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save_parser_preset

Store a generated parser under a name so it can be reused by id. Scrape later with scrape's preset_id instead of repeating the selectors, and every run is scored per field — when the recent success rate decays (the site redesigned), the preset regenerates itself from source_url and bumps a version. Give it a source_url whenever you can: without one it can never self-heal.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesA name you'll recognise, e.g. 'amazon product page'
fieldsNoThe original field descriptions, so a self-heal regenerates the same shape
parserYesThe parser to store — normally the `parser` object returned by generate_parser
renderNoThe page needs a browser render to show its content
auto_healNoRegenerate automatically on decay (default true when source_url is set)
source_urlNoPage to relearn from when the parser decays — required for self-healing

Schema Changelog

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

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Beyond the annotations, the description discloses a notable behavioral trait: every preset is scored per field, and on decay it automatically regenerates from `source_url` and bumps a version. It also warns that without `source_url` the preset can never self-heal. This adds genuinely useful runtime behavior context beyond the simple read/write hints.

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?

Three sentences, each earning its place: what the tool does, how it is used later, and the key self-healing caveat. The most actionable information (store, reuse, source_url) is front-loaded, with no filler or redundant restatement of the name or schema.

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 six parameters and nested objects, the description covers the important behavioral consequences (self-healing, version bumps, inability to heal without source_url) while the schema covers parameter details. The only minor gap is that the return value is not explicitly described, but 'reused by id' strongly implies a preset id is returned, so the description remains adequately 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%, so the baseline is 3; the schema already documents `name`, `parser`, `fields`, `render`, `auto_heal`, and `source_url`. The description adds emphasis on `source_url` for self-healing, but that is already reflected in the schema. No significant new parameter meaning is introduced beyond the schema.

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 verb and resource: 'Store a generated parser under a name so it can be reused by id.' This clearly distinguishes saving a preset from sibling tools like generate_parser, heal_parser_preset, and list_parser_presets. It also immediately conveys the post-condition (future reuse via preset_id).

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 gives clear practical context: save a parser if you want to reuse it in scrape via `preset_id` instead of repeating selectors. It also advises providing `source_url` to enable self-healing. It does not explicitly contrast with heal_parser_preset, but the intended workflow is evident enough that no exclusions are needed.

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

A4.1/5.0
Disambiguation5/5

Each tool targets a distinct resource or action: single scrape, batch scrape, crawl, search, dataset creation, parser lifecycle, proxy management, and SEO audit. Even the five status pollers are clearly differentiated by job type and their descriptions explicitly state which job they poll, so an agent can reliably select the right tool.

Naming Consistency4/5

Most names follow a verb-first pattern (create_dataset, generate_parser, run_collector, save_parser_preset, whitelist_ip) and listing tools consistently use the 'list_' prefix. However, a few are noun-first (parser_preset_stats, proxy_locations, collector_run_status) and the status polling tool for collectors breaks the otherwise consistent '<job>_status' convention ('collector_run_status' instead of 'run_collector_status').

Tool Count3/5

At 25 tools, the set is at the upper edge of the 'heavy' range. The tools all serve distinct functions, reflecting a broad platform covering scraping, crawling, search, datasets, parsers, proxies, and SEO, but the count borders on overwhelming for an agent, and some consolidation (e.g., a generic async job status endpoint) could reduce the surface.

Completeness4/5

The tool surface covers the core data-extraction lifecycle well: discovery (map, search), acquisition (scrape, batch, crawl), structured extraction (generate_parser, save_parser_preset, parser stats/heal), proxy management, and result aggregation (datasets, collectors). Notable gaps are the absence of any cancellation/abort mechanism for long-running async jobs and no way to delete a parser preset, but these are minor for most workflows.