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alialtunar

pricing-time-machine-mcp

by alialtunar

pricing_timeline

Read-onlyIdempotent

Track how a pricing page's prices changed over time by reading archived copies, grouping identical price sets into periods, and listing added or removed plans.

Instructions

How a pricing page's prices changed over the years. Reads points archived copies spread across the range, groups identical price sets into periods and lists what was added/removed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesPricing page, e.g. 'notion.so/pricing' or 'https://slack.com/pricing'.
pointsNoHow many copies to read across the range (more = slower).
to_yearNoOptional year bound, e.g. 2019.
from_yearNoOptional year bound, e.g. 2019.
response_formatNo'markdown' (default) or 'json'.markdown

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive and open-world, so safety is covered. The description adds real behavioral context the annotations lack: it reads `points` archived copies across the range, groups identical price sets into periods, and reports additions/removals – telling the agent what the call actually does and roughly what it returns.

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?

Two tight sentences with the purpose front-loaded and the mechanism second; no filler, no repetition of the name or schema fields beyond what is needed for orientation.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With an output schema present, the description needn't detail return values, and it still sketches the output shape (periods, added/removed). For a 5-parameter read tool the coverage is nearly complete, with the only gap being any hint of cost/latency tradeoffs beyond the points note.

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?

Schema description coverage is 100%, so every parameter (url, points, from_year, to_year, response_format) is already documented with examples and constraints. The description restates the `points` sampling behavior ('more = slower') which the schema already conveys, adding no new semantics.

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?

States a specific resource (a pricing page's prices) and the temporal scope (changed over the years), so the agent can distinguish it from a point-in-time tool. However it never names or contrasts the siblings pricing_snapshots, pricing_at, or compare_pricing, leaving the timeline/at-a-moment distinction to be inferred.

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

Usage Guidelines2/5

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

There is no explicit when-to-use or when-not-to-use guidance, and no sibling alternative is mentioned. The second sentence explains the mechanism (reads archived copies, groups into periods) rather than telling the agent when this tool is the right choice over pricing_at or pricing_snapshots.

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