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competlab

competlab-mcp-server

by competlab

get_pricing_history

Read-only

Retrieve paginated history of pricing monitoring runs for a project to track competitor price changes over time. Nulls mark runs that could not be analyzed, not zero.

Instructions

Get paginated history of Pricing Intelligence monitoring runs. Check pagination.hasMore to fetch additional pages. Same summary shape and same reading rule as get_pricing_dashboard: a null is a run whose pricing we could not analyze, never a zero or a no. Do not read a run-to-run change in a null field as a competitive event.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoPage number (1-indexed, default: 1)
limitNoItems per page (default: 20, max: 100)
projectIdYesProject ID (from list_projects)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv4.0.1
    • removedInput schema / additionalProperties
      Removed value: -false
    • addedInput schema / properties / page / maximum
      Added value: +9007199254740991
  2. First observedv1.0.0

TDQS

A3.9/5.0
Behavior4/5

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

Beyond the readOnlyHint/openWorldHint annotations, the description discloses meaningful data semantics: a null means an unanalyzable run rather than zero, and null changes should not be read as competitive events. This is valuable interpretive context. It does not describe rate limits or output structure, but the added interpretation warrants a 4.

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?

Three front-loaded sentences with essentially no waste: purpose first, pagination behavior second, then the reading rule. Slightly dense but every sentence earns its place.

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 no output schema, the description compensates by explaining the pagination signal (pagination.hasMore) and how to interpret null fields, which an agent needs to call and read results correctly. It stops short of describing the full summary shape but references get_pricing_dashboard for that, which is reasonable.

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 coverage is 100% and each of the three parameters (page, limit, projectId) is fully documented in the schema. The description mentions pagination but adds no parameter syntax or format detail beyond the schema, so baseline 3 is appropriate.

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 (Get) and resource (paginated history of Pricing Intelligence monitoring runs), which cleanly separates it from siblings like get_pricing_dashboard and get_pricing_run_detail. An agent can identify its role without opening the schema.

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

Usage Guidelines3/5

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

The description implies usage by referencing get_pricing_dashboard's reading rule and explaining pagination with pagination.hasMore, giving practical fetch guidance. However, it never explicitly states when to choose this tool over get_pricing_run_detail or the dashboard, leaving the alternative selection to inference.

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