Signal Band decision engines

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

With no annotations, the description carries the full behavioral burden. It usefully discloses return content (new API key on first purchase only, download links) and an idempotency-style constraint ('once'), but says nothing about auth requirements or what happens on a repeat/second call (error vs. same result).

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?

One dense sentence with the action, precondition, and return payload front-loaded. Every clause earns its place with no 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?

For a one-parameter tool with no output schema, the description covers the essential flow (pay, then claim) and even inlines the return values. The remaining gaps are the undocumented session_id and unstated failure/repeat-call behavior.

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 the single required parameter session_id is never explained in the description. The agent must infer that session_id comes from the payment/checkout flow; format, source, and validity are left undocumented.

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 a specific verb and resource ('claim the purchase') and adds scope ('after the owner pays', 'once'), which clearly separates it from sibling create_checkout. It never names a sibling explicitly, so it stops short of full differentiation.

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 gives clear timing context ('After the owner pays') and an invocation constraint ('claim the purchase once'), telling the agent when this tool is appropriate. It does not name alternatives or state what to do if the claim has already happened.

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 disclose real behavioral context: the link is an external Stripe artifact requiring owner approval/payment, and it returns a claim URL. It omits auth requirements, link expiry, idempotency, and what happens if the owner never pays.

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 short sentences, front-loaded with the action and followed by the workflow and return value. Nothing is redundant and every sentence earns its place.

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

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

With no output schema, the description steps in to name the return value ('claim URL'), which is helpful. But for a three-parameter, no-annotation mutation tool, it leaves parameter semantics, error conditions, and link lifecycle unexplained.

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 33% – only 'email' is documented. The description adds nothing about 'offer' or 'billing'; the enums list values but do not explain which billing cadence is valid for which offer. With a coverage gap this large, the description should have compensated and did not.

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 ('Create a Stripe Checkout link for an offer'), which is unambiguous and actionable. It does not distinguish itself from siblings such as claim_purchase or get_pricing, but the core purpose is clear.

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

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

The description implies the workflow ('Give the link to the account owner to approve and pay'), which tells the agent what to do with the result. However, it gives no explicit when-to-use guidance relative to siblings like claim_purchase or get_pricing, and no exclusions 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 no annotations, the description carries the full burden and does meaningful work: it declares the check is free, enumerates the four possible actions including not_a_fit, states what is returned, and clarifies 'The owner always approves payment,' implying this call never itself moves money. Gaps remain on auth, rate limits, and what a 'fit range' concretely is.

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 trigger, then the return shape, then the payment caveat. Only the branded opener 'Free Agent Soundcheck' is arguably dead weight; the rest 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?

With no output schema, the description correctly picks up the burden by naming the return fields and enumerating the action values. For a free, 8-parameter advisory tool it is nearly complete, missing only the semantics of the three undocumented parameters and any auth/permission context.

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

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

Schema coverage is a middling 63%, with descriptions on decision, forClients, alternative, and inputsAvailable. The description adds only the loose framing 'the owner's decision' and mentions engines/packs/practitioners, doing nothing for stakes, budgetUSD, or runsPerMonth. Baseline 3 is appropriate; it neither compensates for the gap nor wastes words.

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 second sentence gives a concrete verb and scope: check whether an engine, Power BI pack, or practitioner fits the owner's decision before any purchase. That distinguishes it from run_engine, get_pricing, and claim_purchase, though the leading brand phrase 'Free Agent Soundcheck' contributes nothing. Clear purpose, just no explicit sibling naming.

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?

'Before recommending any purchase, check whether ... fits the owner's decision' gives a clear trigger condition and a natural place in the workflow. It does not state when NOT to call it or name the sibling tools it replaces, but the context is unambiguous.

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 does disclose the key behavioral fact: identity comes from the API key in the Authorization header, so no parameters are needed. However, it says nothing about the read-only nature, rate limits, caching, or whether the data is live versus aggregated, which matters for a usage/billing 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?

A single tight sentence with no filler, and the most important information (whose data, and what data) is front-loaded. It is a fragment rather than a full sentence, but nothing 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?

For a zero-parameter read tool with no annotations and no output schema, the description usefully enumerates the returned fields (credit balance, plans, recent usage), which compensates for the absent output schema. The main gap is the missing routing guidance relative to billing-oriented 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?

The tool takes zero parameters, so the baseline is 4. The description correctly signals there is nothing to supply beyond the Authorization header, and adds no misleading param expectations.

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 the concrete data returned (credit balance, plans, recent usage) for the authenticated API key, so an agent can infer this is a read of the caller's own account. It lacks an explicit verb and never differentiates itself from siblings such as get_pricing or list_products, which also surface account-adjacent billing data.

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 statement of when to call this versus the many sibling tools (get_pricing, get_engine, list_products). Usage is only implied by the word 'account' in the name; no prerequisites, triggers, or exclusions 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?

No annotations are provided, so the description carries the full burden. It usefully discloses what the call returns (schema, sample input, outputs, method), which is meaningful behavioral context given the absence of an output schema, but it says nothing about read-only nature, error behavior for an unknown engine_id, or response size.

