Elasticsearch Knowledge Graph for 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 that the tool 'uses AI to retrieve relevant entities and relations', implying intelligent filtering, but doesn't disclose critical behaviors such as rate limits, authentication requirements, error handling, or what 'retrieve' entails (e.g., pagination, format of results). For a query tool with 7 parameters and no output schema, this lack of behavioral context 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.

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

The description is a single, well-structured sentence that efficiently conveys the core purpose without redundancy. It uses clear language ('Agent driven knowledge graph inspection', 'uses AI', 'retrieve relevant entities and relations', 'based on a query') and avoids unnecessary details, making it easy to parse and understand quickly. Every word earns its place.

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 complexity (7 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain what the tool returns (e.g., structure of entities/relations, pagination), how the AI-driven retrieval works, or any limitations. Without annotations or output schema, the agent lacks sufficient context to understand the tool's full behavior and results, making this inadequate for effective use.

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 7 parameters thoroughly. The description doesn't add any parameter-specific semantics beyond what's in the schema (e.g., it doesn't explain how 'information_needed' and 'keywords' interact or provide examples). With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract from the schema's 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: 'Agent driven knowledge graph inspection that uses AI to retrieve relevant entities and relations based on a query.' It specifies the verb ('retrieve'), resource ('entities and relations'), and mechanism ('based on a query'). However, it doesn't explicitly differentiate from sibling tools like 'search_nodes' or 'get_recent', which appear related to knowledge graph operations, leaving some ambiguity about when to choose this specific inspection tool.

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 of the sibling tools (e.g., 'search_nodes', 'get_recent', 'open_nodes') that might overlap in functionality, nor does it specify prerequisites, exclusions, or typical use cases. The agent must infer usage from the purpose alone, which is insufficient for optimal tool selection.

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