burden. It does disclose the shape of the returned payload (pricing, profit split, rules, payout speed, platforms, referral link), which is useful. However, it says nothing about read-only semantics, error behavior for an invalid slug, rate limits, or whether the response is cached/live — gaps that matter for a lookup tool with zero annotation coverage.

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

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

Front-loaded and efficient in intent, but the second half is a wholesale duplication of the enum list already present in the input schema, which is waste in the description channel. Two sentences is otherwise appropriate for a one-parameter tool.

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

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

No output schema exists, and the description compensates by enumerating the fields returned, which is the key missing piece for callers. Combined with a fully documented parameter, an agent has enough to invoke correctly; only authentication/error context is absent.

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 single 'slug' parameter is fully documented in-schema, so the baseline of 3 applies. The description only repeats the valid-values list that already exists in the schema rather than adding format, normalization, or fallback 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?

States a specific verb and resource ('Get detailed information about a specific futures prop trading firm') and enumerates the data returned (pricing, profit split, rules, payout speed, platforms, referral link), so the agent knows exactly what it retrieves. The word 'specific' implies single-entity lookup against the sibling list/search/compare tools, but no sibling is named 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?

Usage is only implied: 'a specific firm' suggests this is the single-firm detail lookup as opposed to compare_firms or search_firms, but the description never states when to choose it over those siblings or what prior step (e.g., list_firms) supplies the slug. No exclusions or prerequisites 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?

With no annotations, the description carries the full burden, and it delivers the key behavioral facts: this is an argument-free fetch that returns exactly 9 questions with options, descriptions, and metadata. It doesn't mention permission requirements or constraints, but for a read-only static quiz fetch that is a minor gap.

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, no filler: what it returns, what it contains, and the workflow it belongs to. The return shape is front-loaded before the usage instruction.

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?

Because no output schema exists, the description must describe the return value, and it does so at a useful level (9 questions with options, descriptions, metadata). It could name the metadata fields or the answer format expected by recommend, but nothing essential for calling it correctly 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?

The tool takes zero parameters, so per the baseline there are no parameter semantics to clarify. The description correctly implies no inputs are required to retrieve the question set.

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?

States a specific verb and resource (get quiz questions) plus scope (all questions for the prop firm recommendation quiz) and even the expected payload size. An agent can distinguish it immediately from siblings like get_firm, search_firms, and recommend.

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 to use it to walk users through the quiz step-by-step and to follow up with the recommend tool once answers are collected, which gives a clear sequencing context. It lacks any when-not-to-use or exclusion guidance, 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?

No annotations are provided, so the description must disclose behavior. It describes the return contents (firm name, Trustpilot rating, monthly fee, key strength) and the fixed size (12 firms), which is valuable behavioral context. It does not explicitly state read-only nature, but 'List' strongly implies it.

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 the action and scope, followed by the return details. Every sentence earns its place with 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?

For a simple, no-parameter list tool with no annotations and no output schema, the description adequately covers purpose, scope, and return fields. An agent has everything needed to invoke it correctly.

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

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

There are zero parameters, so the baseline score of 4 applies. The description correctly does not need to explain parameter semantics beyond noting the scope is all firms.

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?

States a specific verb (List) and resource (futures prop trading firms), with scope 'all available' and returned fields. This implicitly distinguishes it from get_firm (single) and search_firms (filtered), but does not name the siblings 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?

The phrase 'List all available' implies use when a complete overview is needed, giving some context. However, it offers no explicit when-to-use guidance or comparison to alternatives like search_firms or get_firm, and no exclusions are 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?

No annotations are supplied, so the description carries the full burden. It usefully discloses the return shape ('top 3 recommendations with match reasons, warnings, and referral links') and that it weighs multiple profile dimensions, but says nothing about permissions, whether results are deterministic, or how partial input is handled. Some behavioral value, but notable gaps for an unannotated engine.

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

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

Three sentences, front-loaded with the purpose, then the mechanism, then the output contract. No filler, though 'This is the core recommendation engine' slightly restates what the first sentence already conveys.

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 9-parameter, no-output-schema, no-annotation tool, the description covers purpose and return shape adequately but leaves the meaning and effect of five enum parameters undocumented. It is workable but not complete for a tool this wide.

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 33%, so the description should compensate, and it partially does by naming experience, budget, trading style, concerns and dealbreakers as analyzed inputs. It omits situation, supportNeeds, payoutPriority, paymentPreference and the enum semantics, leaving half the parameter surface unexplained in either place.

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 names a specific verb and resource ('Get personalized futures prop firm recommendations') and identifies itself as 'the core recommendation engine,' which separates it well from get_firm, search_firms, list_firms and compare_firms. It stops short of explicitly contrasting itself with those siblings, which keeps it from a 5.

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

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

Usage is implied: an agent can infer this is the tool to call when it wants personalized picks based on trader profile inputs. However, there is no explicit when/when-not guidance and no routing to alternatives such as search_firms for browsing or compare_firms for head-to-head comparison of already-known firms.

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 are provided, so the description carries the full burden. It does not state that this is a read-only operation, whether results are ranked or how, whether filters are ANDed together, what happens with zero filters, any result limit or pagination, or the return shape.

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 tight sentences with the purpose front-loaded and the filter dimensions grouped in the second. Every clause earns its place, though the second sentence reads as a parameter dump rather than added 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?

For a 7-param, all-optional filter tool with no output schema and no annotations, the description covers which axes can be filtered but omits return format, filter combination semantics, and empty-filter behavior — leaving real gaps for an agent to guess at.

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 0% and there are 7 bare-named parameters, so the description must compensate. It does list all the filter axes (budget, payment type, Trustpilot rating, consistency rules, overnight holding, activation fees, payout speed), which maps to the 7 parameters, but adds no semantics such as rating scale, budget currency, or default behavior.

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?

States a specific verb and resource ('Search and filter futures prop trading firms') and even enumerates the filterable dimensions. It is clearly a discovery tool, but it never distinguishes itself from the sibling list_firms or explains how it relates to compare_firms/get_firm.

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?

Usage is implied by 'search and filter by criteria' — an agent can infer this is the tool for narrowing down firms. However, no explicit when-to-use, when-not, or alternative routing is given despite four plausible sibling tools (list_firms, compare_firms, recommend, get_firm).

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