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 compact sentence with the verb and returned artifacts front-loaded and no filler. It is efficient, though the comma-listed payload reads as slightly terse rather than 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?

With no annotations and no output schema, the description does helpfully cover the return contents, but it leaves the sole required parameter undocumented and omits any failure or scope behavior, so it is only partially complete for a metadata-fetch 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 0%, so the description must compensate for the single engine_id parameter and does not. It never mentions engine_id, its format, or where to obtain a valid value (e.g., from list_engines).

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 ('Get') and resource ('one engine') and enumerates what is returned: input JSON schema, sample input, outputs, and method. This clearly separates it from run_engine (execution) and list_engines (enumeration), though it does not name those 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?

There is no statement of when to use this tool versus list_engines or run_engine. The appropriate context (inspecting an engine before invoking run_engine) must be inferred entirely by 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?

No annotations are provided, so the description carries the burden, but this is a zero-parameter read tool with low behavioral risk. The description does add value by disclosing the currency (USD) and that each entry includes inclusion/exclusion details, though it says nothing about freshness, caching, or whether output is static.

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 front-loaded sentence with no filler — every clause (categories, currency, scope of detail) carries information. It is dense but earns its 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?

With no output schema and no annotations, the description must cover the return shape, and it only gestures at it ('what each includes and excludes', 'in USD'). It is adequate for a simple static pricing lookup but leaves the actual result structure unspecified.

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 no parameters, so there is nothing for the description to clarify; the baseline for zero-param tools applies. The description correctly avoids inventing filter semantics that do not 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 enumerates the concrete resource set — credits, monthly/annual subscriptions, Power BI licence packs, Practitioner Review — which tells an agent exactly what pricing data is returned and distinguishes it from siblings like list_products. It lacks an explicit verb, relying on the name 'get_pricing' to supply the action, but the resource scope is 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?

There is no statement of when to call this versus alternatives such as list_products, evaluate_fit, or create_checkout. The enumeration implies it is a reference lookup, but no condition, prerequisite, or exclusion is given.

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

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

No annotations are provided, so the description carries the full burden. It usefully discloses the return shape (id, pricing model, credit cost, outputs), which is the key behavioral payload for a list tool, but says nothing about ordering, pagination, auth requirements, or whether all engines are returned.

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?

One sentence, front-loaded with the verb and resource, and every clause (the four returned fields) earns its place. No 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?

For a zero-parameter list tool with no output schema, the description covers the essential return fields an agent needs to decide whether to call it. Minor gaps are pagination/ordering and whether the list is complete, but nothing critical 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 the baseline is 4. The description correctly implies no filtering input is available, meaning the caller gets the full set of engines.

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 ('callable decision engines'), and enumerates the fields returned (id, free/paid, credits per run, return values). The plural form implicitly contrasts with the sibling get_engine, though 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: an agent can infer this is the discovery step before get_engine or run_engine. There is no explicit when-to-use statement, no exclusions, and no named alternative such as get_engine for a single engine.

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 behavioral burden. The verb 'List' implies a safe read and the description names the fields returned, but it says nothing about ordering, pagination, or the effect of omitting the filter.

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?

One dense but front-loaded sentence that leads with the verb and resource. Nothing is wasted, though the trailing clause listing returned fields makes it slightly crowded.

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 or annotations, the description reasonably conveys the shape of the result set, but it leaves the enum meaning and selection guidance unexplained for an agent trying to call it correctly.

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

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

Schema description coverage is 0% and the single 'line' parameter has an unexplained enum (validate/decide/futures/programs). The description only says 'by line' without defining what a line is or that omitting it returns all products, so it does not compensate for the schema gap.

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

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

States a specific verb (List) and resource (Signal Band products), and clarifies the domain ('Power BI decision tools') plus the fields returned (question, engine, licence offer). It does not, however, explicitly differentiate itself from the nearby list_engines or get_pricing siblings.

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 when-to-use guidance, no prerequisites, and no mention of alternatives such as list_engines or get_engine. The agent must infer that this is a catalog-browsing tool from the verb alone.

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

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

With no annotations, the description carries the full burden and does disclose real behavioral traits: free engines require no key, paid engines authenticate via the Authorization header, and running a paid engine spends credits. It omits failure/error behavior, idempotency, and rate limits, keeping it below a 5.

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 core action front-loaded and the auth/billing caveat immediately after. No 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?

Covers purpose, auth, and cost, but with no output schema and no annotations the agent gets no information about return values or error conditions for a tool with a nested object input. Adequate but with clear 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 only 50% (engine_id is undocumented) and the description adds nothing about how to obtain engine_id or what shape 'structured input' takes. It does reference the inputSchema contract indirectly, but an agent still needs get_engine to call this 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?

States a specific verb (run) and resource (decision engine) plus the input shape (structured input), so an agent can distinguish it from get_engine/list_engines. It does not differentiate itself from the potentially overlapping sibling evaluate_fit, so it falls short of 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 only implicit: run an engine when you have an engine_id and matching input. The free-vs-paid distinction hints at prerequisites but names no alternative tool or exclusion, and never says when to prefer this over evaluate_fit.

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