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Convertir des règles en instructions

ficiel_convert_learned_patterns
DestructiveIdempotent

Ajoute le texte de règles proposées ou adoptées aux instructions personnalisées de Kord (lisibles et modifiables par ficiel_edit_agent_prompt), puis les retire de la liste des règles apprises. Une règle qui n'est pas dans l'état de départ attendu n'est pas touchée ; le nombre de règles modifiées est rendu (updated). Portée « write » requise.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pattern_idsYesIdentifiants des règles (pattern_id, voir ficiel_list_learned_patterns).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare destructiveHint=true, idempotentHint=true and readOnlyHint=false, so the safety profile is partly covered. The description adds real context beyond that: the appended text is user-visible and editable, the rule is deleted from the learned list, non-conforming rules are left untouched, and the return value ('updated' count) plus the write-scope requirement are disclosed. Only the atomicity/partial-failure semantics of a multi-id call remain unstated.

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

Conciseness4/5

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

Three sentences, front-loaded with the action, with no redundant padding; each clause carries a distinct fact (what is written, what is removed, precondition, return value, scope). Slightly dense but efficient for a mutation with a precondition.

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?

With no output schema and a single fully documented parameter, the description still supplies the pieces an agent needs: the destination of the migrated text, the removal side effect, the state precondition, the returned count, and the required write scope. An explanation of partial success behavior when some ids fail would fully close the gap.

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

Parameters3/5

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

Schema description coverage is 100%, so pattern_ids (uuid array, 1-100 items) and its link to ficiel_list_learned_patterns are already documented in the schema. The description adds no format or selection detail beyond that, so the baseline 3 applies.

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?

States a specific compound verb (append rule text to Kord's custom instructions, then remove them from the learned-rules list) and names the exact resource. It cross-references ficiel_edit_agent_prompt and ficiel_list_learned_patterns, so an agent can place it among the other learned-pattern siblings without opening their schemas.

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

Usage Guidelines3/5

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

Usage is implied: the description explains what the operation does and where the resulting instructions can be read/edited, and the precondition (a rule not in the expected starting state is untouched). However it never states when to choose this over the close siblings ficiel_approve_learned_patterns, ficiel_dismiss_learned_patterns or ficiel_ne_plus_proposer, leaving the routing decision to inference.

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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