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vacuous-tests-mcp

Name: vacuous-tests-mcp
Author: zegroged

by zegroged

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Python

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vacuous-tests-mcp

Un servidor MCP que encuentra pruebas que pasan sin importar lo que haga el código.

Una prueba vacua es peor que una que falta. Una prueba que falta es visiblemente ausente. Una prueba vacua se sienta en la suite, se ejecuta en verde, cuenta en el total y se cita como evidencia de que un comportamiento está cubierto — mientras no comprueba nada. Es cobertura que está invisiblemente ausente, y sobrevive exactamente a las situaciones para las que existen las pruebas.

Son fáciles de escribir por accidente y difíciles de detectar leyendo, porque una prueba vacua y una real a menudo se ven casi idénticas.

El caso del que surgió esto

Un archivo Rust, de 6,764 líneas, en un proyecto cuya suite de pruebas ya estaba bajo auditoría manual regular. Dos pruebas en él:

// flagged
const SRC: &str = include_str!("lib.rs");
assert!(SRC.contains("pub sas_verified: bool,"), "the field must be exposed");

// not flagged
const SRC: &str = include_str!("lib.rs");
let prod = SRC.split_once("\n#[cfg(test)]\nmod tests {").expect("test module").0;
assert!(prod.contains("InviterSecrets::create_for_group("), "production never calls it");

Ambas incrustan el código fuente del propio archivo. La primera afirma directamente sobre SRC — y SRC contiene la prueba en sí, incluida la cadena que se busca. Es verdadera por construcción: elimina el código de producción que dice proteger y sigue en verde. La segunda corta primero el módulo de prueba y afirma solo sobre la porción de producción, por lo que falla cuando el código de producción cambia. Esa es una barrera real.

Escanear ese archivo informa un hallazgo, en la línea correcta, y deja los otros cuatro sitios de include_str! en paz. El escáner sigue el enlace en lugar de hacer coincidencia de patrones en include_str!, que es lo que separa los dos casos.

Related MCP server: openhonest-umbra

Reglas

Regla	Severidad	Qué detecta
`self-referential-source`	alta	La prueba incrusta su propio código fuente y afirma que un literal aparece en él. El literal está en la afirmación, por lo que nunca puede fallar.
`no-assertions`	alta	Ninguna afirmación de ningún tipo. Solo un panic o un throw puede hacer fallar la prueba, por lo que un comportamiento incorrecto pero silencioso pasa.
`tautological-assertion`	alta	`assert!(true)`, `assert_eq!(x, x)`, `expect(true).toBe(true)` — se cumple independientemente del código.
`empty-body`	alta	Nada en el cuerpo que pueda fallar.
`skipped-test`	info	`#[ignore]`, `it.skip`, `@pytest.mark.skip`. Pasa en verde porque no se ejecuta.

Lenguajes

Lenguaje	Método	Precisión
Python	`ast` de la biblioteca estándar	Exacta
Rust	escáner de coincidencia de llaves sobre el texto fuente	Heurística
JavaScript / TypeScript	escáner de coincidencia de llaves sobre el texto fuente	Heurística

Los escáneres de Rust y JS enmascaran literales de cadena y comentarios antes de coincidir, por lo que una { dentro de una cadena o una afirmación comentada no puede engañarlos. Están ajustados para omitir un caso en lugar de inventar uno: un falso positivo cuesta más que un falso negativo aquí, porque la primera respuesta incorrecta enseña a la gente a ignorar la salida.

Trata cada hallazgo como una pregunta a comprobar, no como un veredicto. Cada uno nombra un archivo y una línea, por lo que confirmarlo lleva segundos.

Instalación

Aún no está en PyPI — instala desde el código fuente:

git clone https://github.com/zegroged/vacuous-tests-mcp
cd vacuous-tests-mcp
pip install .

Eso pone un comando vacuous-tests-mcp en tu PATH.

Úsalo desde un cliente MCP

Añade a la configuración MCP de tu cliente:

{
  "mcpServers": {
    "vacuous-tests": {
      "command": "vacuous-tests-mcp"
    }
  }
}

Para Claude Code:

claude mcp add vacuous-tests -- vacuous-tests-mcp

Luego pídele que escanee algo:

Escanea ./src en busca de pruebas que no puedan fallar.

Herramientas

scan_tests(path, include_skipped=True, max_findings=100) Recorre un archivo o directorio e informa de pruebas que no pueden fallar. Los directorios de compilación y dependencias (target, node_modules, .venv, …) se omiten. Los hallazgos vuelven primero por mayor severidad, cada uno con una ruta, línea, nombre de prueba, regla y fragmento.

list_rules() Cada regla con una descripción, para que un modelo pueda decidir qué pedir.

explain_rule(rule) Qué detecta una regla y cómo se resuelve normalmente el hallazgo.

