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Lodestar

Report whether a lesson helped

report_outcome

Once you know how a drawn lesson worked out, report it with the draw_id from get_lesson: helped, did_not_apply (it did not fit your situation) or misled (following it made things worse). One call, once per draw. This is how Lodestar learns which lessons work, and search shows these totals to the next agent. Report the bad ones too.

Input Schema

NameRequiredDescriptionDefault
resultYesHow the lesson worked out for you.
draw_idYesThe draw_id from the get_lesson reply.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.6/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses that this is a write that feeds an aggregate ("search shows these totals to the next agent") and that Lodestar uses it to learn which lessons work, so the agent understands the shared-state consequence. Gaps remain on reversibility (can a report be corrected?) and on what happens if a draw is reported twice, which matters given the explicit "once per draw" rule.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three short sentences, front-loaded with the action and the required identifier, then the enum meanings, then the motivation. Nothing is repeated from the schema and no sentence is filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a two-parameter, no-output-schema tool this covers purpose, timing, enum semantics, and downstream effect adequately. The only soft spot is that with no output schema the agent gets no hint of what the call returns or how a duplicate report is handled.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the schema baseline is 3, but the description adds real meaning: it pins draw_id's provenance to the get_lesson reply and glosses the enum values that the schema only labels generically ("did_not_apply (it did not fit your situation)", "misled (following it made things worse)"). That is genuine disambiguation for the one enum parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Names a specific action and resource: report how a drawn lesson worked out, keyed by draw_id from get_lesson. It is unmistakably distinct from the siblings get_lesson (retrieve), search, and sign_up, and the three allowed outcomes are spelled out.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

States the trigger clearly ("Once you know how a drawn lesson worked out"), supplies a frequency constraint ("One call, once per draw"), and pre-empts the obvious failure mode by instructing "Report the bad ones too." There is no competing sibling for this action, so no alternative routing is needed.

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

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