mcp_engram_search_by_relation

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

No annotations provided, so description carries full burden. It explains behavior: returns relations, filtered by label/direction, k limits results. Warns about potential data overload. Missing explicit side-effect declaration, but search is likely read-only.

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?

Two focused sentences plus a note. Front-loaded with purpose, then usage advice. No wasted words.

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?

Comprehensive for a graph traversal tool: purpose, parameters, usage advice, and alternatives. No output schema needed as return is typical relation list.

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 coverage is 100%, baseline 3. Description adds context: explains direction enum and k limit importance for scoping. Provides practical usage advice beyond schema.

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 traverses the knowledge graph to find concepts related to a seed, with filtering by label and direction. It distinguishes from sibling tools like mcp_engram_query_pure or mcp_engram_read_concept by focusing on relation traversal.

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?

Provides explicit guidance: use label, direction, and k to keep results small; start narrow and drill down with visualize or context/recall. Mentions avoiding data overload on high-relation nodes and suggests alternatives.

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