Earning Paths Atlas

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

With no annotations, the description carries the full burden and does reasonably well: it discloses the verdict taxonomy, that the call is free, the ban-reason and cost outputs, and an explicit scope limitation (no legal or tax advice, not a KYC bypass). It does not state whether the call is read-only/idempotent or whether tools like get_plan are prerequisites.

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-loads the usage directive before the return-value detail, and the trailing "Free. No legal or tax advice..." sentences are short and load-bearing. The middle sentence is dense with German verdict tokens and bracketed field names, which costs some readability but not much space.

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?

There is no output schema, so the description must describe returns and does: verdict, top ban reasons, risk_without_plan with sources, and what_the_plan_adds. This covers a 4-parameter query tool adequately, though it omits error/empty-result behavior and any prerequisite on get_platform/list_platforms.

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 baseline is 3, but the description adds genuine meaning by tying the inputs together: the verdict depends jointly on country, automated operation, and AI content. It explains why those parameters matter rather than just restating 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?

States a specific verb+resource: it returns a verdict (ja/bedingt/unklar/nein) on whether an agent-run, AI-content setup can earn on a given platform from a given country. That is clearly distinguishable from siblings like get_plan, get_platform, list_platforms, and recommend, and it even names plan-related output fields.

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?

"Use before automating anything on a platform" gives a clear trigger condition, and "never use it to bypass KYC or platform rules" sets a hard boundary. It does not, however, name an alternative sibling tool or say when NOT to run the check.

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 carries the burden and does reasonably well: it discloses that the tool is free, that it returns the full plan as Markdown, that an equivalent HTTP endpoint (GET /plans/{platform}) exists, and which platforms are supported. It omits what happens for an unsupported platform and any rate/auth constraints, but for a read-only free retrieval that gap is minor.

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 with the usage trigger, then the payload contents, then the mechanics, so the most decision-relevant information comes first. The trailing platform enumeration duplicates the schema enum and is the one mildly wasteful element.

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 steps in to describe the return format (Markdown) and the substance of the returned plan, which is exactly what an agent needs before calling. Only the semantics of preview_only and unsupported-platform behavior are left unaddressed.

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 both parameters are already documented, and the description's platform list merely echoes the schema's 'One of:' enumeration. The 'Free: returns the full plan' phrasing gestures at preview_only but never explains that the parameter restricts output to a preview, so no meaning is added 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?

States a specific verb+resource ('get the executable step-by-step plan') and enumerates the plan's contents (bot/human split, human minutes, order, ban-reason checklist, DE tax/payout notes). It also names the platform set, which lets an agent distinguish it from get_platform, list_platforms, recommend, and can_i_earn_here without opening schemas.

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?

'Use when you decided on a platform' gives a clear trigger condition and implicitly excludes the pre-decision phase, where a sibling like get_platform or recommend would apply. It never names those alternatives explicitly, so routing is inferred rather than 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 exist, so the description carries the full burden. It adds useful context — free to call, includes a check date (implying freshness/staleness), and explicitly disclaims legal/tax advice — but says nothing about what happens on an unknown id, error behavior, or that it is a pure read. Adequate but 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?

One front-loaded sentence keyed on the triggering condition, followed by a compact disclaimer. The field checklist is dense but each item conveys real payload content, so little 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, the description must convey return contents and does so thoroughly via the field enumeration. Only minor gaps remain: behavior on invalid ids and confirmation that the call is read-only.

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 id parameter is fully documented with an exhaustive value list in the schema. The description adds no extra meaning about the id beyond what the schema 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 states a specific resource (all sourced facts for one platform) and enumerates the payload: rules, human steps, KYC, fees, payout minimum/methods, VAT, ban reasons, sources, check date. It is clearly a single-platform detail fetch versus the sibling list_platforms, though it never names that sibling 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?

"Use when you already picked a platform" gives an explicit precondition that routes agents away from recommend and list_platforms toward this tool. It stops short of naming alternatives or saying 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?

With no annotations, the description carries the full burden and does reasonably well: it enumerates the fields returned per platform (AI-content rule, automation rule, merchant-of-record status, payout minimum, estimated human minutes), declares the tool is free, and disclaims legal/tax advice. It omits pagination or result-size behavior, but for a free read-only list this is solid disclosure.

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, front-loaded with the usage trigger, then the return payload, then cost and disclaimer. Nothing is padded, though the final two fragments are terse enough that they read more as bullet residue than prose.

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?

There is no output schema, so the description's enumeration of returned fields is doing necessary work, and it covers cost and legal scope. Given a one-optional-parameter list tool with no annotations, the entry is nearly complete; only pagination/volume expectations are 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 description coverage is 100%, and the single category parameter is fully documented in the schema with examples. The description only restates that results are "filtered by category" without adding format or matching semantics, 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 states a specific verb and resource — listing earning platforms for an AI agent and its human — plus the scope (filtered by category). It is clearly distinguishable from get_platform in intent, though it never names that sibling or any other to sharpen the boundary.

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?

"Use when you need an overview of platforms" gives a usable trigger condition and implies the category filter's role. However, it never says when NOT to use it, nor points to get_platform or recommend for the single-platform or recommendation cases, leaving the routing to inference.

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 substantial work: it discloses the return shape (top 3 with reasons, verdict, ban reasons, sources), the exclusion rule ('verdict nein' platforms are filtered and listed with reason), and the ranking caveat ('ranking ignores whether a plan exists'). It omits any note on auth/permissions or rate limits, keeping it from 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?

Front-loaded with the usage condition, then output shape, then filtering behavior, then cost/disclaimer. Dense sentences with no filler; every clause conveys a distinct fact an agent needs.

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?

There is no output schema, so the description takes on the burden of explaining returns and does so (top 3, reasons, verdict, ban reasons, sources, exclusion handling). Combined with the free/no-legal-advice disclaimers, an agent has everything needed to call and interpret 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 description coverage is 100%, so the schema already documents all seven parameters with examples and defaults. The description restates which inputs drive the result (goal, budget EUR, human minutes, country, skills) but adds no syntax, format, or edge-case meaning 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 states a specific verb+resource ('Returns the top 3 platforms') and is explicit about the pre-condition that separates it from siblings: 'you have not chosen a platform yet', which contrasts with get_platform/list_platforms. An agent can tell this is the discovery/recommendation tool versus the lookup 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?

It gives a clear when-to-use condition ('when your goal is to earn money with a small budget and limited human time and you have not chosen a platform yet'). It does not explicitly name alternatives (e.g. can_i_earn_here) or state when not to use it, so it stops 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.