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This connector has been deprecated

This was a duplicate manual listing. Use Summer’s official listing, imported from the official MCP Registry. Summer’s MCP server remains active at https://api.gosummer.ai/mcp. Setup and documentation: https://gosummer.ai/docs/mcp-clients.

List companies the person can access and whether this connection includes each one

summer_account_companies_list
Read-onlyIdempotent

List companies the person can access and whether this connection includes each one.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "summer_account_companies_list"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.1/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, openWorldHint=true and destructiveHint=false, so the safety profile is fully covered by structured data. The description's one contribution is telling the agent what the payload conceptually contains (access flag plus connection-inclusion flag), but it says nothing about volume, pagination, or ordering. With annotations carrying the behavioral load, a 3 is appropriate.

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?

A single sentence with no filler, and the substantive scope (access plus connection inclusion) is front-loaded. It is however identical to the title, so the brevity comes at the cost of adding no new information.

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

Completeness3/5

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

For a zero-parameter lister with no output schema, the description conveys enough to know roughly what comes back: the accessible companies and a per-company connection flag. It leaves open how many companies are returned, the response shape, and what "this connection" refers to, which is the main ambiguity for an agent that must act on the result.

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?

The tool takes zero parameters, so the baseline is 4; the schema is a closed empty object and nothing in the description could add parameter meaning. No parameter-related gap exists for the description to fill.

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

Purpose3/5

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

The description is a verbatim restatement of the title, but that content is itself reasonably specific: verb "list", resource "companies", scope "the person can access", plus the added dimension of whether the current connection includes each one. It stops short of distinguishing this tool from the close sibling summer_account_connections_list, which an agent could easily confuse with it.

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

Usage Guidelines2/5

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

There is no statement of when to use this tool versus alternatives, and no mention of summer_account_connections_list, summer_account_me, or any other account sibling in the toolset. The agent must infer the selection context entirely from the name.

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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