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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.

Read recent scheduled runs with original definition revisions, outcomes, coverage, saved source counts and failures

summer_scheduled_jobs_runs
Read-onlyIdempotent

Read recent scheduled runs with original definition revisions, outcomes, coverage, saved source counts and failures.

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": {
      "company": "<company>",
      "environment": "<environment>",
      "job": "<job>"
    },
    "name": "summer_scheduled_jobs_runs"
  }
}

Input Schema

NameRequiredDescriptionDefault
jobYes
companyYes
environmentYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

C2.7/5.0
Behavior3/5

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

Annotations already declare the safety profile (readOnly, idempotent, non-destructive, openWorld), so the description is not responsible for that. The description adds useful content about returned run details, but it does not state the time window for 'recent,' pagination behavior, or authorization requirements.

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 a single front-loaded sentence with no tangential or repetitive text. Its brevity comes at the cost of completeness, but it is structurally concise.

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

Completeness2/5

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

For a tool requiring three identifiers, the description never explains them, nor does it define how many runs 'recent' covers or how results are ordered or paginated. It lists output categories but is insufficient for an agent to invoke the tool without opening the schema and guessing parameter roles.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

All three required parameters (company, environment, job) have zero schema description coverage, and the description does not mention any of them. It provides no meaning for the UUID identifiers or the production/staging environment enum beyond what the raw schema shows.

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 states a specific verb and resource: 'Read recent scheduled runs' and enumerates the included data categories (definition revisions, outcomes, coverage, saved source counts, failures). It is more specific than a tautology, but it does not distinguish this tool from sibling tools such as summer_scheduled_jobs_history or summer_scheduled_jobs_list.

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?

The description provides no guidance on when to use this tool versus alternatives, no exclusions, and no prerequisites. It merely describes what the tool returns, leaving the agent to infer selection from similarly named siblings.

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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