approve_agent_run
Approve an Algenta agent run waiting on manual approval.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| run_id | Yes |
Algenta MCP Server
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
C2.9/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries full burden. It only states the action without disclosing side effects, state changes, permissions needed, or what happens post-approval. This is insufficient for an action that likely triggers execution.
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 description is a single sentence, concise but overly minimal. It lacks details that could be added without becoming verbose, making it under-specified rather than efficiently 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 simple tool with one parameter and no output schema, the description is incomplete. It fails to explain how to obtain the run_id or what happens after approval, leaving gaps for the agent.
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?
Schema coverage is 0% and the description provides no explanation of the 'run_id' parameter. The agent is left to infer its meaning and format without any guidance.
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 clearly specifies the action 'Approve' and the resource 'Algenta agent run waiting on manual approval'. It is specific and distinguishes from sibling tools like cancel_agent_run or resume_agent_run.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use this tool ('waiting on manual approval') but does not explicitly state alternatives or when not to use it. No guidance on differentiation from similar tools like resume_agent_run.
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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TDQS
C2.7/5.0
Disambiguation4/5
Most tools target distinct resources or actions, but there is some overlap (e.g., run_repository_fix vs run_repository_pipeline vs simulate_repository) that could cause confusion. Overall, descriptions help differentiate.
Naming Consistency3/5
Tool names are primarily snake_case with a verb_noun pattern, but there are inconsistencies (e.g., single-word verbs like 'simulate', 'tokenize', and mixed prefixes like 'preview_', 'product_'). The pattern is readable but not uniform.
Tool Count1/5
With 140 tools, the server is extremely over-scoped for typical MCP usage. This overwhelms agents and suggests poor separation of concerns, likely violating the principle of minimal tool surfaces.
Completeness4/5
The tool set covers a wide range of functionalities including data onboarding, simulation, decisions, repository management, and admin operations. Minor gaps exist (e.g., no update_agent_run), but core workflows are well-supported.