El servidor solo lee. No escribe archivos, no ejecuta el código que escanea y no mira fuera de la ruta que se le da.

Desarrollo

pip install -e ".[dev]"
pytest

La suite cubre cada regla en cada lenguaje y — lo más importante — comprueba que una prueba normal situada junto a una vacua no se informe. También hay una prueba de extremo a extremo que inicia el servidor como subproceso y lo conduce a través de un apretón de manos MCP real, por lo que la capa de protocolo está cubierta en lugar de asumida.

Limitaciones

La detección de Rust y JS/TS es textual, no un análisis sintáctico. Las pruebas generadas por macros, el formato inusual y los cierres muy anidados pueden pasarse por alto.
no-assertions no sabe que una prueba puede existir puramente para demostrar que algo no entra en pánico. Tal prueba es un verdadero positivo según la regla y un falso positivo por intención; dilo con una afirmación explícita y se callará.
Solo se escanean los lenguajes de la tabla anterior. Otros archivos se ignoran, no se adivinan.

Licencia

MIT

Available Tools

3 tools

explain_ruleA

Explain one rule: what it detects and how the finding is usually resolved.

Args: rule: A rule id as returned by list_rules or by a finding, for example self-referential-source.

ParametersJSON Schema

Name	Required	Description	Default
`rule`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

A3.7/5.0

Behavior3/5

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

No annotations are provided, so the description carries the full behavioral burden. It does disclose the core behavior (returns an explanation of detection and resolution), but says nothing about error handling for unknown rule ids, the structure of the returned explanation, or any side effects. Given an output schema exists, the return structure gap is partly covered, but the description alone is thin on behavioral detail.

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 purpose is front-loaded in the first sentence, and the argument documentation follows in a compact, readable block. Every sentence earns its place — the example and provenance note are exactly the information an agent needs. No filler, no redundancy.

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?

For a single-parameter tool of low complexity with an output schema present, the description is largely sufficient: it states the tool's purpose, what the output covers, and where the argument value comes from. Minor gaps like behavior on invalid rule ids are acceptable given the simple scope and the output schema's presence.

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?

With 0% schema description coverage, the description fully compensates for the 'rule' parameter by stating its provenance ('as returned by list_rules or by a finding') and providing a concrete example ('self-referential-source'). This adds real meaning beyond the bare string type in the schema, though it stops short of enumerating valid values or format constraints.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb+resource pair ('Explain one rule') and spells out what the explanation covers ('what it detects and how the finding is usually resolved'). The purpose is unambiguous and clearly distinct from list_rules (which lists rules) and scan_tests (which runs scans), though it never names those siblings explicitly, so differentiation relies on the reader inferring it.

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

Usage Guidelines3/5

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

The description implies a workflow by telling the agent the rule id comes 'as returned by list_rules or by a finding', which subtly signals that explain_rule is the follow-up step to understanding a rule. However, it never explicitly states when to use this tool versus alternatives, nor any conditions where it should not be used. The guidance is implied rather than stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_rulesA

List every rule the scanner applies, with a one-paragraph description of each.

ParametersJSON Schema

Name	Required	Description	Default
No parameters

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

A3.8/5.0

Behavior3/5

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

With no annotations, the description carries the full burden for behavioral disclosure. It states that the tool returns a list with one-paragraph descriptions, which is a behavioral outcome. However, it does not explicitly state that it is read-only, non-destructive, or what happens if there are no rules. For a simple list operation, this is adequate but not enriched—no mention of side effects, ordering, or potential errors.

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 sentence, front-loaded with the action and purpose. It conveys the exact scope ('every rule') and the output format ('one-paragraph description'). There is zero waste; every word earns its place.

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?

For a simple list tool with no parameters and an output schema, the description is sufficient to convey what the agent will get. It covers the full scope ('every rule') and the format of each entry. The main missing element is an explicit statement about its read-only nature, but given the simplicity and the existence of an output schema, the description is adequately complete.

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?

The tool has zero parameters, and the schema already reflects that (coverage 100%). The description adds no parameter info because none are needed. Per the rubric, 0 params gives a baseline of 4, and the description does not need to compensate for any missing parameter documentation. There is nothing to clarify.

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 action 'List every rule' with a specific resource ('the scanner applies') and the nature of the content ('with a one-paragraph description of each'). It distinguishes itself from siblings: explain_rule presumably explains a single rule, and scan_tests likely performs scanning. The verb 'list' and scope 'every' make the purpose unambiguous.

