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Hybrid search RedM docs (semantic + lexical)

semantic_search
Read-only

Search RedM/RDR3 docs by behavior, concept, OR exact token. Use when you don't have a specific native hash/name (use lookup_native) and the term isn't a known asset name in a large data table (use grep_docs). Hybrid mode (default) handles 'how do I X' queries ('teleport player', 'spawn vehicle', 'inventory add item') AND tokens ('addItem', 'weapon_pistol_volcanic', 'CPED_CONFIG_FLAG_') — fused via RRF over vector + BM25. Returns ranked snippets (path, breadcrumb, heading, snippet, score). Call get_document({path, heading}) for full chunk content. mode=semantic for pure vector; mode=lexical for pure BM25. Filter via category=vorp|rsgcore|oxmysql|natives|discoveries|jo_libs|learnings or namespace. Community findings merged by default; category=learnings returns only findings. If you are retrying after a previous call returned no useful results, populate prior_attempt so the server can surface alternative wordings and learn what's missing from the docs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNoRetrieval mode. Default hybrid (recommended).
limitNoHow many ranked snippets to return. Default 20 (Anthropic contextual-retrieval research: top-20 outperforms top-5/10 before reranking).
queryYesNatural language or token query
categoryNoLimit to one doc category
namespaceNoLimit to a native namespace, e.g. PLAYER, ENTITY
prior_attemptNoPopulate ONLY when retrying after a previous semantic_search call returned no useful results. Skip on first attempts.
responseFormatNo`concise` (default): 400-char snippet per hit — cheap, browse-style. `detailed`: full chunk content — use when you need an answer in one round-trip and want to skip the `get_document` follow-up.concise

TDQS

A4.9/5.0
Behavior5/5

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

Beyond the readOnlyHint annotation, the description reveals rich behavioral details: hybrid mode with RRF fusion, output snippet structure (path, breadcrumb, heading, snippet, score), community findings merging, category=learnings behavior, and the retry mechanism with prior_attempt. 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?

Every sentence earns its place: purpose, alternatives, hybrid behavior, output format, follow-up, mode options, filtering, community findings, retry guidance. It is dense but not wasteful, front-loading the main purpose and then layering necessary details for a complex 7-parameter 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?

The tool has 7 parameters, nested objects, no output schema. The description compensates by specifying the return structure (ranked snippets with fields), covering edge cases (retry, learnings, categories), and guiding subsequent get_document calls. It is complete enough for an agent to operate effectively without additional clarification.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the semantics of mode (semantic vs lexical vs hybrid) and giving concrete query examples that illustrate parameter usage. It also clarifies prior_attempt's purpose beyond the schema description, earning a 4.

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 starts with a specific verb and resource: 'Search RedM/RDR3 docs by behavior, concept, OR exact token.' It clearly distinguishes from siblings by explicitly stating when to use lookup_native and grep_docs instead, making the purpose unmistakable.

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 guidance: 'Use when you don't have a specific native hash/name (use lookup_native) and the term isn't a known asset name in a large data table (use grep_docs).' It also directs users to get_document for full content and describes the prior_attempt retry workflow, covering both use cases and exclusions.

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.