Test Impact Analysis MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
All six tools have clearly distinct purposes: impact analysis, repo cloning, repo monitoring, risk assessment, test recommendation, and initialization. No overlap in functionality.
Naming Consistency3/5
Most tools use snake_case (impact_analysis, repo_clone, etc.), but TIA-init introduces a hyphen and different casing, breaking the pattern. Also, verb placement varies: some are noun_verb, some noun_noun.
Tool Count5/5
Six tools cover the workflow of test impact analysis without being excessive or insufficient. Each tool fulfills a necessary role.
Completeness4/5
The tool set covers initialization, monitoring, cloning, analysis, risk assessment, and recommendations. Only minor gaps like configuration management exist, but they are noted as manual edits.
Average 4.3/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 31 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is failing
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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

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of disclosure. It explains the scoring formula (files 0-60, modules 0-40, confidence penalty 0-20) and risk level thresholds, providing significant insight into tool behavior beyond parameter definitions. However, it does not mention side effects, authentication needs, or rate limits.
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 concise and well-structured, with a clear opening sentence, usage examples, and a breakdown of scoring calculation. Every sentence adds value, and there is no redundancy or filler.
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?
Given no output schema, the description explains the return values (risk score and risk levels) and mentions mitigation suggestions. It covers the key aspects: purpose, usage, scoring, and risk levels. However, it could elaborate on the structure of mitigation suggestions or provide more detail on when to use different parameters.
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?
Schema coverage is 100%, so parameters are already described. The description adds value by giving concrete usage examples (risk_assessment(name='gh-backend')) and clarifying defaults (from defaults to waterline, to defaults to remote HEAD), which enhances understanding beyond the schema.
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 clearly states the tool quantifies code change risk and generates a risk score (0-100) and mitigation suggestions. It includes examples of usage (by repo name or module), but does not explicitly differentiate from sibling tools like impact_analysis or test_recommendation.
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 examples of when to use the tool (e.g., assessing from waterline to HEAD or by module) but lacks explicit guidance on when not to use it or comparisons to alternative tools. Usage is implied but not fully contextualized.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Given no annotations, the description carries full burden and discloses key behaviors: actions, side effects (check updates watermark and dedups seenShas), and special behavior for reset with sinceDate. It lacks details on error cases, permissions, or performance, but covers the main operational traits.
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 well-structured with sections for actions, scope, and examples. It front-loads the purpose. While it could be slightly more concise, the examples provide clarity that likely aids understanding without excessive verbosity.
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 description covers all actions and parameters comprehensively, but lacks information about return values or expected output format. Since there is no output schema, the description could benefit from briefly summarizing what each action returns (e.g., status outputs). This gap limits completeness for a monitoring tool.
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?
Schema coverage is 100%, but the description adds valuable context: explains action enum in prose, clarifies the mutual exclusivity of name and module, and describes label and sinceDate in the context of the reset action. This goes beyond the schema's property descriptions.
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 the tool as a unified repository monitoring tool with three specific actions (status, check, reset) and distinct scope options. It differentiates from sibling tools like impact_analysis or repo_clone by focusing on monitoring and watermark management.
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 provides explicit usage context for each action and scope (name or module) and includes concrete examples for cron scheduling and agile reset. It does not explicitly state when not to use the tool, but the action descriptions make the appropriate use cases clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses key behaviors: auto-issuing API key, agent type detection (header/parameter), idempotency, and error handling (403). No annotations exist, so description carries full burden; it does so fairly well, though side effects on config are not fully detailed.
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?
Concise yet informative, front-loaded with 'must call on first use'. While somewhat dense, it efficiently covers purpose, prerequisites, and behavior without excess 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?
Fairly complete for a simple init tool, but lacks output format description (e.g., structure of returned command file) and error details beyond 403. Given no output schema, this is a gap.
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?
Schema coverage is 100% with enum and description. The description adds meaning by mapping agent types to command file generation, providing context beyond the schema.
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 purpose as first-time initialization for TIA MCP Server. It details specific actions (issuing API key, determining agent type, returning command file) and distinguishes itself from sibling tools by being the mandatory first step.
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?
Explicitly states it must be called first and describes prerequisites (IP whitelist) and repeatability. Lacks explicit 'when not to use' guidance, but the context strongly implies it's only for initialization before other tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It reveals the scoring formula (risk weights × confidence), thresholds, and categorization. It does not explicitly state read-only behavior, but the recommendation nature implies no destructive actions. The behavioral context is well disclosed.
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 concise with no wasted words. It uses an effective structure: main purpose, usage examples, and formula in separate blocks. Every sentence adds information.
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 description assumes knowledge of 'Phase 2' without explanation. It describes output as categories and a minimal test set, but does not specify exact output structure (e.g., list of test cases with scores). Given no output schema, more detail on return format is needed.
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?
Schema coverage is 100%, baseline 3. The description adds value by giving concrete examples (e.g., name='gh-backend' for repo alias, module='用户中心' for module analysis) and explaining defaults for from/to (current watermark, remote HEAD). This enriches the schema.
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 it recommends test execution order based on code changes and Phase 2 impact analysis, generating a minimal viable test set. The examples further clarify usage with name and module, distinguishing it from sibling tools like impact_analysis and risk_assessment.
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 provides explicit usage examples (name vs module) and explains the scoring formula and threshold categories. It implies that Phase 2 analysis should be done first, but does not explicitly state when not to use this tool or directly compare with all siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description fully bears the burden. It discloses storage paths by repoType, automatic transport-mode behavior (stdio vs http), force override, and remote-mode return behavior. Prerequisites for incremental mode are also detailed.
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?
Well-structured with sections and bullet points, front-loading the core purpose. While lengthy, the complexity of the tool justifies the length. No redundant sentences.
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 7 parameters, 1 required, no output schema, and moderate complexity, the description thoroughly covers all aspects: positioning, modes, storage, transport behavior, and prerequisites. Agent can confidently invoke without ambiguity.
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?
Input schema has 100% description coverage, so baseline is 3. The description adds significant semantic context: clarifies the relationship between name/module, elaborates on mode effects, explains sinceDate/sinceMrId, and provides transport-specific clientBaseDir meaning.
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 it clones monitoring repository code, specifying two positioning methods (name/module) and two cloning modes (full/incremental). It distinguishes from sibling tools like impact_analysis or repo_monitor, which serve different purposes.
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 explicit guidance on when to use full vs incremental cloning, and how to choose between name and module. Includes prerequisites for incremental mode. Lacks explicit when-not-to-use scenarios, but usage context is well-covered.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that it returns a priority-sorted list of affected test cases and mentions the config file rules. It could be improved by noting any potential side effects, but for a read-only analysis tool, the information is adequate.
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 concise but well-structured with usage examples and prerequisites. Every sentence adds value, and the bullet-point format enhances readability.
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 rich input schema and no output schema, the description provides sufficient context: it explains the return type (priority-sorted list), includes prerequisites, and offers multiple usage patterns, making it complete for an AI agent to correctly invoke the tool.
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 input schema covers 100% of parameters, so the baseline is 3. The description adds value by explaining the mutual exclusivity of 'name' and 'module', and through examples, it clarifies optional parameters and default behaviors (watermark, HEAD).
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 '分析代码变更对测试用例的影响' (analyze impact of code changes on test cases), which is specific and distinguishes from sibling tools like repo_clone, repo_monitor, risk_assessment, test_recommendation, and TIA-init.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly provides usage examples with different parameter combinations (name, from-to, module) and lists prerequisites (repo_monitor initialized, config file edited), making it clear when and how to use the tool.
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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