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roblox-logics-mcp

Search Roblox logics

search_logics
Read-only

Search Roblox game-development knowledge by describing your build problem, then browse cheap metadata results and fetch full guides with get_logic.

Instructions

Primary entry point. Search the knowledge base for how to build a Roblox system — 'stop exploiters firing remotes', 'save player data safely', 'melee hitbox that feels fair on high ping'. Returns metadata only (id, title, category, difficulty, summary) so browsing is cheap; follow up with get_logic(id) for the full write-up. Use search_code instead when you are looking for a specific Luau API such as UpdateAsync or BindToClose.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNoRequire all of these tags (exact, kebab-case), e.g. ['datastore','autosave'].
limitNoMax results. Keep at 5 unless you are surveying a topic.
queryYesNatural-language description of the problem or system. Plain words beat keywords.
categoryNoRestrict to one category. Omit unless you are certain — it can hide good cross-category matches.
difficultyNoRestrict to one difficulty level.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds genuine value beyond that by disclosing the return shape ('metadata only — id, title, category, difficulty, summary') and the rationale ('browsing is cheap'), which is important since no output schema exists. It stops short of describing pagination or ranking behavior, so a 4 rather than 5.

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?

Front-loads the role ('Primary entry point'), then examples, then return shape, then the alternative. Every sentence earns its place with no redundancy or filler.

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?

For a search tool with no output schema, the description is complete: it covers the routing role, the cheap-metadata return contract, the follow-up path to get_logic, and the sibling alternative. An agent has everything needed to call it correctly and act on results.

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 description coverage is 100%, so every parameter including query, tags, limit, category, and difficulty is already documented with guidance. The description's example queries loosely illustrate the natural-language style of 'query' but add no new semantics beyond the schema, so the baseline 3 applies.

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?

States a specific verb and resource ('Search the knowledge base for how to build a Roblox system') with concrete example queries that pin down the intent. It distinguishes itself from siblings by naming both get_logic (for follow-up detail) and search_code (for specific Luau APIs).

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

Usage Guidelines5/5

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

Explicitly positions itself as the 'Primary entry point', gives the condition for the alternative ('Use search_code instead when you are looking for a specific Luau API'), and prescribes the follow-up (get_logic(id)). When-to-use, when-to-use-something-else, and next-step are all covered.

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