What can you do with this server?

This server turns JavaScript/TypeScript coverage artifacts and source files into practical test prioritization insights, all locally without sending code externally. * Analyze coverage reports (lcov, Istanbul JSON, Cobertura XML) to reveal uncovered files, functions, lines, and branches. * Find source functions that lack a corresponding test file, with configurable source/test directories and file extensions. * Score functions by cyclomatic complexity to identify which deserve testing attention first. * Suggest structured test cases (happy path, boundary, error, async, type) for specific functions. * Generate terminal/CI coverage reports via the test-intel-report command. * Integrate with MCP clients (stdio or HTTP) and GitHub Actions for automated test-priority summaries.

How do I use test-intel-mcp?

1. Click on "Install 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., "@test-intel-mcp find untested functions in src and suggest test cases" 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.

de en es ja ko ru zh

test-intel-mcp

by barissozudogru

Overview Schema Source Related Servers Score Discussions Admin

JavaScript

Hybrid

test-intel-mcp

Turn JavaScript and TypeScript coverage artifacts into practical test priorities. The server reads local coverage and source files, identifies gaps, scores function complexity, and suggests focused test cases without sending code to an external service.

Tool page · npm · Listed in the official MCP Registry

test-intel-mcp analyzing real p-limit coverage

Start in one minute

Add one local server entry to any stdio-compatible MCP client:

{
  "mcpServers": {
    "test-intel": {
      "command": "npx",
      "args": ["-y", "@barissozudogru/test-intel-mcp"]
    }
  }
}

Then ask the client to inspect an existing coverage file:

Use test-intel to analyze coverage/lcov.info, identify the highest-impact gaps,
and suggest tests for the most complex uncovered function.

No account, API key, or hosted service is required. File access is restricted to the directory where the server starts.

Related MCP server: Chisel

Proof on this repository

Running the complexity tool against the real src/paths.ts file produces:

Function complexity analysis for: src/paths.ts
Total functions analyzed: 1

Priority | Function        | Line | Cyclomatic | Branches | Loops
---------|-----------------|------|------------|----------|------
low      | deriveTestPaths | 11   | 3          | 1        | 1

Summary: 0 critical, 0 high, 0 medium, 1 low priority

The same server reads lcov, Istanbul JSON, and Cobertura files to surface uncovered functions, lines, and branches.

A pinned p-limit 7.3.1 case study reproduces coverage from the upstream project's 22 passing tests. test-intel-report reduces its lcov artifact to two uncovered lines and one uncovered branch while preserving the original coverage file for inspection.

If this saves you time, consider starring the repository. It helps other developers find it.

Tools

Tool	What it answers
`analyze_test_coverage`	Which files, functions, lines, and branches remain uncovered?
`find_untested_functions`	Which source functions have no corresponding test file?
`get_function_complexity`	Which functions deserve testing attention first?
`suggest_test_cases`	Which happy path, boundary, error, async, and type cases should be reviewed?

Supported coverage formats:

Format	Common producers
lcov	Jest, Vitest, nyc, Istanbul
Istanbul JSON	Jest, nyc, Istanbul
Cobertura XML	Jest, pytest-cov, JaCoCo

Heuristic source-to-test matching and complexity scores are prioritization signals. Native coverage data and human review remain the source of truth.

Coverage report command

The package also includes a non-MCP command for CI and terminal use:

npx -y -p @barissozudogru/test-intel-mcp test-intel-report coverage/lcov.info

An explicit format can be supplied when the filename is ambiguous:

test-intel-report coverage/result.xml cobertura

GitHub Action

Generate coverage with the project's own test runner, then pass the resulting artifact to the action:

name: Test priorities

on: [pull_request]

jobs:
  test-intel:
    runs-on: ubuntu-latest
    permissions:
      contents: read
    steps:
      - uses: actions/checkout@v4
      - name: Run tests with coverage
        run: npm ci && npm test -- --coverage
      - uses: barissozudogru/test-intel-mcp@v0.8.1
        with:
          coverage-path: coverage/lcov.info

The report is written to the workflow summary. The action analyzes an artifact that already exists and does not upload source or coverage data.

