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shinypebble

microsoft-ads-mcp

by shinypebble

estimate_keyword_bids

Read-only

Get estimated first-page bids for keywords, including min bid, average CPC, CTR, and weekly performance ranges, to inform bid optimization in Microsoft Advertising.

Instructions

Estimate the bid to reach the first page (or mainline) for keywords -- the "estimated first page bid" from Keyword Planner.

For each keyword, returns one estimate per match type: estimated_min_bid (the headline first-page/mainline bid) plus modeled average CPC, CTR, and weekly clicks/impressions/cost ranges. Estimates are account-scoped and may be null where Microsoft has no data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keywordsYesKeyword texts to price, e.g. ["running shoes", "trail running shoes"].
languageNoLanguage name for the estimate, e.g. "English" (optional).
match_typesNoSubset of ["Broad", "Phrase", "Exact"]; defaults to ["Exact"].
location_idsNoMicrosoft location ids to scope demand to (optional).
currency_codeNoISO currency for the bids (e.g. "USD"); defaults to the account currency.
target_positionNo"FirstPage" (default), "MainLine", or "MainLine1" (top ad slot).FirstPage

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

The description adds value beyond the readOnlyHint annotation by detailing that estimates are per-match-type with modeled metrics, account-scoped, and may be null. It does not contradict annotations. Could mention more about data sourcing or rate limits.

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?

The description is concise with four sentences, front-loading the purpose and efficiently covering output, scoping, and nulls. Every sentence adds value with no redundancy.

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 has 6 parameters, output schema, and annotations, the description covers the main behavioral aspects (return structure, scoping, nulls). It is slightly incomplete regarding how to interpret estimates or handle edge cases, but overall adequate.

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 parameters are already well documented. The description adds an example for keywords but does not provide additional semantics beyond what the schema offers, meeting the baseline.

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 clearly states the tool estimates the bid to reach first page or mainline for keywords, using a specific verb and resource. It is distinct from siblings like 'get_keyword_traffic_estimates' which estimate traffic, and 'check_first_page_bids' which checks current bids, though it does not explicitly differentiate.

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 when to use (to estimate bids for first page/mainline) but does not provide explicit guidance on when not to use it or how it compares to similar siblings like 'check_first_page_bids'. The context is clear but lacks exclusions or alternative recommendations.

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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