Graforest MCP

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

The description adds valuable behavioral context beyond annotations: it explains the tool's role in a multi-step workflow, mentions the 500k character limit (implied from input schema but emphasized), and describes the output ('schema + instructions'). Annotations already cover safety (readOnlyHint=true, destructiveHint=false), so the description appropriately focuses on operational behavior without contradiction.

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: it starts with the core purpose, then details the workflow, and ends with key benefits. Every sentence adds value—no fluff or repetition. The bullet-point workflow enhances readability without wasting space.

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 (multi-step workflow) and rich annotations (covering safety and idempotency), the description is complete: it explains the tool's role, workflow, alternatives, and constraints. Although there's no output schema, the description specifies the return ('schema + instructions'), which is sufficient for an agent to proceed correctly.

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 parameters thoroughly. The description adds minimal parameter-specific information beyond the schema (e.g., 'large block of text' aligns with text_content). It does not provide additional syntax, format, or constraints, so it meets the baseline for high schema 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: 'BATCH INGESTION — the fast way to populate a knowledge graph.' It specifies the verb ('ingest'), resource ('text content'), and scope ('batch' vs 'per-entity'), distinguishing it from sibling tools like add_knowledge_nodes which handles only nodes. The description explicitly contrasts with 'per-entity approach,' making the distinction clear.

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 usage guidance: it outlines a '3-CALL WORKFLOW' with named alternatives (add_knowledge_nodes, add_knowledge_relationships), specifies when to use this tool ('BATCH INGESTION — the fast way'), and when not to use it ('replaces per-entity approach'). It clearly defines the context for invoking this tool first in a sequence.

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