gapup-mcp

content_evergreen_score_analyzer

Read-onlyIdempotent

Evaluates content evergreen potential for CMOs by analyzing historical traffic patterns and backlink authority. Takes a content URL and optional time range, returns an evergreen score (0-100), traffic trend analysis, and backlink profile. Ideal for content strategy planning, SEO optimization, and identifying high-value evergreen assets. Uses Wayback Machine and Common Crawl public APIs.

Input Schema

TableJSON Schema

Name	Required	Description
`url`	Yes	Content URL to analyze
`async`	No	If 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.
`toDate`	No	End date for historical analysis (YYYY-MM-DD)
`fromDate`	No	Start date for historical analysis (YYYY-MM-DD)

Output Schema

TableJSON Schema

Name	Required	Description	Default
`status`	Yes
`sources`	Yes
`lastSeen`	No
`warnings`	Yes
`firstSeen`	No
`trafficTrend`	Yes
`backlinkCount`	No
`evergreenScore`	Yes
`backlinkDomains`	No

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already declare readOnly and idempotent. Description adds that the tool uses Wayback Machine and Common Crawl public APIs, and mentions async behavior via the async parameter. This provides useful context beyond the structured annotations.

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?

Three sentences with clear front-loading: first sentence states core purpose, second lists inputs/outputs, third adds use cases and implementation details. No redundancy or filler.

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 output schema exists (not shown but known), the description adequately covers purpose, inputs, outputs, and external dependencies. No critical missing information for an agent to decide usage.

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?

With 100% schema coverage, the schema already describes all parameters. The description only restates 'URL and optional time range' without adding new details like format constraints or relationship between parameters.

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?

Description clearly states the tool evaluates content evergreen potential for CMOs using historical traffic and backlink authority. It specifies inputs (URL, optional time range) and outputs (score, trends, backlinks). This distinguishes it from sibling tools like content_audience_profile or content_discovery.

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?

Description suggests ideal use cases (content strategy planning, SEO optimization, identifying evergreen assets). It does not provide explicit when-not-to-use or alternative tools, but the context is clear enough for an AI agent to infer appropriate usage.

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

All gapup-mcp tools Inspect

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

C2.5/5.0

Disambiguation2/5

With 271 tools, many have overlapping purposes (e.g., multiple competitor intel tools, multiple financial modelers, multiple ESG auditors). Detailed descriptions help slightly, but the sheer volume creates confusion. Agents would struggle to select the right tool among many similar options.

Naming Consistency1/5

Tool names are wildly inconsistent: mix of English and French, snake_case and short phrases, some very generic (process, run, execute equivalents). No discernible naming convention (e.g., abm_architect vs. boundary_control vs. bp_narratif). This makes it hard to predict tool names.

Tool Count1/5

271 tools is far beyond typical well-scoped servers (3-15). This indicates an unfocused, over-bloated tool surface. Even for a general business intelligence server, this number is excessive and violates the principle of each tool earning its place.

Completeness2/5

Despite the large count, coverage feels scattered. Some domains (e.g., content, competitive intel) have many tools, while others (e.g., supply chain, HR) have gaps. The set lacks a coherent scope; it seems like a dump of many separate tool collections rather than a complete, curated surface.

Resources

Need Help?