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Bing Webmaster Tools MCP

Performance by query

get_query_stats
Read-only

Retrieves clicks, impressions, CTR, and average position per search query in Bing to analyze keyword performance and guide SEO decisions.

Instructions

Clicks, impressions, CTR and average position per search query in Bing. Bing keeps weekly data for recent months; by default rows are summed per query over the period.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum rows to return (default 50, max 200). Prefer filters or aggregation over large limits
sortByNoSort order (default: clicks)
endDateNoEnd date, YYYY-MM-DD (optional)
siteUrlYesSite URL exactly as verified in Bing Webmaster Tools (e.g. https://example.com/). Call list_sites if unsure.
containsNoOnly rows whose query/URL contains this text (case-insensitive)
aggregateNotrue (default): one row per item summed over the period. false: one row per item per week
startDateNoStart date, YYYY-MM-DD (optional)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=true, so safety is covered. The description adds genuinely useful context beyond that: Bing retains only weekly data for recent months, and the default return shape is one row per query summed over the period. It does not mention rate limits or row/size caps, which the limit param hints at.

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 short sentences, front-loaded with the returned metrics, with no redundant or filler content. Every clause earns its place by conveying either output shape or data-availability behavior.

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 usefully enumerates the returned fields and discloses the weekly retention window and default aggregation. That is nearly sufficient for correct invocation; pagination is only implicitly handled via the schema's limit parameter.

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%, so all seven parameters are documented in the schema itself. The description restates the default aggregation behavior ('rows are summed per query over the period'), which mirrors the aggregate param description rather than adding new syntax or format detail. Baseline 3 is appropriate.

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 the exact metrics returned (clicks, impressions, CTR, average position) and the resource granularity (per search query in Bing), which cleanly distinguishes it from get_page_stats and get_queries_for_page. It never names a sibling explicitly, so differentiation is implicit rather than stated.

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 the tool's role (query-level metric retrieval) and notes data retention and default summing, but gives no explicit when-to-use vs alternatives guidance (e.g. when to prefer get_query_trend or get_page_stats). Usage is inferable but not directed.

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