Apply forward-chaining IF/THEN rules to input facts, deriving new facts for logical inference and decision auditing.
Run forward-chaining IF/THEN rules over a set of facts to derive new facts.
| Name | Required | Description | Default |
|---|---|---|---|
| facts | Yes | List of fact strings, e.g. ['Person(John)', 'Employee(John)'] | |
| rules | Yes | IF/THEN rule strings, e.g. ['IF Employee(?x) THEN WorkerBee(?x)'] |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description must carry the full transparency burden. It never states whether derived facts are persisted, returned transiently, or materialized into the graph, leaving side effects ambiguous for a non-read-only-looking operation.
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?
One tight sentence that front-loads the verb, key input concept (facts + rules), and purpose. No filler or redundancy.
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?
Without annotations or an output schema, the description should clarify what the caller receives and whether state changes. It does not disclose persistence, return value, failure behavior, or inference output structure, leaving an agent with incomplete execution expectations.
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 already documents both parameters with examples, so the baseline is adequate. The description's 'IF/THEN' and 'forward-chaining' wording clarifies rule semantics but adds no new constraints, formats, or parameter-level behavior.
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?
Description states a specific action ('Run forward-chaining IF/THEN rules'), a clear input ('facts'), and a distinct outcome ('derive new facts'). This clearly differentiates the tool from sibling extraction, query, and graph-mutation tools.
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 implies a clear usage context: use when you have facts plus rules and want derived facts. It does not explicitly name exclusions or when not to use it, but it is specific enough to route an agent effectively.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.