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bobberrisford

affiliate-networks-mcp

affiliate_awin-advertiser_list_clicks

List recent Awin advertiser clicks, optionally filtered by programme and date, to debug traffic and check if your affiliate links are being clicked. Compare with conversions.

Instructions

List recent affiliate clicks on Awin (advertiser), 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 Awin (advertiser)?". Returns Click records; pair with list_transactions to compare clicks vs conversions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toNo
fromNo
brandYes
limitNo
cursorNo
offsetNo
programmeIdNo
Behavior3/5

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

With no annotations, the description carries the burden of disclosing behavior. It correctly implies a read-only operation by using 'List' and stating 'Returns Click records', which is helpful. However, it does not explain pagination behavior (cursor/offset), default date ranges, or response structure, leaving gaps in behavioral understanding.

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

Conciseness5/5

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

The description is three compact sentences, front-loaded with the primary action and filters. Every sentence earns its place by adding use-case context or pairing guidance. It is neither verbose nor under-specified.

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

Completeness3/5

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

Given no output schema, no annotations, and 7 parameters, the description provides the core purpose and a useful pairing hint, but lacks details on pagination, date range semantics, or required parameters. It is adequate for a simple list tool but not fully complete for a robust agent decision.

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%, so the description must compensate. It adds meaning for 'programme' (programmeId) and 'date' (from/to), but does not explain 'brand' (required), 'limit', 'cursor', or 'offset'. For a tool with 7 parameters, this is insufficient guidance for correct invocation.

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 clearly states the tool's function: 'List recent affiliate clicks on Awin (advertiser)' with specific filter options (programme, date). It distinguishes itself from sibling tools by explicitly noting the advertiser perspective and framing the use case around traffic-side debugging. The examples ('are my links being clicked at all?') further clarify intent.

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 a clear use case ('traffic-side debugging') and complementary tool ('pair with list_transactions to compare clicks vs conversions'), which helps the agent choose when to use it. It lacks explicit when-not-to-use guidance or direct alternatives, but the context is strong enough to avoid misuse.

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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