MCP RAG 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 'semantic similarity' as the search method but doesn't describe other behavioral traits such as performance characteristics (e.g., speed, accuracy), error handling, or what happens if no results are found. For a search tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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: 'Search for relevant document chunks using semantic similarity.' It is front-loaded with the core purpose, has zero wasted words, and is appropriately sized for the tool's complexity. Every part of the sentence earns its place by specifying key details.

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 moderate complexity (2 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and method but lacks details on usage guidelines, behavioral traits, and output expectations. Without an output schema, the description doesn't explain return values, leaving the agent to infer results from the context. It meets the minimum viable standard but has clear gaps.

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?

The input schema has 100% description coverage, with clear documentation for both parameters ('query' and 'k'). The description adds no additional meaning beyond the schema, such as explaining how 'semantic similarity' applies to the query or detailing result formats. Since the schema does the heavy lifting, the baseline score of 3 is appropriate.

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: 'Search for relevant document chunks using semantic similarity.' It specifies the action (search), the target (document chunks), and the method (semantic similarity). However, it doesn't explicitly differentiate from sibling tools like 'get_chunk' (which might retrieve a specific chunk) or 'refresh_index' (which might update search indices).

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 sibling tools like 'get_chunk' (for direct retrieval), 'ingest_docs' (for adding documents), or 'refresh_index' (for updating indices), nor does it specify contexts or prerequisites for use. The agent must infer usage from the purpose alone.

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