Client setup

Use the stdio configuration from the quickstart. Config file locations differ by client, but the server entry is the same:

{
  "mcpServers": {
    "test-intel": {
      "command": "npx",
      "args": ["-y", "@barissozudogru/test-intel-mcp"]
    }
  }
}

Create .vscode/mcp.json:

{
  "servers": {
    "test-intel": {
      "type": "stdio",
      "command": "npx",
      "args": ["-y", "@barissozudogru/test-intel-mcp"]
    }
  }
}

Start the local endpoint:

npx @barissozudogru/test-intel-mcp --http

The MCP endpoint is http://localhost:3000/mcp and the health endpoint is http://localhost:3000/health. Set PORT to use another port.

docker build -t test-intel-mcp .
docker run -p 3000:3000 -v "$(pwd):/project" -w /project test-intel-mcp

Connect an HTTP client to http://localhost:3000/mcp.

Local development

npm install
npm test
npm run build
node dist/index.js

Requirements: Node.js 18 or newer.

See CONTRIBUTING.md for the development workflow.

Security and limits

Analysis runs locally and does not require network access.
Paths outside the server's starting directory are rejected.
Function discovery, source-to-test matching, and complexity scoring are heuristic.
A missing matching test filename does not prove a function is behaviorally untested.

License

MIT

Available Tools

4 tools

analyze_test_coverageAnalyze Test CoverageA

Parse a coverage report (lcov, istanbul JSON, or cobertura XML) and return uncovered files, functions, lines, and branch gaps.

ParametersJSON Schema

Name	Required	Description	Default
`format`	No	Coverage format. Auto-detected from file extension if omitted.
`coverage_path`	Yes	Path to the coverage file (lcov.info, coverage-summary.json, coverage.xml, etc.)

TDQS

A3.7/5.0

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. It states what inputs it accepts and what it returns, but does not disclose side effects (though it's likely a read-only operation), error handling, or format auto-detection details (though that's in the schema). It adds some value by listing the output types (files, functions, lines, branch gaps) but lacks deeper behavioral context like return structure or failure modes.

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, well-formed sentence that conveys the key information without fluff: what it parses, which formats, and what it returns. It is efficiently front-loaded and every word earns its place, making it highly concise and well-structured.

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 tool is relatively simple (2 params, one required, no output schema). The description covers the core function but omits details like error behavior, output structure, and how it handles unsupported formats. Given the absence of annotations and output schema, the description could be more complete, but it is adequate for a basic analysis tool.

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 description coverage is 100%: both coverage_path and format have descriptions in the schema. The tool description does not add extra meaning beyond what's in the schema. Per the rubric, baseline is 3 when the schema covers all parameters, and since the description adds no additional parameter semantics, a 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 parses coverage reports in specific formats (lcov, istanbul JSON, cobertura XML) and returns uncovered files, functions, lines, and branch gaps. This specific verb+resource+output distinguishes it from sibling tools like suggest_test_cases or find_untested_functions, which focus on different aspects of test coverage.

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 usage when a developer has a coverage report and wants to identify gaps, but it does not explicitly discuss when NOT to use it or mention alternative tools. Sibling tools like find_untested_functions might be better for per-function analysis, but this is not stated. There is clear scope but no explicit exclusions or alternatives.

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

find_untested_functionsFind Untested FunctionsA

Scan source files for function/method declarations and identify those with no corresponding test file.

ParametersJSON Schema

Name	Required	Description
`test_dir`	No	Directory containing test files. Defaults to same directory as source.
`extensions`	No	File extensions to include, e.g. [".ts", ".js"]. Defaults to [".ts", ".tsx", ".js", ".jsx"].
`source_dir`	Yes	Directory containing source files to scan.

