RedM Mcp

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

Name	Required	Description
`hash`	No	Asset 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)`).
`name`	No	Exact asset name, case-insensitive. Examples: `a_c_bear_01`, `weapon_pistol_volcanic`, `p_safe01`, `armysupplywagon`. Use when you know the precise name.
`type`	No	Filter results to one category. Useful when a name fragment matches multiple types (e.g. `horse` hits peds + vehicles).
`limit`	No	Max matches to return. Default 5, max 50. Only applies to `search` — exact `name`/`hash` always return 0 or 1.
`search`	No	Substring 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

Name	Required	Description	Default
`hint`	Yes
`assets`	Yes
`status`	Yes
`hashFormat`	Yes
`suggestions`	Yes

TDQS

A5/5.0

Behavior5/5

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

Beyond the readOnlyHint=true annotation, the description discloses substantial behavioral traits: O(1) lookup against pre-parsed tables, typical latency (p50 ~15ms, p95 ~65ms), exact-match semantics (name/hash return 0 or 1), fuzzy trigram fallback behavior with the exact trigger condition, and the 'type' source-file nuance (same asset can appear under multiple types with different metadata). It even warns about decimal hashes and why fuzzy rarely fires without type narrowing. This is far more than the annotation provides and fully arms the agent.

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 long but every section earns its place: purpose, return format, performance, not-for alternatives, parameter usage, type nuance, fuzzy behavior, and examples. It is front-loaded with the one-sentence purpose, uses clear section breaks and bullet lists, and avoids fluff. The length is appropriate for a tool with five parameters and nuanced edge cases; it reads as a compact reference rather than padded prose.

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 (5 parameters, 5 asset types, fuzzy matching, type source-file ambiguity), the description is remarkably complete. Even with an output schema present, the description explains the non-obvious return fields (matchType, variants, relationship, coords, etc.) and the exact conditions under which different metadata appears. It also covers edge cases like the same asset existing under multiple types and the decimal-hash rejection. There are no obvious gaps that would leave an agent unsure how to invoke the tool correctly.

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 coverage is 100%, so the baseline is 3, but the description adds critical semantics not present in the JSON schema: mutual exclusivity of name/hash/search (the schema does not enforce oneOf), the fact that 'limit' only applies to search, and the exact behavior of the fuzzy fallback (trigram similarity >=0.4, when it fires and when it doesn't). It also provides rich examples for each parameter (e.g., hash 0xBCFD0E7F resolves to a_c_bear_01) and clarifies the exact format expectations. This far exceeds what the schema already documents.

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 clear, specific statement: 'Resolve a RedM game-data asset (ped model, weapon, object, door, vehicle) by exact name, 32-bit hash, or partial-name search.' It explicitly distinguishes itself from sibling tools (lookup_native, grep_docs, semantic_search) in the 'NOT for' section, and even provides the exact output shape (type, name, normalized hash, source file, metadata). This goes well beyond a vague purpose and clearly differentiates from alternatives.

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?

The 'NOT for' section lists three specific alternative tools with exact use cases (natives -> lookup_native, tokens -> grep_docs, behavior queries -> semantic_search). It also gives clear parameter usage rules ('Pass exactly ONE of name/hash/search'), explains when to use the optional 'type' filter, and includes concrete examples for each parameter mode. This is explicit, actionable guidance with no ambiguity.

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

A4.7/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose: asset_lookup handles game-asset hashes/names, lookup_native handles script natives, grep_docs does exact token search, semantic_search handles concept/behavior queries, browse/list_namespaces orient, get_document/read_lines retrieve content, get_invoke_guide is a specialized reference, and share_finding contributes. The descriptions explicitly cross-reference when NOT to use each tool, eliminating ambiguity.

Naming Consistency4/5

Most tools follow verb_noun pattern (get_document, grep_docs, list_namespaces, lookup_native, read_lines, share_finding), but asset_lookup uses noun_verb order, and browse is a bare verb. The deviation is minor and the pattern remains predictable.

Tool Count5/5

10 tools is well within the ideal 3-15 range. Each tool earns its place: search, retrieval, discovery, lookup, and contribution are all covered without bloat. The count matches the server's purpose as a comprehensive documentation interface.

Completeness5/5

The domain is RedM/RDR3 documentation access, and the set covers the full lifecycle: orientation (list_namespaces, browse), search (semantic_search, grep_docs, lookup_native, asset_lookup), retrieval (get_document, read_lines, get_invoke_guide), and contribution (share_finding). There are no obvious dead ends or missing operations for the stated purpose.

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