How do I use vacuous-tests-mcp?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@vacuous-tests-mcp scan ./src for vacuous tests" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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vacuous-tests-mcp

by zegroged

Overview Schema Source Related Servers Discussions Admin

Python

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vacuous-tests-mcp

An MCP server that finds tests which pass no matter what the code does.

A vacuous test is worse than a missing one. A missing test is visibly missing. A vacuous test sits in the suite, runs green, counts in the total, and gets quoted as evidence that a behaviour is covered — while checking nothing. It is coverage that is invisibly absent, and it survives exactly the situations tests exist to catch.

They are easy to write by accident and hard to spot by reading, because a vacuous test and a real one often look nearly identical.

The case this was built from

A Rust file, 6,764 lines, in a project whose test suite was already under regular manual audit. Two tests in it:

// 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");

Both embed the file's own source. The first asserts on SRC directly — and SRC contains the test itself, including the very string being searched for. It is true by construction: delete the production code it claims to guard and it stays green. The second cuts the test module off first and asserts on the production slice only, so it fails when the production code changes. That one is a real gate.

Scanning that file reports one finding, at the right line, and leaves the other four include_str! sites alone. The scanner follows the binding rather than pattern-matching on include_str!, which is what separates the two cases.

Related MCP server: Chaos-MCP

Rules

Rule	Severity	What it catches
`self-referential-source`	high	The test embeds its own source and asserts a literal appears in it. The literal is in the assertion, so it can never fail.
`no-assertions`	high	No assertion of any kind. Only a panic or throw can fail the test, so wrong-but-quiet behaviour passes.
`tautological-assertion`	high	`assert!(true)`, `assert_eq!(x, x)`, `expect(true).toBe(true)` — holds regardless of the code.
`empty-body`	high	Nothing in the body to fail.
`skipped-test`	info	`#[ignore]`, `it.skip`, `@pytest.mark.skip`. Runs green because it does not run.

Languages

Language	Method	Accuracy
Python	`ast` from the standard library	Exact
Rust	brace-matching scanner over the source text	Heuristic
JavaScript / TypeScript	brace-matching scanner over the source text	Heuristic

The Rust and JS scanners mask string literals and comments before matching, so a { inside a string or a commented-out assertion cannot mislead them. They are tuned to miss a case rather than invent one: a false positive costs more than a false negative here, because the first wrong answer teaches people to ignore the output.

Treat every finding as a question to check, not a verdict. Each one names a file and a line, so confirming it takes seconds.

Install

Not on PyPI yet — install from source:

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

That puts a vacuous-tests-mcp command on your PATH.

Use it from an MCP client

Add to your client's MCP configuration:

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

For Claude Code:

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

Then ask it to scan something:

Scan ./src for tests that can't fail.

Tools

scan_tests(path, include_skipped=True, max_findings=100) Walk a file or directory and report tests that cannot fail. Build and dependency directories (target, node_modules, .venv, …) are skipped. Findings come back highest severity first, each with a path, line, test name, rule and snippet.

list_rules() Every rule with a description, so a model can decide what to ask for.

explain_rule(rule) What one rule detects and how the finding is usually resolved.

The server only reads. It does not write files, does not execute the code it scans, and does not look outside the path it was given.

Development

pip install -e ".[dev]"
pytest

The suite covers each rule in each language, and — more importantly — checks that a normal test sitting next to a vacuous one is not reported. There is also an end-to-end test that starts the server as a subprocess and drives it through a real MCP handshake, so the protocol layer is covered rather than assumed.

Limitations

Rust and JS/TS detection is textual, not a parse. Macro-generated tests, unusual formatting and heavily nested closures can be missed.
no-assertions does not know that a test may exist purely to prove something does not panic. Such a test is a true positive by the rule and a false positive by intent; say so with an explicit assertion and it goes quiet.
Only the languages in the table above are scanned. Other files are ignored, not guessed at.

License

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

ActivityMaintained

ResponsivenessNo issues

Resources

GitHub Repository

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vacuous-tests-mcp

The case this was built from

Rules

Languages

Install

Use it from an MCP client

Tools

Development

Limitations

License

Available Tools

Output Schema

TDQS

Output Schema

TDQS

Output Schema

TDQS

Tool Schema Changelog

TDQS

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

leakguard-mcp

Chaos-MCP

OrangePro MCPofficial

openhonest-umbraofficial