Actual group – offres d'emploi

List offer filter values

list_offer_facets

Read-only

Exact filter values accepted by search_offers for one dimension (contract type, job family, industry or region), with the number of open offers for each. Optionally scoped by a query and a town.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "dimension": "<dimension>"
    },
    "name": "list_offer_facets"
  }
}

Input Schema

Name	Required	Description
`q`	No	Optional free text to scope the counts.
`town`	No	Optional town or postal code to scope the counts.
`dimension`	Yes	Which filter to list.

Schema Changelog

Changes observed during successful MCP inspections.

Changed4 schema fields changedNov 9, 2026

removedInput schema / properties / context

Removed value: -{
-  "description": "Why are you calling this tool? Describe the job seeker's or recruiter's goal in one short sentence. Do not include names, emails or phone numbers.",
-  "type": "string"
-}

removedInput schema / properties / conversation_id

Removed value: -{
-  "description": "Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request.",
-  "type": "string"
-}

removedInput schema / properties / llm_model

Removed value: -{
-  "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess.",
-  "type": "string"
-}

changedInput schema / required

Previous value: -[
-  "dimension",
-  "context",
-  "llm_model"
-]New value: +[
+  "dimension"
+]

Changed1 schema field changedOct 23, 2026

changedInput schema / properties / context / description

Previous value: -"Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\""New value: +"Why are you calling this tool? Describe the job seeker's or recruiter's goal in one short sentence. Do not include names, emails or phone numbers."

Changed4 schema fields changedOct 22, 2026

addedInput schema / properties / context

Added value: +{
+  "description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\"",
+  "type": "string"
+}

addedInput schema / properties / conversation_id

Added value: +{
+  "description": "Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request.",
+  "type": "string"
+}

addedInput schema / properties / llm_model

Added value: +{
+  "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess.",
+  "type": "string"
+}

changedInput schema / required

Previous value: -[
-  "dimension"
-]New value: +[
+  "dimension",
+  "context",
+  "llm_model"
+]

First observedOct 16, 2026

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds useful behavior beyond annotations: it discloses that the tool returns exact filter values with open-offer counts and that results can be optionally scoped by a query and town.

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?

Two sentences with no wasted words. The core purpose and return value are front-loaded, followed by the optional scoping parameters.

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, the description fills an important gap by explaining that the result includes each filter value and its count of open offers. It also covers parameter roles and the relationship to search_offers, leaving only minor details like pagination or result limits unaddressed.

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 the schema already documents all three parameters with their types and descriptions. The description maps the dimension enum values to human-readable labels and mentions query/town scoping, but adds little beyond what the schema provides.

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 (list) and resource (offer filter values / facets for one dimension), and explicitly ties the tool to search_offers. It names the four possible dimensions and says the output includes counts of open offers, making it distinguishable from sibling search and lookup tools.

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

Usage Guidelines4/5

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

The description makes clear this tool supplies the exact filter values accepted by search_offers, which implies when to use it. It does not explicitly state when not to use it or name alternatives among the siblings, so it falls short of a full 5.

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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