Costory: Your Finops MCP

list_teams

Read-only

List all non-archived teams in the organization, including teams the current user is not a member of. Each team has an id (pass as teamId on set_object_permissions), visibility, and role (the caller's membership role when they belong to the team; omitted otherwise). Each scope has an id (pass as scopeId to query or a report widget to apply the saved scope) and a pre-defined filterCel string (pass as filterCel for the same effect inline). Optional query filters by team name. Use when the user asks about a team's costs, wants to filter by team scope, or needs a teamId to share a dashboard, report, budget, or cost alert.

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": {
      "context": "<context>"
    },
    "name": "list_teams"
  }
}

Input Schema

Name	Required	Description
`slug`	No	Organization slug. Omit to auto-detect from your account (fails if you belong to multiple orgs).
`query`	No	Optional team-name search. Omit to list every non-archived team in the organization.
`context`	Yes	A few words on why you are calling this tool, for the server developer's analytics. Describe people and things by role, such as "a customer". Do not quote the user or include names, emails, or other personal details.
`llm_model`	No	The model identifier you are running as, for example "claude-opus-4-8" or "gpt-5.2". Pass "unknown" if you are not certain. Recorded for the server developer's analytics.
`conversation_id`	No	The conversation_id this server returned earlier in this conversation, unchanged, including after later user messages. Omit it if you have none. Recorded for the server developer's analytics.

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedNov 14, 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: +"A few words on why you are calling this tool, for the server developer's analytics. Describe people and things by role, such as \"a customer\". Do not quote the user or include names, emails, or other personal details."

changedInput schema / required

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

Changed4 schema fields changedNov 12, 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": "The conversation_id this server returned earlier in this conversation, unchanged, including after later user messages. Omit it if you have none. Recorded for the server developer's analytics.",
+  "type": "string"
+}

addedInput schema / properties / llm_model

Added value: +{
+  "description": "The model identifier you are running as, for example \"claude-opus-4-8\" or \"gpt-5.2\". Pass \"unknown\" if you are not certain. Recorded for the server developer's analytics.",
+  "type": "string"
+}

addedInput schema / required

Added value: +[
+  "context",
+  "llm_model"
+]

Changed1 schema field changedSep 16, 2026

addedInput schema / properties / query

Added value: +{
+  "description": "Optional team-name search. Omit to list every non-archived team in the organization.",
+  "type": "string"
+}

First observedAug 19, 2026

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already declare a safe, read-only, non-destructive, closed-world operation, so the bar is lower, and the description adds real behavior beyond that: archived teams are excluded, teams outside the caller's memberships are still returned, and `role` is omitted when the caller is not a member. It does not say anything about result size, ordering, or pagination.

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

Conciseness3/5

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

The core sentences are front-loaded and useful, but the middle sentence about scopes (`scopeId`, `filterCel`, saved scopes) is not about teams at all and reads as misplaced boilerplate, adding ambiguity to a tool that lists teams. That passage weakens an otherwise tight description.

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 correctly carries the burden of describing returned fields (`id`, `visibility`, `role`), which it does. Missing are ordering, whether results are paginated or capped, and from where the stray scope/`filterCel` guidance comes — a notable gap for a list tool that could return many rows.

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 baseline is 3, but the description adds routing semantics the schema does not: `id` is what to pass as `teamId` to set_object_permissions, and it documents the `query` filter's effect of narrowing by team name. It does not cover `slug`, `context`, `llm_model`, or `conversation_id`, though those are self-describing analytics parameters in the schema.

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 verb and resource ("List all non-archived teams in the organization") plus a scope clarification that genuinely differentiates it — it includes teams the caller is not a member of, which an agent could otherwise assume is filtered out. This is distinct from siblings like list_users or list_organizations.

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?

Gives concrete usage triggers: asking about a team's costs, wanting to filter by team scope, or needing a teamId to share a dashboard, report, budget, or cost alert. It also names downstream consumers (set_object_permissions, query, report widgets), but never states when not to use it or which sibling to prefer for an overlapping need.

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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