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Lookup RedM game-data asset (ped/weapon/object/door/vehicle)

asset_lookup
Read-only

Resolve a RedM game-data asset (ped model, weapon, object, door, vehicle) by exact name, 32-bit hash, or partial-name search. O(1) structured lookup against pre-parsed discoveries tables — replaces the common workflow of grepping a_c_bear_01 in peds_list.lua, then cross-referencing RELATIONSHIP/README.md for its relationship group. Returns: type, name, normalized hash (0x + 8 uppercase hex), source file + line, plus type-specific metadata (peds get variants + relationship, weapons get group, doors get coords + model_hash, objects get category/subcategory). Catalog ~22,500 entries (mostly objects). Typical latency p50 ~15ms, p95 ~65ms.

NOT for:

  • Script natives like SET_ENTITY_COORDS, GetPedHealth, or hashes from Citizen.InvokeNative(0x...) — use lookup_native. Native hashes are 64-bit (0x06843DA7060A026B); asset hashes are 32-bit (0xBCFD0E7F). Different namespaces, never collide.

  • Flag enums, settings, clipsets, scenario keys like CPED_CONFIG_FLAGS, MP_Style_Casual, mech_loco_m@, MAGGIE_SEAT_CHAIR_DESK_WRITING. Those live as tokens in lua source but not in this catalog. Use grep_docs.

  • Behavior queries ("which animal is the bear", "weapons in the lemat family") — use semantic_search.

Pass exactly ONE of name / hash / search. Optional type narrows to a category (useful when a fragment like "horse" hits both peds and vehicles). Note: type reflects the SOURCE FILE — the same asset name can exist under multiple types. e.g. mp006_p_mshine_int_door01x appears as type=object (1 row from object_list.lua) AND type=door (2 rows from doorhashes.lua, different door hashes for distinct in-world instances with coords). Pick type=door when you want lockable in-world doors with positions; type=object for the model itself.

Examples:

  • {name: "a_c_bear_01"} → exact ped lookup, returns variants=11 + relationship=REL_WILD_ANIMAL_PREDATOR.

  • {hash: "0xBCFD0E7F"} → resolves to ped a_c_bear_01 (omit 0x ok).

  • {search: "lemat", type: "weapon"} → substring match → weapon_revolver_lemat.

  • {search: "moonshine", type: "door"} → exact substring misses (no door name contains "moonshine"), fuzzy trigram fallback fires → mp006_p_mshine_int_door01x. Fuzzy mainly fires when type narrows out the exact-substring matches; without type, common terms find substring hits first and never reach fuzzy.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hashNoAsset hash (32-bit jenkins) in HEX format, case-insensitive, `0x` prefix optional. Examples: `0xBCFD0E7F`, `bcfd0e7f`. Use when you have a hash from decompiled code or another table and need the canonical name + metadata. Decimal-formatted hashes (e.g. `1946191463`) are NOT accepted — convert to hex first (`(1946191463).toString(16)`).
nameNoExact asset name, case-insensitive. Examples: `a_c_bear_01`, `weapon_pistol_volcanic`, `p_safe01`, `armysupplywagon`. Use when you know the precise name.
typeNoFilter results to one category. Useful when a name fragment matches multiple types (e.g. `horse` hits peds + vehicles).
limitNoMax matches to return. Default 5, max 50. Only applies to `search` — exact `name`/`hash` always return 0 or 1.
searchNoSubstring fragment within asset name, case-insensitive. Examples: `lemat`, `norfolk`, `volcanic`. Use when you remember part of the name. Algorithm: exact substring (ILIKE) first; if zero hits, falls back to pg_trgm `strict_word_similarity` ≥0.4 — catches abbreviation gaps like `moonshine`↔`_mshine_` when narrowed by `type` (without `type`, common terms find substring matches first and fuzzy never fires). `matchType` in the response tells you which path hit: `search` = exact substring, `fuzzy` = trigram.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
hintYes
assetsYes
statusYes
hashFormatYes
suggestionsYes

TDQS

A5/5.0
Behavior5/5

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

Annotations only state readOnlyHint=true and openWorldHint=false, but the description adds substantial behavioral detail: O(1) lookup, latency expectations, hash format (32-bit vs 64-bit), type reflects source file with multiple type rows possible, fuzzy trigram fallback threshold, matchType response, and limit semantics. No contradiction with annotations.

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 well-structured with a clear one-sentence summary, a 'NOT for' exclusion block, parameter-specific guidance, and labeled examples. It is long but every section carries essential information, is front-loaded, and avoids 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?

Given the tool's complexity (five parameters, multiple asset types, dual search modes, and an output schema), the description is remarkably complete. It covers exclusions, parameter selection, type semantics, fuzzy fallback triggers, and real-world examples. The expected output is described in sufficient detail even though an output schema exists.

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?

Although schema_description_coverage is 100%, the description adds critical semantic meaning beyond the schema: it explains that type reflects the source file and the same asset can appear under multiple types, describes how search alternates between substring and fuzzy trigram matching, and clarifies that limit only applies to search results. This substantially enriches the parameter understanding.

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 opens with a precise verb 'resolve' and specifies the exact resource (RedM game-data asset) and categories (ped, weapon, object, door, vehicle). It clearly distinguishes from sibling tools via the 'NOT for' section, explicitly naming lookup_native, grep_docs, and semantic_search as alternatives for different use cases.

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?

Provides explicit 'when to use' and 'when not to use' guidance, including named alternative tools. It prescribes exactly one of name/hash/search, explains when to use the type filter, and gives concrete examples for each lookup mode. The fuzzy fallback behavior is also clearly described with a specific example.

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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TDQS

A4.5/5.0
Disambiguation5/5

Each tool targets a distinct retrieval mode: structured asset lookup, script native resolution, exact token grep, semantic search, doc navigation, raw line access, and contribution. Descriptions explicitly cross-reference 'NOT for' cases, making misselection unlikely even where overlap exists.

Naming Consistency3/5

Names mix verb-object (lookup_native, get_document, read_lines), object-verb (asset_lookup), bare verbs (browse), and descriptive phrases (semantic_search). The verb 'lookup' appears as both suffix and prefix, and 'get'/'read' are used interchangeably for retrieval, though all names are lowercase snake_case and readable.

Tool Count5/5

10 tools is well-scoped for a documentation and reference server. Each tool fills a clear niche with no obvious bloat or redundancy, staying comfortably within the ideal 3-15 range.

Completeness5/5

The surface covers the full lifecycle of documentation access: orientation (list_namespaces), discovery (browse), exact and semantic search, native/asset lookup, full-content retrieval (get_document), raw line reading for large tables (read_lines), a calling-convention guide, and community contribution (share_finding). Known limitations in the data layer are addressed with companion tools.