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BoxLab-Ltd

yandex-metrica-mcp

by BoxLab-Ltd

Describe Metrica counter

describe_counter
Read-only

Fetch a Yandex Metrica counter's configuration sections—settings, goals, segments, filters, operations, grants—and extract goal IDs needed for conversion metrics. Specify sections with include.

Instructions

Read a single counter's configuration — settings, goals, segments, filters, operations and access grants — choosing sections with include. The goals section returns the goal ids needed to build conversion metrics (ym:s:goalreaches / conversionRate) in run_report, so call this with include=["goals"] before any conversion question. To list the account's counters, use get_metadata. Read-only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
includeNoWhich configuration sections to return. Default ["settings","goals"]. "goals" gives the goal ids needed for conversion metrics in run_report; "grants" is a read-only access audit.
counterIdNoYandex Metrica counter id. Optional if YANDEX_METRIKA_COUNTER_ID is configured.
fullResponseNoIf true, return the full raw objects with every field. Default false: a curated subset to save context.
Behavior4/5

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

Annotations already provide readOnlyHint=true, and the description adds context beyond that: the goals section returns IDs needed for conversion metrics in run_report, and sections are selectable via include. No contradiction with annotations. It does not describe return format, but the read-only safety is covered.

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?

Three concise sentences: the first states purpose, the second gives a specific use case and ties to run_report, the third distinguishes from get_metadata. Every sentence earns its place; no fluff.

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?

Given the tool's complexity and rich schema/annotations, the description is nearly complete: it covers purpose, selectable sections, a conversion-metric use case, and the sibling alternative. The only minor gap is not describing the return structure, but the included sections and schema cover the essentials.

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 baseline is 3. The description reiterates the include parameter's role ('choosing sections with include') and the goals purpose, but adds no new meaning beyond what the schema's parameter descriptions already state.

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 opens with 'Read a single counter's configuration' — a specific verb, resource, and scope. It enumerates the sections (settings, goals, segments, filters, operations, access grants) and distinguishes from get_metadata for listing counters.

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 explicitly states when to use this tool: 'call this with include=["goals"] before any conversion question' and points to an alternative: 'To list the account's counters, use get_metadata.' This gives clear context and exclusions.

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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