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Scrapeer

Scrapeer MCP Server

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by Scrapeer

scrapeer_validate_flow

Read-onlyIdempotent

Dry-run validate a flow before saving to ensure gateway acceptance. Returns errors and warnings; errors block save, warnings are advisory.

Instructions

Dry-run validate a flow without saving it. USE THIS TOOL WHEN: you've constructed a flow and want to check whether it will be accepted by the gateway before calling scrapeer_create_flow / scrapeer_update_flow. Returns { ok, errors[], warnings[] }. Errors block save; warnings are advisory. DO NOT USE: to validate a stored flow (no persistent flow lookup - pass the flow JSON directly).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
flowYesReactFlowJSON-style flow payload - the same shape returned by scrapeer_get_flow. Use scrapeer_validate_flow to dry-run before writing.
Behavior5/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, but the description adds valuable context: it doesn't persist, returns { ok, errors[], warnings[] }, and explains that errors block save. This goes beyond the structured data without contradicting it.

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 compact, front-loaded with the core purpose, and uses clear signposting (USE WHEN, DO NOT USE, returns). Every sentence contributes real value without fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter validation tool, the description covers what it does, when to use it, what it returns, and how errors/warnings behave. No output schema exists, but the return format is explicitly stated, making it complete for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already provides 100% coverage with detailed descriptions for nodes, edges, and defaults. The description adds a useful cross-reference ('same shape returned by scrapeer_get_flow') and emphasizes the dry-run usage, slightly enhancing the schema's meaning.

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 ('validate') and resource ('flow') with the scope 'dry-run without saving it', clearly distinguishing it from create, update, and run tools. It also names sibling tools for context, making its purpose unambiguous.

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?

Explicit guidance is provided: 'USE THIS TOOL WHEN' for pre-save checks, and 'DO NOT USE' for stored flows, with alternatives named (scrapeer_create_flow / scrapeer_update_flow). This leaves no ambiguity about when to invoke it.

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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