test-intel-mcp
Server Quality Checklist
Latest release: v0.6.1
- 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 Consistency5/5All 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 Count5/5Four 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.
Completeness4/5The 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.
Average 3.7/5 across 4 of 4 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 12 commits in the last 12 weeks
- Last stable release on
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add
glama.jsonto the root of your repository:{ "$schema": "https://glama.ai/mcp/schemas/server.json", "maintainers": [ "your-github-username" ] }Then . Browse examples.
Add related servers to improve discoverability.
How to sync the server with GitHub?
Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.
To manually sync the server, click the "Sync Server" button in the MCP server admin interface.
How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
Tool Scores
- 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.
Conciseness5/5Is 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.
Completeness3/5Given 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.
Parameters3/5Does 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.
Purpose5/5Does 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 Guidelines2/5Does 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.
- 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.
Conciseness5/5Is 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.
Completeness3/5Given 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.
Parameters3/5Does 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.
Purpose5/5Does 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 Guidelines3/5Does 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.
- 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.
Conciseness5/5Is 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.
Completeness3/5Given 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.
Parameters3/5Does 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.
Purpose5/5Does 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 Guidelines4/5Does 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.
- 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.
Conciseness5/5Is 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.
Completeness3/5Given 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.
Parameters3/5Does 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.
Purpose5/5Does 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 Guidelines3/5Does 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.
GitHub Badge
Glama performs regular codebase and documentation scans to:
- Confirm that the MCP server is working as expected.
- Confirm that there are no obvious security issues.
- Evaluate tool definition quality.
Our badge communicates server capabilities, safety, and installation instructions.
Card Badge
Copy to your README.md:
Score Badge
Copy to your README.md:
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/barissozudogru/test-intel-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server