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Rivalize MCP Server

by Downshift
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TypeScript
Remote

Get data freshness

get_freshness
Read-onlyIdempotent

Check data freshness for a Rivalize project: when its report was made and when each competitor was last observed and how.

Instructions

How current your Rivalize data is for one project: the date of its latest report, and for EACH tracked competitor the date Rivalize last actually observed it and how. Reading is free; this never fetches anything.

Args:

  • project_id (string, required): the project's UUID. Call list_projects first for the id.

Returns JSON: { data: { project_id, project_name, latest_report: { id, created_at, depth } | null, competitors: Competitor[], observation_kinds } }. Each Competitor: competitor_id, name, website, status ("observed" or "never_observed"), last_observed_at, last_observed_by, and observations with the newest date of each kind:

  • report_run: a report you can read analysed it and its crawl read the site (report_id names the report)

  • site_crawl: a stored crawl of its site

  • monitoring_page_capture: a monitoring check fetched one of its pages (page_type, url)

  • search_check: a monitoring search asked whether anything changed. It does not read the site, so it never sets last_observed_at. last_observed_at is the newest of report_run, site_crawl and monitoring_page_capture, and last_observed_by names which. A competitor nothing has observed is "never_observed" with null dates: no date is ever estimated or filled in, and a record's edit time is never used as an observation date. A failed crawl, a rejected report and a baseline copied from stored data are not observations.

Returns a 404 error if the project is not in your account.

Use when: "how fresh is this data?", "when did we last look at ?", or before relying on a figure, to say how old it is.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idYesProject UUID from list_projects

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.2

TDQS

A4.6/5.0
Behavior5/5

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

Goes well beyond the annotations (which already cover readOnly/idempotent/non-destructive) by disclosing that reading is free and 'this never fetches anything,' that no date is ever estimated or backfilled, that edit times are never treated as observation dates, that failed crawls / rejected reports / baseline copies are not observations, and that a 404 is returned for out-of-account projects. This is unusually rich behavioral disclosure.

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?

Front-loads the purpose, then Args, then Returns, then Use-when, so an agent can extract what it needs quickly. The Returns block is lengthy, but with no output schema it earns much of its space; a few clauses (observation kind taxonomy) are dense.

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 no output schema, the description carries the full burden and does so thoroughly: it enumerates the return shape, each competitor field, the observation-kind semantics, the status enum values, and the error behavior. Nothing an agent needs to call and interpret this correctly is missing.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3, but the description adds workflow value by specifying the parameter is 'the project's UUID' and instructing the agent to call list_projects first for the id. It adds a small amount of meaning beyond the schema.

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?

States a specific verb and resource ('how current your Rivalize data is for one project') and defines the exact scope: latest report date plus, per competitor, the date last observed and by what means. This is distinguishable from siblings like get_evidence or list_reports, which return content rather than freshness metadata.

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?

Gives explicit when-to-use triggers ('how fresh is this data?', 'when did we last look at <competitor>?', before relying on a figure) and a prerequisite ('Call list_projects first for the id'). It does not name an alternative tool or state when NOT to use it, keeping it just short of a 5.

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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