TDQS

A3.5/5.0

Behavior3/5

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

No annotations are provided, so the description bears full responsibility for behavioral disclosure. It clearly conveys a read-only scanning action, but it omits important behaviors such as how 'corresponding test file' is defined (e.g., naming convention, directory structure) and whether it actually parses code or just checks file existence. This leaves room for misinterpretation in edge cases.

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, focused sentence that immediately states the action and the criterion for identification. Every word adds value, and it is front-loaded with the primary verb 'Scan.' This is a model of conciseness—no unnecessary detail or redundancy.

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?

With no annotations and no output schema, the description must convey the tool's full operational context. It states the high-level action but does not specify what qualifies as a 'corresponding test file,' nor does it describe the return format. For a tool of this simplicity, the description is adequate but leaves some operational nuances ambiguous.

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 description coverage is 100% with each parameter clearly documented (source_dir, test_dir, extensions). However, the tool description itself adds no additional parameter-level context beyond what the schema provides. Per rubric, high schema coverage sets a baseline of 3, and the description does not exceed this.

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: 'Scan source files for function/method declarations and identify those with no corresponding test file.' This uses a specific verb and resource, and it differentiates from sibling tools like suggest_test_cases (which generates tests) and analyze_test_coverage (which measures coverage), making its unique purpose distinct.

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?

There is no guidance on when to use this tool versus alternatives. The description does not mention scenarios, exclusions, or point to any sibling tools for other tasks. Without this context, an agent has no explicit direction on when to select this over other test-related tools, leaving usage unclear.

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

get_function_complexityGet Function ComplexityA

Analyze a file's functions for cyclomatic complexity to prioritize which functions need tests most urgently.

ParametersJSON Schema

Name	Required	Description	Default
`file_path`	Yes	Path to the source file to analyze.

TDQS

A3.7/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full burden of disclosing behavior. It states that it analyzes cyclomatic complexity, but it does not mention whether the operation is read-only, whether it modifies anything, what the return format is, or error/edge-case behavior. The word 'Analyze' implies a read operation, but this is not explicit.

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. It states the action, the target, and the motivation efficiently.

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 tool is simple with one parameter and no output schema, but the description does not explain what the tool returns or how results are presented. The stated purpose mitigates this somewhat, but the absence of output details leaves a gap for the agent.

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 schema has 100% coverage with a single file_path parameter already described as 'Path to the source file to analyze.' The description adds the context of analyzing functions and complexity, but it does not add meaning beyond the schema's existing parameter documentation.

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 identifies a specific verb ('Analyze'), a specific resource ('a file's functions'), and a specific metric ('cyclomatic complexity'), with a clear purpose ('prioritize which functions need tests most urgently'). This distinguishes it from sibling tools like analyze_test_coverage or find_untested_functions.

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 implies when to use the tool: when you need complexity-based prioritization for testing. It does not explicitly mention alternatives or exclusions, but the stated purpose gives clear contextual guidance for selection.

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

suggest_test_casesSuggest Test CasesA

Analyze a specific function and generate structured test case suggestions based on its logic, branches, and type signatures.

ParametersJSON Schema

Name	Required	Description	Default
`file_path`	Yes	Path to the source file containing the function.
`function_name`	Yes	Name of the function to analyze.

TDQS

A3.7/5.0

Behavior3/5

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

With no annotations provided, the description carries the burden of behavioral disclosure. It implies a static analysis ('analyze') and mentions 'logic, branches, and type signatures' as inputs, but does not explicitly state that it does not modify code, does not execute the function, or what the output structure looks like beyond 'structured test case suggestions.'

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 that clearly conveys the action and output. Every part adds value, with no redundant or unnecessary words.

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 tool is simple with two well-documented parameters, but there is no output schema and no annotations. The description hints at 'structured test case suggestions' but does not specify the return format, side effects, or any limitations, leaving some gaps for a complete understanding.

