RedM Mcp

Read an exact line range from a raw doc file

read_lines

Read-only

Read an exact line range from a raw doc file by absolute line number — the windowed-read companion to grep_docs. When grep_docs returns a hit at path:line inside a large file, call read_lines({ path, start, end }) to pull the surrounding block. This is the ONLY way to read around a hit in the largest rdr3_discoveries data tables (audio_banks, ingameanims_list, ptfx, soundsets, imaps_with_coords, megadictanims, etc.): their full bodies are NOT in the vector/heading index (only an ~80-line preview is), so semantic_search can't reach them and get_document resolves real section headings only — NOT synthetic lines N-M offsets. start/end are 1-based and inclusive; omit end for a 50-line window; one call returns at most 400 lines (narrow the range for more). For prose .md docs prefer get_document with a heading; to search values use grep_docs; for individual script natives use lookup_native.

Input Schema

TableJSON Schema

Name	Required	Description
`end`	No	Last line to return (1-based, inclusive). Omit for a 50-line window from `start`. Spans over 400 lines are capped.
`path`	Yes	Doc path exactly as returned by `grep_docs` / `browse` / `semantic_search`, e.g. `discoveries/audio/audio_banks/audio_banks.lua`. Do not invent paths.
`start`	Yes	First line to return (1-based, inclusive). Use the line number from a `grep_docs` hit.

TDQS

A4.7/5.0

Behavior5/5

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

Annotations already indicate readOnlyHint=true, but the description adds valuable behavioral context: 1-based inclusive lines, 50-line default window, 400-line cap, and the indexing limitation that semantic_search/get_document cannot reach full table bodies. This goes beyond what annotations provide and helps the agent predict behavior.

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 dense but every sentence is purposeful. It front-loads the primary purpose, then adds usage context, limitations, and alternatives without any filler or redundancy.

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?

Despite lacking an output schema, the description covers trigger conditions, why this tool exists, parameter behavior, limits, and alternatives. It is sufficiently complete for an agent to select and invoke the tool correctly in the intended scenarios.

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 100% and the input schema already describes all parameters, including 1-based/inclusive semantics and the 400-line cap. The description mostly restates these details (e.g., 'omit end for a 50-line window') rather than adding new parameter-level meaning, so a baseline 3 is appropriate.

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 uses a specific verb+resource: 'Read an exact line range from a raw doc file by absolute line number.' It clearly distinguishes itself from siblings by being the 'windowed-read companion to grep_docs' and the ONLY way to read around hits in large data tables, making its 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?

The description gives explicit when-to-use guidance: call read_lines when grep_docs returns a hit, and states it is the only way for certain large tables. It also names alternatives for other cases: get_document for prose .md files, grep_docs for searching values, and lookup_native for script natives.

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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