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 the siblings (explain_rule, scan_tests). It does not mention any conditions, prerequisites, or exclusions that would help an agent decide. The agent must infer from the name and the minimal description that this is for getting an overview, but no explicit direction is given.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

scan_testsA

Scan a file or directory for tests that cannot fail.

Reports tests that pass regardless of the behaviour of the code they claim to cover: assertions that are true by construction, tests with no assertion at all, empty bodies, and tests that are skipped while still appearing in the suite.

Python is analysed with the ast module and is exact. Rust and JavaScript/TypeScript use a brace-matching text scanner, which is a heuristic tuned to miss cases rather than invent them. Every finding carries a file and line so it can be checked directly.

Args: path: File or directory to scan. Build and dependency directories (target, node_modules, .venv, ...) are skipped automatically. include_skipped: Include tests marked skipped or ignored. These always run green because they do not run at all, but they are usually deliberate, so they are reported at info severity. max_findings: Cap on returned findings. The summary always counts every finding, including any beyond the cap.

ParametersJSON Schema

Name	Required	Description	Default
`path`	Yes
`max_findings`	No
`include_skipped`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

A4.6/5.0

Behavior4/5

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 does so effectively: it explains what the tool detects, how detection differs across languages (exact vs. heuristic), and that every finding includes file and line. It also discloses that include_skipped findings are reported at 'info' severity. The one gap is that it does not explicitly state the tool is read-only (no file modifications), though 'scan' strongly implies it. Overall, it is far more transparent than typical tool descriptions.

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 well-structured and front-loaded: the core purpose is stated in the first sentence, followed by detailed detection rules, then language-specific behavior, and finally parameter details. Each paragraph adds distinct value without repetition or fluff. The format is scannable and efficient for an agent.

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 presence of an output schema, the description need not enumerate return fields, but it still provides rich context: what the tool detects, severity semantics, language-specific accuracy, and parameter behavior. An agent has all necessary information to invoke the tool correctly, choose appropriate inputs, and interpret results. It is complete for a scanning tool of this complexity.

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 description coverage is 0%, so the description must compensate, and it does. The 'Args' section fully explains each parameter: path (file/dir, auto-skipped dirs), include_skipped (why include, severity implications), and max_findings (cap behavior, summary counts all). This goes beyond the schema by providing behavioral context, making parameter semantics extremely clear.

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 specific verb and resource: 'Scan a file or directory for tests that cannot fail.' It then enumerates concrete categories of such tests (true-by-construction assertions, no assertions, empty bodies, skipped). The purpose is unambiguous and distinct from sibling tools list_rules and explain_rule, which operate on rules rather than tests.

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?

The description clarifies when to use the tool: it is for scanning files/directories for non-failing tests, and it explicitly notes that build/dependency directories are auto-skipped. It also distinguishes Python analysis (exact via ast) from Rust/JS (heuristic), helping agents set expectations. While it does not name alternative tools for comparison (the siblings are clearly rule-focused, not test scanners), the use case is sufficiently well-scoped. No explicit when-not-to-use guidance, but this is minor given the clear domain.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

3 tool updatesv0.1.0Aug 27, 2026
- First observedexplain_rule
- First observedlist_rules
- First observedscan_tests

Deploy Server

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.2/5.0

Scored 2026-08-27 10:50 across 3 tools

Disambiguation5/5

Each tool has a distinct purpose: listing available rules, explaining a specific rule, and scanning tests for vacuous assertions. There is no overlap between scan_tests and the two rule-related tools, and list_rules/explain_rule are clearly separated by verb (list vs. explain).

Naming Consistency5/5

All tools follow a consistent snake_case verb_noun pattern: list_rules, explain_rule, scan_tests. The naming is predictable and aligns perfectly with the tool's action.

Tool Count5/5

With 3 tools, the server is well-scoped for its purpose—a test scanner with rule documentation. Each tool earns its place, and the count falls squarely within the typical 3-15 range for a focused server.

Completeness5/5

The tool surface fully covers the intended workflow: discover rules, understand a rule, and run the scanner. There are no obvious gaps—the scanner's options are handled via arguments rather than requiring additional tools, and the rule lifecycle (list/explain) is complete for a read-only server.

Maintenance

ActivitySlowing

ResponsivenessNo issues

Resources

GitHub Repository

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vacuous-tests-mcp

El caso del que surgió esto

Reglas

Lenguajes

Instalación

Úsalo desde un cliente MCP

Herramientas

Desarrollo

Limitaciones

Licencia

Available Tools

Output Schema

TDQS

Output Schema

TDQS

Output Schema

TDQS

Tool Schema Changelog

TDQS

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

OrangePro MCPofficial

openhonest-umbraofficial

faultlineofficial

TestHeal