Graforest MCP

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

Annotations already indicate read-only, non-destructive, and idempotent behavior, which the description doesn't contradict. The description adds valuable context by specifying that this tool should be called first to understand available types and fields, which goes beyond annotations by explaining its preparatory role in the workflow. However, it doesn't mention potential rate limits or authentication needs, leaving some behavioral aspects uncovered.

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 two sentences that are front-loaded with the core purpose and followed by actionable guidance. Every word earns its place—there's no redundancy or fluff, making it highly efficient and easy to parse for an AI agent.

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 (schema retrieval with parameters) and lack of output schema, the description does well by emphasizing its preparatory role. However, it doesn't detail what the schema output looks like (e.g., structure or format), which could be helpful since there's no output schema. The annotations cover safety aspects, but more context on return values would enhance completeness.

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 50%, with one parameter ('project_code') well-described in the schema. The description doesn't add any parameter-specific information beyond what the schema provides, such as explaining the 'environment' enum choices or how 'project_code' relates to sibling tools. Since schema coverage is moderate, the description doesn't fully compensate, resulting in a baseline score.

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 verb ('Get') and resource ('full schema — entity types with fields, relationship types with from/to mappings'), making the purpose specific. It distinguishes from siblings by explicitly mentioning it should be called 'before adding nodes or relationships,' which differentiates it from tools like 'add_knowledge_nodes' or 'search_knowledge_graph' that operate on the schema itself.

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 explicit guidance on when to use this tool: 'CALL THIS FIRST before adding nodes or relationships to understand what types and fields are available.' This clearly indicates its role as a prerequisite for other operations, distinguishing it from alternatives like 'get_knowledge_entity' or 'list_knowledge_entities' that work with existing data.

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