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mambalabsdev

Publication Cadence Tracker MCP Server

by mambalabsdev

Track Publication Cadence

track_publication_cadence
Read-onlyIdempotent

Analyze a company domain to measure long-form publishing volume and trend, returning post counts, monthly average, and cadence trend (accelerating, steady, declining, dormant) with reliability flags.

Instructions

Given a company domain, measure how much long-form work that company publishes and whether the rate is rising or falling. Returns post counts for the last 30 days, 90 days and 12 months, a monthly average, and a cadence_trend of accelerating, steady, declining, dormant or unknown, plus the percent change behind it. The trend compares the last 90 days against the prior 275 days, both normalized to posts per month. Also returns the blog URL, the format mix (blog posts, guides, reports, case studies, whitepapers, podcasts, videos, press releases, research), the number of distinct bylines, and how the post list was discovered. This measures EDITORIAL output volume, not product changelogs: a release feed is detected and rejected rather than counted. Publication dates are read from the post pages themselves, because sitemap lastmod was measured to be a modification date that runs later than publication by a median of 151 to 1653 days. When a site's date field turns out to track edits rather than publication, date_source_reliable comes back false and every count is nulled rather than reported wrong, so check that field before quoting a number. Counts are a census when the archive fits the page budget and a scaled even sample otherwise, flagged by counts_are_estimate. Public sitemaps, feeds and pages only. Returns flat Clay-ready JSON. Read-only; requires an APIFY_TOKEN and consumes Apify credits per domain analyzed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainNoA single company domain, e.g. zapier.com. Provide either domain or domains.
domainsNoBatch mode: several company domains analyzed in one call. Takes precedence over domain.
batchSizeNoHow many domains to analyze concurrently. Default 2.
skipCacheNoForce a fresh crawl and ignore the 3 day result cache.
max_pages_to_dateNoHow many post pages to fetch per domain for dating. Default 400. Above this cap the counts are estimated from an even sample across the archive and counts_are_estimate is set true. Raise it for a tighter number on a large archive, at the cost of run time.
domain_time_budget_msNoHard wall-clock ceiling per domain, default 75000. When it is nearly spent the crawl stops and the row is returned with partial_result true and reduced confidence rather than timing out.
Behavior5/5

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

The description goes far beyond the readOnlyHint and idempotentHint annotations. It explains why publication dates are read from post pages rather than sitemap lastmod (with a specific median lag), what happens when dates track edits (date_source_reliable=false and counts nulled), how counts can be a census or an even sample (counts_are_estimate), the requirement for APIFY_TOKEN, and that it consumes credits per domain. This is rich, non-obvious behavioral context that an agent needs to interpret results correctly.

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 long but packed with unique value: every sentence introduces a caveat, metric, or constraint. It is front-loaded with the core purpose and then systematically covers reliability, counting methodology, and operational details (token, credits). While it could be trimmed slightly, the density is justified for a tool with no output schema and high behavioral nuance.

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?

With 6 parameters, no output schema, and no sibling tools, the description carries the full burden of explaining return values, edge cases, and operation constraints. It covers the returned fields (post counts, cadence_trend, format mix, bylines, discovery method), reliability flags, counting methodology, and prerequisites (APIFY_TOKEN). This is a complete picture for an agent to invoke the tool and interpret results 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?

The schema description coverage is 100%, so the parameters are already well-documented. The description adds a bit of context (e.g., effect of max_pages_to_date on sampling, domain_time_budget_ms partial_result behavior) but mostly reinforces what the schema already states. The baseline of 3 is appropriate because the schema does the heavy lifting and the description does not introduce significant new semantics.

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 first sentence states a specific verb and resource: 'measure how much long-form work that company publishes and whether the rate is rising or falling.' This clearly distinguishes the tool's purpose, even without sibling tools to compare, and immediately orients the agent to what it does.

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 explicitly excludes product changelogs ('This measures EDITORIAL output volume, not product changelogs'), warns to check the date_source_reliable field before quoting numbers, and limits usage to 'Public sitemaps, feeds and pages only.' No alternatives are mentioned because no sibling tools exist, so the guidance is clear and provides actionable context.

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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