Skip to main content
Glama

mcp-knowledge

by io.github.getgapup

retail_media_attribution_bridge

Read-onlyIdempotent

Provides unified attribution insights for retail media and programmatic campaigns by analyzing MMM signals from FreeWheel Marketplace and Common Crawl. Designed for ad revenue operations teams to bridge cross-channel performance gaps. Accepts campaign IDs, date ranges, and channel filters as input. Returns structured attribution data with source provenance and confidence scores.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
asyncNoIf true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.
endDateYesEnd date for attribution window (YYYY-MM-DD)
channelsNoChannels to include in analysis
startDateYesStart date for attribution window (YYYY-MM-DD)
campaignIdsYesList of campaign identifiers to analyze
confidenceThresholdNoMinimum confidence score for included signals

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
sourcesNo
warningsNo
attributionNo

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint, openWorldHint, and idempotentHint. The description adds useful context about analyzing MMM signals and returning structured data with provenance and confidence scores, enhancing transparency without contradiction.

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 sentences, front-loaded with purpose, and avoids unnecessary words. Every sentence adds value, making it highly concise and well-structured.

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

Completeness5/5

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

Given the tool's complexity (6 params, 3 required) and the presence of an output schema, the description covers key aspects: inputs summarized, output mentioned (structured data with provenance and confidence scores), and target audience specified. It is complete for an AI agent's selection and invocation.

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

Parameters3/5

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

Schema description coverage is 100%, so the description's summary of inputs (campaign IDs, date ranges, channel filters) aligns with the schema. It does not provide additional detail beyond the schema, achieving the baseline score.

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 purpose: providing unified attribution insights for retail media and programmatic campaigns. It specifies the resources (FreeWheel Marketplace, Common Crawl) and distinguishes from siblings like programmatic_attribution_calibrator by focusing on cross-channel bridging.

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 targets ad revenue operations teams and mentions bridging cross-channel performance gaps, which implies when to use it. However, it does not explicitly state when not to use it or provide direct alternatives, though sibling tools exist.

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

All mcp-knowledge toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

C2.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially in competitive intelligence, ESG, and risk assessment. For example, there are multiple tools for competitor analysis (competitive_deep_dive, competitor_intel, competitor_moves, etc.) with unclear boundaries. Agents would struggle to select the correct tool without deep understanding of subtle differences.

Naming Consistency2/5

Tool names are a mix of English and French, and follow no consistent pattern. Some use snake_case (e.g., abm_architect, action_plan_esg), while others are verb-focused (e.g., content_catalog, fx_rate). The lack of a uniform naming convention makes it hard for agents to predict tool names.

Tool Count1/5

With 271 tools, the server is excessively large. Even for a broad knowledge domain, this number of tools makes discovery and selection inefficient. Typical coherent servers have 3-15 tools; this has an order of magnitude more, indicating poor scoping.

Completeness3/5

The tool set covers many domains (compliance, finance, marketing, HR, etc.), but the coverage is uneven due to redundancy. Key areas have multiple overlapping tools, while some sub-domains may still have gaps. Overall, the surface is broad but not well-curated.

Resources