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bzsasson

Screaming Frog SEO Spider MCP Server

read_crawl_data

Read-only

Read crawl data from exported CSV files. Filter rows, paginate, and select specific columns to analyze website crawl results.

Instructions

Read CSV data from an export. Use after export_crawl.

Args: export_id: The export_id from export_crawl file: CSV filename to read (from the file list in export_crawl output) limit: Max rows to return (default 100, max 1000) offset: Number of rows to skip (for pagination) filter_column: Optional column name to filter by filter_value: Optional value to match in the filter column filter_mode: How to match filter_value: "contains" (default, case-insensitive substring), "exact" (case-insensitive exact match), or "regex" (Python regex) columns: Optional comma-separated column names to return. Wide exports (Internal:All has dozens of columns) flood the context; request just the ones you need, e.g. "Address,Status Code".

Returns: CSV data as formatted text with column headers.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
export_idYes
fileYes
limitNo
offsetNo
filter_columnNo
filter_valueNo
filter_modeNo
columnsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

The description aligns with the readOnlyHint annotation and adds significant behavioral details: pagination (limit, offset), filtering options, and a warning about wide exports flooding context. This goes well beyond what annotations provide.

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 and well-structured: an introductory sentence, a clear list of parameters, and a returns note. Every sentence is informative, and the information is front-loaded.

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?

The description covers all parameters, output format, and usage prerequisite. It provides enough context for an AI to correctly invoke the tool, including handling of wide exports and pagination, making it complete for a data reading tool.

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

Parameters5/5

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

With 0% schema description coverage, the description fully compensates by explaining all 8 parameters, including defaults, constraints (e.g., limit max 1000), and filter modes. It adds meaning that the schema alone lacks.

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 reads CSV data from an export, using the verb 'Read' and specifying the resource. It distinguishes from siblings like export_crawl (which creates the export) and aggregate_crawl_data (which processes data), making the 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 Guidelines4/5

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

The description explicitly says 'Use after export_crawl,' providing a clear prerequisite. It also describes parameters like filter modes and pagination, guiding usage. However, it does not explicitly mention when not to use or alternatives, missing a small opportunity for deeper guidance.

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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