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cloro

by dev.cloro

Scrape Perplexity

scrape_perplexity
Read-only

Submit a prompt to Perplexity from a chosen country (and optionally US state) and return the answer with cited sources. Use this to see how Perplexity 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 Perplexity.
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 readOnlyHint, openWorldHint, and destructiveHint, so the description only needed to add behavioral detail. It does: it discloses that the tool submits a live prompt, geo-targets by country/state, and returns the answer plus cited sources. This is meaningful beyond annotation hints, though it doesn't mention cost, latency, 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?

Two sentences, front-loaded with the action and result, and the second sentence adds practical use-case value. No filler or redundant detail.

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?

For a tool with no output schema, the description gives a reasonable contract: input prompt plus geo-target, output answer with cited sources. The schema fills in state/inclusion prerequisites (list_states/list_countries, include flags). It is slightly short on return-structure detail, but not enough to make it incomplete for an agent.

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 the description restates country/state only at a high level. It adds no parameter semantics beyond the schema; the detailed include flags are fully documented in the schema. Therefore the baseline score of 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 uses a specific verb ('Submit a prompt to Perplexity'), names the exact resource, and states the geo-targeting behavior and return value ('answer with cited sources'). The second sentence ('Use this to see how Perplexity answers...') reinforces what makes it distinct from sibling scraper tools, so an agent can tell it apart from scrape_chatgpt or scrape_gemini.

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?

It provides clear usage context: use this when you need Perplexity's answer and which brands/sources it mentions. It does not explicitly name alternatives or when-not conditions, but the Perplexity-specific framing plus sibling tool names is enough direction.

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