Regenerate a preset's selectors now (the manual trigger for the automatic repair). Refetches the source page and adopts new selectors ONLY if they extract more than the current ones — a heal that finds nothing better leaves the preset untouched and is not billed.
| Name | Required | Description | Default |
|---|---|---|---|
| force | No | Bypass the cooldown between heals | |
| preset_id | Yes | The preset id |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description goes well beyond the annotations by disclosing that the tool refetches the source page, only adopts new selectors if they extract more than current ones, leaves the preset untouched otherwise, and is not billed in that case. This is meaningful behavioral context that the annotations (readOnlyHint false, destructiveHint false, openWorldHint true) do not provide.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that front-loads the action and then packs in the key behavioral, conditional, and billing details. Every clause earns its place with no repetition or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a two-parameter mutation tool with full schema coverage, the description explains the trigger, the refetch behavior, the acceptance condition, and the billing consequence. No output schema exists, but the description sufficiently conveys what will happen for an agent to decide and invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so both `preset_id` and `force` are already documented in the input schema. The description does not add anything about parameter meanings beyond the schema, so it does not need to compensate. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description starts with a specific verb and resource: 'Regenerate a preset's selectors now'. It clarifies this is the manual trigger for the automatic repair, which distinguishes it from any automatic repair process and from sibling tools like save_parser_preset or generate_parser. The `heal` framing and the condition about adopting selectors only if they extract more makes the tool's unique role unmistakable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by calling this 'the manual trigger for the automatic repair', so an agent can infer it is for manually forcing a repair that would otherwise happen automatically. However, it does not explicitly state when to prefer this over alternatives, nor does it mention any conditions or exclusions beyond the improvement check.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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').
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.
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.
