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Algenta MCP Server

by io.github.thyn-ai
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approve_agent_run

Approve an agent run that is waiting on manual approval (status requires_approval) and execute it synchronously to completion. Runs in any other state fail with agent_run_invalid_state; an unknown run_id fails with agent_run_not_found. The approval is the human-in-the-loop gate for manual-mode runs and is audit-logged and checkpointed. Returns the updated run resource. Use resume_agent_run for paused runs instead.

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": {
      "run_id": "<run_id>"
    },
    "name": "approve_agent_run"
  }
}

Input Schema

NameRequiredDescriptionDefault
run_idYesWaiting run id from create_agent_run or list_agent_runs.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / run_id / description
      Added value: +"Waiting run id from create_agent_run or list_agent_runs."
  2. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Annotations are sparse (all hints false), so the description carries the behavioral burden. It discloses that the tool executes synchronously to completion, is audit-logged and checkpointed, returns the updated run resource, and specifies failure modes for invalid state and unknown run_id. This is rich, transparent context beyond the annotations.

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?

The description is compact and every sentence carries essential information: the action and precondition, error cases, behavioral side effects (audit, checkpoint), return value, and sibling routing. Zero redundancy.

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 single-parameter mutation tool with no output schema, the description covers everything an agent needs: preconditions, execution semantics, error behavior, return value, and the alternative tool. Nothing is missing.

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% and the schema already documents run_id as a waiting run id from create_agent_run or list_agent_runs. The description adds meaning by specifying the valid state (requires_approval) and the error behavior for invalid states (agent_run_invalid_state), helping the agent select the correct run_id.

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 ('Approve'), a specific resource ('agent run'), and the exact precondition (status requires_approval). It also clearly distinguishes itself from the sibling resume_agent_run, so an agent can tell them apart immediately.

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?

It explicitly states when to use the tool: for agent runs waiting on manual approval. It also names the alternative (resume_agent_run) and the condition that selects it ('for paused runs instead'). This gives clear 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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