MCP Server with OpenAI Integration

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure. While it mentions the tool uses 'the configured LLM,' it doesn't disclose important behavioral traits like rate limits, authentication requirements, cost implications, or what happens with very long inputs. The description is minimal and lacks operational context.

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 extremely concise - a single sentence that efficiently communicates the core functionality. Every word earns its place, and it's front-loaded with the essential information. No wasted words or unnecessary elaboration.

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 has no annotations, no output schema, and 0% schema description coverage, the description is inadequate. It doesn't explain what the tool returns, how the summarization works, quality expectations, or error conditions. For a tool that processes text with an LLM, this leaves significant gaps in understanding.

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 mentions 'configured LLM' which relates to the 'model' parameter, but doesn't explain the 'text' parameter's requirements or constraints. The description adds minimal value beyond what the bare schema provides, failing to adequately compensate for the 0% coverage.

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: 'Summarizes long text into concise bullet points using the configured LLM.' It specifies the verb ('summarizes'), resource ('long text'), and output format ('concise bullet points'). However, it doesn't explicitly differentiate from the sibling tool 'knowledge_search' (which likely searches rather than summarizes).

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 the sibling tool 'knowledge_search' or any other summarization methods. There's no context about when this tool is appropriate versus other approaches.

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