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Glama

genius.hunt

Find ranked, import-ready code implementations across GitHub, grep.app, and searchcode by concept, with quality explanations instead of raw search results.

Instructions

Find the best implementations of a concept across GitHub, grep.app, and searchcode. Returns ranked archetypes (not raw search results) with quality explanations. Example: genius.hunt('rate limiter', language: 'typescript')

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNofast: local+grep (<1.5s), balanced: full discovery (<6s), deep: wider hydrationbalanced
tierNoResponse detail level: L1=compact cards, L2=descriptions, L3=full code, auto=adaptiveauto
queryYesWhat to find: 'retry with backoff', 'rate limiter'
presetNobattle_tested
languageNoFilter by language
maxArchetypesNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations provided, the description carries the full behavioral burden. It discloses the useful trait that output is ranked archetypes with quality explanations rather than raw hits, and identifies its data sources, but says nothing about permissions, latency, result limits, or how quality is judged. Adequate but incomplete for an unannotated tool.

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?

Three sentences, front-loaded with purpose, followed by a return-value clarification and a concrete example. Nothing is padded, though the example could arguably be folded into the schema or dropped for further tightness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a six-parameter discovery tool with no output schema, the description should explain returns more fully. It names the archetype concept but leaves 'quality explanations', the preset semantics, and cross-source aggregation behavior unspecified. Reasonable but with clear gaps given the tool's complexity.

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 coverage is 67%, with mode and tier already self-documented in the schema and preset and maxArchetypes undocumented. The description adds only a single example using query and language, giving no meaning to the preset choices or maxArchetypes behavior. Roughly a baseline score given the schema does most of the work.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description gives a specific verb ('Find'), an explicit resource ('best implementations of a concept'), and names the three sources (GitHub, grep.app, searchcode). It further distinguishes itself by stating it returns 'ranked archetypes (not raw search results)'. It does not, however, differentiate itself from the very similar sibling genius.find_best or github.search_code.

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

Usage Guidelines2/5

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

There is no explicit when-to-use, when-not-to-use, or alternative routing guidance. The parenthetical 'not raw search results' hints at a contrast with search tools, but the agent is left to infer when this beats genius.find_best or github.search_code. An example invocation is given but that is invocation syntax, not usage guidance.

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