mcp-knowledge

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

A4.1/5.0

Behavior4/5

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

Annotations already indicate readOnlyHint=true, idempotentHint=true, openWorldHint=true. The description adds behavioral context by specifying that it uses Wayback Machine and Common Crawl public APIs, and that it returns a score and analysis. No contradictions, and the extra details are useful.

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 concise at four sentences, with no wasted words. The primary action and key details are front-loaded. Every sentence adds value.

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 existence of an output schema, the description adequately covers purpose, inputs, outputs, data sources, and use cases. It is sufficiently complete for an agent to understand and invoke the tool correctly.

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?

Input schema has 100% coverage, so the baseline is 3. The description only summarizes parameters ('takes a content URL and optional time range') without adding new details or constraints beyond what the schema provides. No extra semantics are given.

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

Purpose4/5

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

Description clearly states it evaluates content evergreen potential using historical traffic and backlink authority. It specifies the target user (CMOs) and the outputs (evergreen score, trends, backlink profile). However, it does not explicitly differentiate from sibling tools that may analyze content, such as content_audience_profile or content_ranking.

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 clear context for when to use the tool: 'Ideal for content strategy planning, SEO optimization, and identifying high-value evergreen assets.' This gives agents a good sense of appropriate use cases, but it does not list exclusionary criteria or alternative tools.

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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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.

content_evergreen_score_analyzer

Input Schema

Output Schema

TDQS

Glama MCP Gateway

TDQS

Resources