Convert Learning Forge source leads into executable lab contracts with measurements, failure cases, and five-flagship ownership. Returns a read-only JSON labs packet.
Use when Learning Forge source leads need executable lab contracts with measurements, failure cases, and five-flagship ownership. Read-only, zero-auth, no external side effects. Returns a JSON Learning Forge labs packet.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.3.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations supplied, the description carries the full behavioral burden, and it does disclose the important traits: read-only, zero-auth, no external side effects, and that the result is a JSON packet. It omits softer traits such as determinism, whether labs are persisted, or idempotency on repeat calls, which prevents a 5.
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?
Three short sentences, front-loaded with the trigger condition, and each sentence adds a distinct fact (when to use, safety envelope, return shape). The dense internal jargon costs readability, but there is no filler or repetition.
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?
For a 0-param tool with no annotations and no output schema, the description covers the essentials an agent needs: trigger condition, safety profile, and the return contract ('JSON Learning Forge labs packet'). The absence of any structural detail about the packet contents is acceptable given no output schema exists, but it leaves the result shape only nominally specified.
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 tool takes zero parameters and the description introduces none, so there is no syntax or meaning to explain. Baseline for a 0-parameter tool is 4; nothing in the text misleads about inputs.
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 names a concrete output (executable lab contracts with measurements, failure cases, and five-flagship ownership) built from a named input source (Learning Forge source leads), so an agent can tell what it produces rather than just restating the name. It does not explicitly contrast itself with the adjacent telos.learning.forge sibling, and the domain jargon ('five-flagship ownership', 'source leads') keeps it from being fully unambiguous, so it lands at clear-but-undifferentiated.
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?
'Use when Learning Forge source leads need executable lab contracts' gives an explicit triggering condition tied to a sibling concept, which is real routing guidance. However, it names no alternative tool and gives no exclusions or 'when not to use' case, so it stops short of full when/when-not coverage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.