mcp-adr-analysis-server

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. It mentions 'AI-powered dynamic tool sequencing' and 'intelligently analyze,' but doesn't detail how the tool behaves—e.g., whether it's read-only or mutative, what permissions are needed, how it handles errors, or what the output format is. For a tool with 6 parameters and no annotations, this is a significant gap in transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence: 'AI-powered dynamic tool sequencing - intelligently analyze user requests and generate structured tool execution plans.' It's front-loaded with the core purpose, uses clear language, and has zero wasted words, 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.

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

Given the tool's complexity (6 parameters, no annotations, no output schema), the description is incomplete. It lacks details on behavioral traits, output format, and usage guidelines. While the schema covers parameters well, the description doesn't compensate for the absence of annotations or output schema, leaving gaps in understanding how the tool operates and what it returns.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

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 parameters thoroughly. The description doesn't add any specific meaning beyond what the schema provides, such as explaining the nuances of 'executionMode' or 'contextHints.' However, with high schema coverage, the baseline is 3, as the schema does the heavy lifting for parameter documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'AI-powered dynamic tool sequencing - intelligently analyze user requests and generate structured tool execution plans.' It specifies the verb ('analyze' and 'generate') and resource ('tool execution plans'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'mcp_planning' or 'bootstrap_validation_loop', which might have overlapping planning functions, so it doesn't reach the highest score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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 alternatives. It doesn't mention any prerequisites, exclusions, or specific contexts for use, such as comparing it to 'mcp_planning' or other planning-related siblings. This lack of explicit usage instructions leaves the agent without clear direction on tool selection.

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