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clamp-sh

Clamp Analytics MCP Server

Official

traffic.timeseries

Read-only

Track website metrics over time with hourly, daily, weekly, or monthly buckets. Choose from pageviews, visitors, sessions, bounce rate, or custom events.

Instructions

Traffic metrics over time as date buckets. Returns [{ date, count }] sorted ascending, where count is the chosen metric's value per bucket. By default charts pageview counts; set metric to chart unique visitors, sessions, bounce rate, or average session duration instead — the same KPIs as traffic.overview, broken out over time. Alternatively set event to count occurrences of a custom event. Granularity is automatic based on period length (hourly for ≤2 days, daily for ≤90 days, weekly for ≤365 days, monthly beyond) and can be overridden via granularity.

Examples:

  • "pageview trend last week" → period="7d"

  • "unique visitors per day this month" → metric="visitors", period="30d"

  • "is bounce rate climbing?" → metric="bounce_rate", period="90d"

  • "signups per day this month" → event="signup", period="30d", granularity="day"

  • "hourly pageviews yesterday" → period="1d", granularity="hour"

Limitations: metric and event are mutually exclusive — when metric is set it always scopes to pageviews and ignores event/property/value. bounce_rate is a percentage (e.g. 47.2), avg_duration is in seconds. Forcing granularity="hour" over a 90-day period produces hundreds of buckets and may be truncated server-side. Buckets with no matching events return zero (the series does not skip missing dates).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idNoTarget project ID (e.g. "proj_abc123"). Required when the credential has access to multiple projects. If omitted and only one project is accessible, that project is used automatically. Call `projects.list` to discover available project IDs.
periodNoTime period. Use "today", "yesterday", "7d", "30d", "90d", or a custom range as "YYYY-MM-DD:YYYY-MM-DD" (e.g. "2026-01-01:2026-03-31"). Defaults to "30d".
metricNoWhich traffic metric to chart over time, mirroring the overview KPIs: "pageviews", "visitors" (unique), "sessions", "bounce_rate" (% of single-pageview sessions), or "avg_duration" (avg session length in seconds). When set, the metric is always scoped to pageviews and `event`/`property`/`value` are ignored. Omit to chart raw counts of a custom `event` instead.
eventNoEvent name to chart raw counts of (defaults to "pageview"). Ignored when `metric` is set. Use any custom event name to see its trend over time.
granularityNoOverride the automatic granularity. "hour" for hourly buckets, "day" for daily, "week" for weekly, "month" for monthly. If omitted, chosen automatically based on the period length.
propertyNoFilter by this custom property key (used with value).
valueNoFilter to events where the property key equals this value.
pathnameNoFilter to a specific page path (e.g. "/pricing", "/blog/my-post"). Must start with /.
utm_sourceNoFilter by UTM source (e.g. "google", "twitter", "newsletter"). Case-sensitive, must match the value in the tracking URL.
utm_mediumNoFilter by UTM medium (e.g. "cpc", "email", "social"). Case-sensitive.
utm_campaignNoFilter by UTM campaign name (e.g. "spring-launch", "product-hunt"). Case-sensitive.
utm_contentNoFilter by UTM content (e.g. "hero-cta", "sidebar-banner"). Case-sensitive.
utm_termNoFilter by UTM term (e.g. "running+shoes"). Case-sensitive.
referrer_hostNoFilter by referrer hostname (e.g. "news.ycombinator.com", "twitter.com", "github.com"). Use this to see what traffic from a specific source did. Must match the value returned by `traffic.breakdown(dimension="referrer_host")` exactly (lowercase, no protocol or path).
countryNoISO 3166-1 alpha-2 country code, uppercase (e.g. "US", "GB", "DE", "NL", "JP"). Filter results to visitors from this country.
regionNoAdministrative region inside a country (e.g. "California", "Bavaria"). Case-sensitive; must match the stored region exactly. Use traffic.breakdown(dimension="region") to discover values.
cityNoCity name (e.g. "San Francisco", "London"). Case-sensitive; must match the stored value. Use traffic.breakdown(dimension="city") to discover values.
device_typeNoDevice category. One of: "desktop", "mobile", "tablet".
browserNoBrowser family (e.g. "Chrome", "Safari", "Firefox"). Use traffic.breakdown(dimension="browser") to discover the exact stored values.
browser_versionNoBrowser version string (e.g. "120.0"). Case-sensitive.
osNoOperating system family (e.g. "macOS", "iOS", "Windows", "Android"). Use traffic.breakdown(dimension="os") to discover stored values.
os_versionNoOS version string (e.g. "14.2"). Case-sensitive.
channelNoTraffic channel. One of: "direct", "organic_search", "organic_social", "paid", "email", "referral".

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
seriesYes
Behavior5/5

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

Beyond the readOnlyHint annotation, the description adds critical behavioral details: automatic granularity adjustment, mutual exclusivity of metric and event, truncation risk for large periods, and that missing dates return zero. No contradictions.

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 well-structured: first sentence defines purpose, then explains options, then provides 5 examples, then lists limitations. Every sentence earns its place with no redundancy.

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 (23 parameters, no required params, output schema exists), the description covers all necessary context: parameter relationships, usage patterns, limitations, and examples. It is fully complete.

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

Parameters5/5

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

Despite 100% schema coverage, the description adds substantial context: explains interaction between metric and event, provides examples for each parameter, clarifies that metric scopes to pageviews and ignores event, and gives format hints for bounce_rate and avg_duration.

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: 'Traffic metrics over time as date buckets.' It specifies the return format and distinguishes from siblings by noting it breaks out the same KPIs as traffic.overview over time.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance: examples for common use cases, mutual exclusivity of metric and event, automatic granularity logic, and limitations about truncation and zero buckets. It tells the agent when to use metric vs event.

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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