Append one evidence-backed lesson to a configured ledger, recording subject, lesson, and verified evidence for reviewed conclusions.
Append one evidence-backed lesson to the configured ledger.
| Name | Required | Description | Default |
|---|---|---|---|
| lesson | Yes | ||
| subject | Yes | ||
| source_refs | No | ||
| verified_evidence | Yes |
Changes observed during successful MCP inspections.
v1.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are absent, so the description bears full responsibility for disclosing behavioral implications. It mentions 'append' which is a mutation, but does not disclose side effects, idempotence, or any constraints on the ledger.
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 sentence with no filler or redundant information, making it highly concise.
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?
The tool has four parameters and no output schema, yet the description omits parameter meanings, return behavior, and error conditions. The minimal detail may leave an AI agent uncertain about how to correctly invoke the tool.
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 has 0% description coverage, and the description does not explain the meanings of subject, lesson, verified_evidence, or source_refs. The agent must infer semantics from parameter names alone.
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 specifies the verb 'append', the resource 'evidence-backed lesson', and the destination 'configured ledger', making the primary function clear. However, it does not explicitly differentiate from sibling tools like evidence_create_note, though the resource type differs.
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 gives no instructions on when to use this tool in preference to siblings such as evidence_read or evidence_search. No prerequisites, exclusions, or alternative scenarios are mentioned, leaving the agent without explicit guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.