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suggest_adrs

Generate architectural decision records by analyzing code changes, project history, and context to maintain consistency and document technical choices.

Instructions

Suggest architectural decisions with advanced prompting techniques (Knowledge Generation + Reflexion). TIP: Read @.mcp-server-context.md first for project history, patterns, and previous ADRs to ensure consistency.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
projectPathNoPath to the project directory.
analysisTypeNoType of analysis to performcomprehensive
beforeCodeNoCode before changes (for code_changes analysis)
afterCodeNoCode after changes (for code_changes analysis)
changeDescriptionNoDescription of the changes (for code_changes analysis)
commitMessagesNoRelated commit messages (for code_changes analysis)
existingAdrsNoList of existing ADR titles to avoid duplication
enhancedModeNoEnable advanced prompting features (Knowledge Generation + Reflexion)
learningEnabledNoEnable Reflexion learning from past experiences
knowledgeEnhancementNoEnable Knowledge Generation for domain-specific insights
conversationContextNoRich context from the calling LLM about user goals and discussion history
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 behavioral disclosure. While it mentions 'advanced prompting techniques (Knowledge Generation + Reflexion),' it doesn't explain what these techniques entail, how the tool behaves (e.g., does it generate text, modify files, or just return suggestions?), potential side effects, or any limitations like rate limits or authentication needs. For a complex tool with 11 parameters, this is a significant gap.

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 appropriately sized with two sentences: one stating the purpose and another providing a usage tip. It's front-loaded with the core function, and the TIP adds value without unnecessary verbosity. However, the parentheses in the first sentence ('Knowledge Generation + Reflexion') could be slightly distracting, but overall it's efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (11 parameters, no annotations, no output schema), the description is incomplete. It lacks details on what the tool returns (e.g., suggested ADR text, structured data, or file outputs), how it integrates with the project (e.g., does it write to files?), and behavioral aspects like error handling. The TIP helps but doesn't fully address these gaps for such a multifaceted 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%, so the schema already documents all 11 parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema, such as explaining how parameters interact (e.g., 'analysisType' influences which other parameters are relevant). With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but also doesn't detract.

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's purpose: 'Suggest architectural decisions with advanced prompting techniques (Knowledge Generation + Reflexion).' This specifies both the action (suggest) and resource (architectural decisions), though it doesn't explicitly differentiate from sibling tools like 'generate_adr_bootstrap' or 'generate_adrs_from_prd' which might have overlapping 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 provides clear context for usage with the TIP: 'Read @.mcp-server-context.md first for project history, patterns, and previous ADRs to ensure consistency.' This gives practical guidance on prerequisites, but it doesn't explicitly state when to use this tool versus alternatives like 'generate_adr_from_decision' or 'validate_adr' among the many sibling tools.

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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