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cloro

by dev.cloro

Scrape Microsoft Copilot

scrape_copilot
Read-only

Submit a prompt to Microsoft Copilot from a chosen country (and optionally US state) and return the answer with cited sources. Use this to see how Microsoft Copilot answers a prompt and which brands/sources it mentions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stateNoOptional state code for state-level targeting (e.g. "CA" when country is "US"). Only some countries support this — call list_states for the supported countries and their codes.
promptYesThe prompt to submit to Microsoft Copilot.
countryYesISO 3166-1 alpha-2 country code to geo-target the request from (e.g. "US"). Use list_countries to see supported codes per model.
includeNoOptional flags for heavier payload fields, each off by default: markdown (the answer rendered as markdown), html (the answer page HTML), rawResponse (the engine's unprocessed response payload). Leave unset for the leanest response.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedInput schema / properties / include / description
      Previous value: -"Optional flags to include heavier payload fields in the response. Leave unset for the leanest response."New value: +"Optional flags for heavier payload fields, each off by default: markdown (the answer rendered as markdown), html (the answer page HTML), rawResponse (the engine's unprocessed response payload). Leave unset for the leanest response."
  2. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already cover the safe read-only nature, so the description adds useful behavioral context beyond them: the tool geo-targets from a country/US state and returns cited sources, including brand mentions. This clarifies what the agent should expect from the response without contradicting the annotations.

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 two concise sentences, front-loaded with the core action and purpose. Every clause adds useful information: what it does, how it geo-targets, what the output contains, and why you would use it. There is zero filler.

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 no output schema, the description does a good job of mentioning the main return value ('answer with cited sources'). It also implies the response will reflect brand/source mentions. It doesn't describe the exact response structure, but the parameter schema covers the request side and the annotations clarify safety, so the overall picture is fairly complete.

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% and each parameter is already well-documented, including the nested 'include' object. The description does not add significant meaning beyond the schema — the only additional hint is 'optionally US state', which the schema already states. 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?

The description opens with a specific action ('Submit a prompt to Microsoft Copilot'), targets a clear resource, and explains the output ('answer with cited sources'). It also distinguishes this tool from sibling scraping tools by naming the model and the unique value of seeing which brands/sources Copilot mentions.

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

Usage Guidelines4/5

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

The description explicitly states the intended use case: 'Use this to see how Microsoft Copilot answers a prompt...' This tells the agent when to choose this tool. It does not explicitly discuss alternatives or exclusion criteria, but the sibling tool names (scrape_chatgpt, scrape_gemini, etc.) make the choice obvious.

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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TDQS

A4.3/5.0
Disambiguation5/5

list_countries and list_states are clearly metadata helpers, while each scrape_* tool targets a specific engine or search vertical. Even the Google-family tools are differentiated by output type: organic results, AI Mode answers, and news.

Naming Consistency5/5

All tools use snake_case verb_noun naming: list_* for metadata and scrape_* for engine-specific operations. Longer names like scrape_google_ai_mode still follow the same pattern with no mixed conventions.

Tool Count5/5

With 10 tools, the set is well-scoped and each tool earns its place. The two list tools support the eight distinct scraping targets without unnecessary redundancy.

Completeness4/5

The core workflow—choose a country/state and scrape an engine—is well covered across major AI engines and Google verticals. However, there is no way to enumerate supported engine models even though list_countries accepts a model parameter, and some obvious Google verticals like images or shopping are absent.

Resources