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abahocodes

@llmgraph/mcp-server

by abahocodes

production

Trigger an LLMGraph workflow deployment by passing a JSON object as input to execute the workflow and return the output.

Instructions

Invoke the LLMGraph workflow deployment "production"

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputYesJSON object sent as the workflow input, passed through as the request body
Install Server

TDQS

B3.4/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of disclosing behavioral traits. It only states that the deployment is invoked, without explaining side effects, return behavior, asynchronous execution, idempotency, or required permissions. This is a significant transparency gap for an action that likely triggers a workflow.

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 a single, front-loaded sentence with no filler or redundant wording. Every word contributes to identifying the action and resource, making it highly effective for an agent scanning tool definitions.

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 with no output schema and no annotations, the description omits important contextual details such as what the workflow returns, whether invocation is synchronous, and what side effects may occur. The input is well-covered, but the overall invocation experience is under-specified.

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

Parameters3/5

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

Schema description coverage is 100%, and the input schema clearly explains that 'input' is a JSON object passed through as the request body. The description itself adds no parameter detail, but the schema already carries the semantic weight, so the baseline score of 3 applies.

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 uses a specific verb ('Invoke') and identifies a precise resource ('the LLMGraph workflow deployment "production"'), so an agent can tell exactly what the tool does. There are no sibling tools to distinguish against, and the resource is unambiguous.

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 the tool should be used when the agent needs to trigger the production deployment, but it does not explicitly state when to use it, when not to use it, or any prerequisites. With no siblings, the lack of alternative routing is acceptable, but the guidance remains only implied.

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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