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boondmanager-mcp-server

Récap hebdomadaire (moi + mon équipe)

boond_workflow_recap_hebdo
Read-onlyIdempotent

Generate a weekly recap runbook for you and your team: ordered Boond API calls with exact filters for opportunities, projects, absences, and CRA. Execute them to see the week at a glance.

Instructions

Compile en une vue ce qui s'est passé / va se passer cette semaine pour moi et mon équipe : opportunités, projets, absences, CRA.

Quand : pour dérouler ce scénario multi-étapes sans avoir à retrouver soi-même le bon enchaînement d'outils et les bons noms de filtres. Plutôt que : le prompt MCP recap_hebdo si le client l'expose — contenu identique, sans consommer un appel d'outil. Cette variante existe pour les clients qui traitent mal prompts/get (claude.ai notamment).

  • N'appelle aucune API BoondManager et ne lit aucune donnée : la réponse est générée côté serveur MCP.

Returns : un runbook en texte — la liste ordonnée des appels Boond à effectuer, avec les filtres exacts. C'est ensuite au modèle de les exécuter ; rien n'est fait par cet appel.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
semaineNoSemaine ciblée (ex: 'cette semaine', 'la semaine prochaine'). Défaut: cette semaine.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv2.12.2
    • removedInput schema / $schema
      Removed value: -"http://json-schema.org/draft-07/schema#"
  2. First observedv2.1.0

TDQS

A4.2/5.0
Behavior4/5

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

Beyond the annotations (readOnlyHint, idempotentHint, destructiveHint), the description adds critical behavioral context: it calls no BoondManager API, reads no data, generates the response server-side, and performs no actions on the model's behalf. This prevents a serious mischaracterization of the tool's side effects. The only slight weakness is the opening 'Compile' wording, which initially suggests more data access than actually occurs.

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 well organized with 'Quand', 'Plutôt que', and 'Returns' sections, making it easy to scan. It is slightly repetitive around 'no API call / nothing is done by this call', but each sentence earns its place and the key behavior is front-loaded in the Returns section.

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 simple tool with one optional parameter and no output schema, the description fully explains what the agent receives, what it should do with it, and what the tool does not do. The runbook output format, the exact-filter content, and the follow-up execution by the model are all explicitly covered, so an agent can invoke 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?

Schema coverage is 100%, so the baseline is 3. The one parameter, `semaine`, is already well documented in the schema with examples and a default; the description adds no additional semantics beyond the general weekly framing.

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

Purpose4/5

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

The description names the scenario clearly: it compiles a weekly view of opportunities, projects, absences, and CRA for the user and their team. However, the opening sentence could be misread as an actual data summary, and the real behavior—producing a text runbook—only becomes fully clear in the Returns section. It also does not distinguish itself from sibling workflow tools, though the name and scenario help.

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?

Explicitly states when to use the tool ('pour dérouler ce scénario multi-étapes sans avoir à retrouver soi-même le bon enchaînement d'outils') and when not to, naming the alternative prompt `recap_hebdo` and the exact condition for preferring it. This gives an agent actionable routing guidance.

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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