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xmp4 — Semantic code knowledge for your stack

by ai.example4

xmp4_source

Extract source code for a symbol in a project.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
docsNoInclude docs: none (default) | summary | full (xmp4_info only)
pageNoPage number (1-based, default 1; ignored by xmp4_info/xmp4_source)
projectYesProject id: 'repo/project' or 'repo/project/language'. Case-insensitive prefix match. Append '/Python'|'/CSharp'|'/Java'|etc. only to disambiguate multi-language projects (e.g. 'django/Django/Python' vs 'django/Django/JavaScript'). 1 match → proceeds; N → warning lists candidates; 0 → do NOT iterate guesses, call xmp4_projects(query=...) once then retry.
file_pathNoFile path to disambiguate
page_sizeNoResults per page (default 20, max 100)
symbol_nameYesSymbol name
output_formatNoOutput format: Compact (default) or Verbose

TDQS

B3.3/5.0
Behavior2/5

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

No annotations are present, so the description carries the full burden for behavioral disclosure. It only says 'Extract source code' and fails to mention output format, pagination, permissions, or potential side effects. The read-only nature is implied but not elaborated.

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 a single, front-loaded sentence with no filler or redundant information. Every word contributes to stating the tool's purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The schema is rich and covers all parameters, but the lack of an output schema and sparse description leaves gaps around return structure and advanced behaviors like pagination or format handling. It is sufficient for basic invocation but not fully complete for nuanced use cases.

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?

The input schema has 100% description coverage for all parameters, including detailed guidance on project disambiguation and output format defaults. The description adds no parameter-specific semantics, but the schema already fully documents them, so a baseline score of 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 ('Extract') and identifies the resource ('source code for a symbol in a project'), making the tool's purpose clear and distinct from siblings like xmp4_info or xmp4_view. It leaves no ambiguity about what operation is performed.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives such as xmp4_info, xmp4_view, or xmp4_outline. It simply states the action without context, exclusions, or references to related tools.

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

A3.7/5.0
Disambiguation5/5

Each tool targets a distinct purpose: callers vs callees vs usages vs tests_for are directionally different, grep vs search separates text from symbol queries, and outline/view/source/info each serve unique functions. No two tools clearly overlap.

Naming Consistency5/5

All tools follow a uniform 'xmp4_<snake_case_noun>' pattern with no mixed conventions or camelCase. The names are terse, descriptive, and predictably formatted.

Tool Count4/5

At 16 tools, this is slightly above the typical well-scoped range (3–15), but the breadth is justified by the comprehensive code-knowledge domain. Every tool covers a distinct feature, so the count feels reasonable rather than bloated.

Completeness5/5

The surface is remarkably complete for semantic code analysis: project discovery (xmp4_projects), symbol lookup (xmp4_search, xmp4_symbol_at, xmp4_info), navigation (xmp4_outline, xmp4_view, xmp4_source), relationship exploration (callers, callees, hierarchy, usages, tests_for), and supporting meta tools (guide, server, deps, grep). No obvious gaps exist for its stated purpose.

Resources