Skip to main content
Glama
KnockOutEZ

wigolo

by KnockOutEZ
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Local

research

Decomposes complex questions into sub-queries, searches in parallel, fetches sources, and produces a cited report with key findings and gaps.

Instructions

Multi-step research on a complex question. Decomposes into sub-queries, searches in parallel, fetches sources, synthesizes a cited report. Beats chaining search + fetch manually for multi-source synthesis.

Key parameters:

  • question: the research question.

  • depth: 'quick' (~15s, 2 sub-queries) | 'standard' (~40s, 4 sub-queries, default) | 'comprehensive' (~80s, 7 sub-queries).

  • max_sources: override per-depth source count.

  • include_domains / exclude_domains: scope.

  • schema: optional JSON Schema — structures the report.

  • stream: progress notifications per phase.

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

Returns report (markdown with [N]), citations[], sources[], sub_queries[], depth, total_time_ms, sampling_supported, and brief with topics, highlights, key_findings, sections (overview.cross_references, comparison, gaps — gaps lists any named sub-entity research could not corroborate).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
depthNoResearch depth: quick (~15s), standard (~40s, default), comprehensive (~80s)
schemaNoOptional JSON Schema -- structure the report to extract these fields
streamNoSend progress notifications as each research phase completes
questionYesThe research question to investigate
max_sourcesNoOverride the default source count for the chosen depth (max 50)
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.
exclude_domainsNoExclude results from these domains
include_domainsNoOnly search results from these domains
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

A4.8/5.0
Behavior5/5

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

No annotations provided, so the description carries full burden. It discloses behavioral traits: multi-step process with parallel sub-queries, time estimates per depth (~15s, ~40s, ~80s), progress notifications via stream, and return format including report, citations, sources, and sub_queries. It thoroughly explains what the tool does.

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 front-loaded with purpose, then lists parameters concisely, and finally describes return value. Every sentence adds important information, no fluff. It is appropriately sized for a complex tool.

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?

Given the tool's complexity (10 parameters, multi-step, no output schema), the description is thorough. It explains the entire research process, parameter details, and return fields including the 'brief' sub-object. It fully informs the agent about what to expect.

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?

Schema description coverage is 100%, so baseline is 3. However, the description adds significant value beyond the schema: explains depth options with time and sub-query counts, describes max_tokens_out with tokenizer drift warning, details citation_format styles, and outlines return structure. This fully compensates for the lack of additional schema detail.

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 performs 'Multi-step research on a complex question' and decomposes into sub-queries, searches in parallel, fetches sources, and synthesizes a cited report. It explicitly distinguishes itself from siblings like 'search' and 'fetch' by noting it beats manual chaining for multi-source synthesis.

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 indicates when to use this tool (for multi-source synthesis) and implies simplicity of alternatives (manual chaining). While it doesn't explicitly list exclusions, the context is clear enough for an AI agent to decide appropriately.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/KnockOutEZ/wigolo'

If you have feedback or need assistance with the MCP directory API, please join our Discord server