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KnockOutEZ

wigolo

by KnockOutEZ
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TypeScript
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agent

Plan queries and URLs from a natural-language prompt, fetch pages in parallel, extract structured data with JSON Schema, and synthesize results with full step transparency.

Instructions

Natural-language data gathering across sources. Plans queries + URLs from a prompt, executes in parallel, optionally extracts structured fields, synthesizes. Full step transparency.

Key parameters:

  • prompt: NL description of what to gather (e.g. "pricing for the top 5 CRM tools").

  • urls: optional seed URLs.

  • schema: optional JSON Schema — extracts matching fields from each page and merges.

  • max_pages: default 10.

  • max_time_ms: default 60000.

  • stream: progress notifications per step.

  • max_tokens_out / include_full_markdown / citation_format: budget + shape controls.

Pipeline: plan → search+fetch in parallel within budget → optional schema extraction → synthesize. steps[] exposes every action with timing. Uses MCP sampling when supported; falls back to keyword extraction otherwise.

Returns result, sources[], pages_fetched, steps[], total_time_ms, sampling_supported.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlsNoSpecific URLs to include in the data gathering
promptYesNatural-language description of what data to gather
schemaNoOptional JSON Schema -- extract structured data matching this schema from each page
streamNoSend progress notifications as each step completes
max_pagesNoMaximum pages to fetch (default 10, max 100)
max_time_msNoMaximum execution time in milliseconds (default 60000)
max_tokens_outNoToken-budget cap on total output. Uses cl100k-base BPE; non-OpenAI tokenizer counts may drift ~5-15%. When both max_tokens_out and max_chars are set, max_tokens_out wins.
citation_formatNoCitation rendering style. 'numbered' (default) inline [N] markers; 'json' returns a citations[] array; 'anthropic_tags' wraps sources in <source id='...'> tags.
include_full_markdownNoInclude full markdown body in the response. Default false on multi-result tools (returns evidence excerpts only); set true to restore.

Tool Definition Quality

A4.5/5.0
Behavior5/5

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

With no annotations, the description fully carries behavioral disclosure. It details the pipeline (plan, search, fetch, extract, synthesize), explains MCP sampling fallback, budget controls, and return fields. This is comprehensive and exceeds expectations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a clear summary, parameter list, and pipeline explanation. It is slightly long but front-loaded and efficient, with each sentence providing distinct value. Minor redundancy does not detract significantly.

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?

Despite no output schema and 9 parameters, the description covers all essential aspects: pipeline steps, return fields, parameter behaviors, and edge cases (e.g., fallback extraction, tokenizer drift). It is complete for agent understanding and invocation.

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?

All 9 parameters have schema descriptions, and the tool description adds significant value: default values, usage examples (e.g., 'pricing for top 5 CRM tools'), and detailed behavior like tokenizer drift for max_tokens_out. It goes well 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 clearly identifies the tool as a natural-language data gathering agent that plans, fetches, extracts, and synthesizes across sources. It distinguishes itself from siblings like 'fetch' or 'search' by describing a multi-step pipeline, making its purpose specific and unique.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

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

The description does not explicitly state when to use this tool versus alternatives. While it implies usage for multi-source synthesis, it lacks explicit 'when-to-use' or 'when-not-to-use' guidance, leaving the agent to infer context.

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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