DevLens MCP

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 'smart intent classification and dynamic workflow generation,' which hints at internal logic, but doesn't disclose critical traits like whether it's read-only (likely, but not stated), performance characteristics, rate limits, authentication needs, or error behavior. For a tool with no annotation coverage, this leaves significant gaps in understanding how it behaves.

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 well-structured and front-loaded: the first sentence states the core purpose, followed by elaboration and a clear Args/Returns section. Every sentence adds value—explaining functionality, parameters, and output—with zero waste. It's appropriately sized for the tool's complexity.

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 no annotations, 0% schema description coverage, but an output schema exists, the description is moderately complete. It covers purpose, parameters, and return values at a high level, but lacks usage guidelines and detailed behavioral context. The output schema likely documents the return structure, so the description doesn't need to explain that deeply, but overall it's adequate with clear gaps for a tool in a crowded sibling set.

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 0%, so the description must compensate. It adds meaningful context: 'query: Research question or task description' and 'known_urls: Optional list of already known URLs (default None).' This clarifies the purpose of each parameter beyond their schema types (string and array), though it doesn't detail format constraints or examples. With 2 parameters and no schema descriptions, this provides adequate semantic value.

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: 'Suggest optimal research workflow for a query' and elaborates with 'Analyzes the query and recommends the best tools and workflow to answer it.' It specifies the verb (suggest/recommend) and resource (workflow/tools), but doesn't explicitly differentiate from sibling tools like tool_classify_research_intent or tool_deep_dive, 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.

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. With siblings like tool_classify_research_intent, tool_search_web, and tool_deep_dive, there's no indication of whether this tool should be used first in a research process, when it's preferable to direct tool invocation, or what scenarios it's designed for. The lack of context leaves the agent guessing about its role.

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