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silamir

boondmanager-mcp-server

by silamir

Poser un drapeau sur un enregistrement

boond_flags_attach
Idempotent

Attach an existing flag to a record to tag it (e.g., mark a candidate after sorting). One call per record; idempotent, re-attaching does nothing.

Instructions

Pose un drapeau existant sur un enregistrement (tagging) — POST /attached-flags.

Quand : pour marquer un lot de fiches après un tri (« ces 12 candidats → vivier Java Q4 ») : un appel par fiche. Plutôt que : boond_flags_create si le drapeau n'existe pas encore ; boond_flags_detach pour le retirer.

  • Corps JSON:API { relationships: { flag, dependsOn } } déduit du modèle — la RAML ne documente que la route ; non éprouvé sur un tenant de test.

  • Idempotent côté intention : reposer un drapeau déjà présent ne doit rien changer.

Returns : { entity, id, flagId, attached: true }.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesID numérique de l'enregistrement.
entityYesType de l'enregistrement.
flagIdYesID du drapeau (`boond_flags_search`).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYes
entityYes
flagIdYes
attachedYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv2.17.0

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare idempotentHint=true and destructiveHint=false, and the description adds a specific clarification (reposing an existing flag changes nothing) plus the JSON:API body format and a candid warning that the route is untested on a test tenant. This goes well beyond the annotations, providing genuine behavioral context.

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?

The description is concise and well-organized: purpose first, then when-to-use, then technical body details and return format. It is front-loaded with the most important usage guidance. Slightly dense but not bloated; each sentence adds value.

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

Completeness5/5

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

For a write operation with three parameters, annotations, and an output schema, the description covers purpose, usage context, alternatives, idempotency semantics, request body structure, and return format. It even discloses the untested status, leaving no critical gap for an agent to call it correctly.

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?

The schema already documents all three parameters (id, entity, flagId) with descriptions and patterns (100% coverage), so the baseline is 3. The description mentions the request body but does not add parameter-specific meaning beyond what the schema provides, so no higher score is warranted.

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?

The description states a specific verb ('Pose') and resource (an existing flag on a record), and clearly distinguishes it from siblings by naming boond_flags_create (if flag doesn't exist) and boond_flags_detach (to remove). This allows an agent to select the correct tool without ambiguity.

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?

Explicit 'Quand' (when) provides a concrete scenario (marking a batch after sorting) and 'Plutôt que' (rather than) names the exact alternatives and their conditions. This is fully actionable guidance with no inference required.

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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