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 schema already provides full descriptions for both parameters (file_path and function_name) with 100% coverage. The description does not add additional meaning or clarify parameter format, so the baseline score of 3 applies.

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 analyzes a specific function and generates structured test case suggestions based on logic, branches, and type signatures. This distinguishes it from sibling tools like analyze_test_coverage or get_function_complexity, which focus on coverage and complexity metrics.

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 provides context that it works on a 'specific function' and produces test case suggestions, but it does not explicitly state when to use this tool over alternatives. No exclusions or alternative tool mentions are provided, leaving some ambiguity for the agent.

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.

4 tool updatesv0.6.1Aug 20, 2026
- First observedanalyze_test_coverage
- First observedfind_untested_functions
- First observedget_function_complexity
- First observedsuggest_test_cases

Install Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

A3.9/5.0

Scored 2026-08-20 15:05 across 4 tools

Disambiguation4/5

Each tool has a distinct focus: generating test cases, parsing coverage reports, finding untested functions, and analyzing complexity. However, find_untested_functions and get_function_complexity both aim at identifying functions that need testing, which could cause some initial confusion.

Naming Consistency5/5

All tool names follow a consistent snake_case verb_noun pattern, e.g., suggest_test_cases, analyze_test_coverage, find_untested_functions, get_function_complexity. This makes the toolset predictable and easy for an agent to navigate.

Tool Count5/5

Four tools is well-scoped for a testing intelligence server, with each tool serving a clear and non-redundant purpose. There is no bloat, and the count feels sufficient for the stated domain.

Completeness4/5

The set covers a natural workflow of testing intelligence: generating tests, assessing coverage, identifying untested functions, and prioritizing via complexity. This is a solid read-only surface, though additional tools like listing existing test files or summarizing coverage trends could round it out further.

Maintenance

ActivityMaintained

ResponsivenessNo issues

Resources

Need Help?

Related Servers

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Connectors

Meeba Brain A
io.github.meemoprasad
Repository knowledge graph MCP server for codebase understanding and debugging.
Code Analysis Developer Tools
51
Testiny Test ManagementOAuth
io.testiny
An MCP server that provides access to Testiny projects, test cases and test runs
Testing & QA Tools Project Management
2
strata
ai.klavis
MCP server for progressive tool usage at any scale (see https://klavis.ai)
Agent Orchestration Autonomous Agents Developer Tools
25,748
Qase Test ManagementOAuth
io.qase
Official MCP server for Qase — manage test cases, runs, suites, defects via AI tools.
Testing & QA Tools Developer Tools
232

Related MCP Servers

codecov-mcp-server
Developer Tools Testing & QA Tools CI/CD & DevOps
stedwick
A
license
A
quality
F
maintenance
MCP server for Codecov that provides tools to get commit coverage totals and prompts to suggest tests to write.
Updated 2 years ago (2025-03-10 03:21 UTC)
1
43
6
ISC
Chisel
Developer Tools CI/CD & DevOps Code Analysis
IronAdamant
A
license
Not graded
quality
B
maintenance
MCP server for test impact analysis and code intelligence. Maps tests to code and git history to determine impacted tests, risk scores, and ownership for AI coding agents.
Updated 2 months ago (2026-07-09 02:15 UTC)
2
MIT
coverage-mcp
Testing & QA Tools Code Analysis
ProgrammerDream
F
license
A
quality
B
maintenance
MCP server for Java branch coverage analysis using JaCoCo. Enables agent-style coverage feedback with per-test-class granularity and uncovered line identification.
Updated 2 months ago (2026-07-02 17:44 UTC)
2
-
test-coverage-mcp
goldbergyoni
A
license
Not graded
quality
D
maintenance
MCP server that provides instant, token-efficient test coverage data for any programming language, allowing agents to query coverage summaries and track session progress.
Updated 7 months ago (2026-02-03 14:29 UTC)
52
41
MIT