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bobberrisford

affiliate-networks-mcp

affiliate_clickbank_list_clicks

List recent ClickBank affiliate clicks, optionally filtered by programme and date, to debug traffic and see if links are being clicked. Compare clicks to conversions by pairing with transaction data.

Instructions

List recent affiliate clicks on ClickBank, optionally filtered by programme and date. Use this for traffic-side debugging — e.g. "are my links being clicked at all?" or "where is traffic going on ClickBank?". Returns Click records; pair with list_transactions to compare clicks vs conversions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toNo
fromNo
limitNo
cursorNo
offsetNo
programmeIdNo
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only adds 'recent' and 'Returns Click records' — it does not mention auth requirements, default date ranges, pagination behavior (cursor/offset/limit), ordering, or rate limits. For a tool with zero annotation coverage, this is insufficient behavioral context.

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

Conciseness4/5

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

The description is compact at roughly 75 words across 3-4 sentences, front-loaded with the core purpose, and uses quoted examples efficiently. No filler or redundancy; the only slight inefficiency is having two similar debugging examples, but both add practical value.

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

Completeness2/5

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

Given the tool's complexity (6 optional params, 0% schema coverage, no annotations, no output schema), the description is incomplete. It covers the what/when/return-type well but omits parameter details, pagination defaults, and behavioral guarantees needed for correct invocation. The pairing hint with list_transactions is valuable but does not compensate for the missing operational specifics.

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

Parameters2/5

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

Schema description coverage is 0% and the description compensates only nominally by mentioning 'filtered by programme and date'. It fails to explain the format or semantics of the 6 parameters (e.g., date format for to/from, meaning of limit, cursor, offset, or programmeId). With no parameter info in either the schema or description, agents cannot reliably construct correct arguments.

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

Purpose5/5

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

The description opens with a specific verb+resource+scope: 'List recent affiliate clicks on ClickBank, optionally filtered by programme and date.' It distinguishes from sibling tools by explicitly stating it returns 'Click records' and referencing list_transactions for conversion comparison, clearly separating click data from transaction data.

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

Usage Guidelines4/5

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

The description provides concrete use cases for traffic-side debugging ('are my links being clicked at all?', 'where is traffic going on ClickBank?') and names the alternative tool list_transactions for comparing clicks vs conversions. However, it lacks an explicit when-not-to-use statement, relying on implied differentiation.

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

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