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xmp4 — Semantic code knowledge for your stack

by ai.example4

xmp4_tests_for

Find direct tests that exercise a given symbol (direct callers filtered to test-file candidates per language pattern: CSharp/Java/PHP: Test(s).; Python: test_.py / *_test.py; TypeScript/JavaScript: *.spec/test.{ts,js}; Rust: *_tests.rs / tests/; etc.).

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

A3.9/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the filtering logic and language patterns, and indicates it returns only direct test callers, which is useful. However, it does not mention return format, error handling, permissions, or any potential side effects, leaving some ambiguity.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single dense sentence that efficiently conveys the main purpose and the language-pattern details. While the parenthetical structure is slightly cluttered, every detail contributes to understanding the tool's behavior, making it appropriately concise.

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

Completeness4/5

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

The description is rich enough for the tool's complexity, covering the core behavior and language-pattern nuances. While there is no output schema and the description does not explicitly state the return format, the name and description strongly imply a list of tests, and the schema handles pagination and formatting parameters.

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 7 parameters, so the schema already documents each parameter adequately. The tool description adds no parameter-specific meaning beyond the overall purpose, so the 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 clearly states the tool's function with a specific verb 'Find' and resource 'direct tests that exercise a given symbol', and distinguishes it from sibling tools by focusing on test-file candidates with language-specific patterns. This makes the purpose unambiguous and distinct.

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

Usage Guidelines4/5

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

Provides clear context for when to use the tool (to find direct tests for a symbol) and includes detailed language-pattern rules that clarify its scope. However, it does not explicitly mention alternatives or when not to use this tool versus other call-discovery 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.

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