Fetch a Collector run by run_id: status (queued|running|done|failed), result count, cost, partial flag and the result rows. Use after run_collector returned 202/async. Pass format 'csv' to get the rows as CSV text.
| Name | Required | Description | Default |
|---|---|---|---|
| format | No | Return rows as JSON (default) or CSV text | |
| run_id | Yes | The run id returned by run_collector |
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?
Annotations already declare readOnlyHint and idempotentHint, so the description only needs to add async/status context. It does: 'Use after run_collector returned 202/async' plus the list of status values and the partial flag, which tells the agent this is a polling/read operation returning progress information. No contradiction.
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?
Two tightly written sentences put the core action first, list the return fields compactly, and add only the workflow trigger and format option. No filler or unnecessary repetition.
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?
Covered: when to call, statuses, result fields, format switch, and safety profile via annotations. Without an output schema, it could say a bit more about the meaning of the partial flag and failure/not-found behavior, but for a simple async status fetch it is nearly complete.
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?
The schema describes both parameters at 100% coverage, so the description need not re-document them. It does clarify the csv format behavior, but that mostly mirrors the schema's 'Return rows as JSON (default) or CSV text' description.
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?
Description starts with a specific verb and resource: 'Fetch a Collector run by run_id', then enumerates exactly what is returned: status, result count, cost, partial flag, and result rows. This distinguishes it from run_collector, which launches the run, and from other status tools by naming the Collector-specific resource.
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?
Explicitly says to use this tool 'after run_collector returned 202/async', tying it to the run_collector workflow. It doesn't name exclusions or alternatives, but the async handoff condition is clear enough for an agent to choose this over launch tools.
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.
