Little Green Light MCP Server

submit_appeal_request_for_review

Submit an appeal request or status update to the Integration Queue for human review, with constituent matching via name and email.

Instructions

Submit an appeal request/status to LGL's Integration Queue for human review. Provide first_name/last_name/email to match the constituent this belongs to. This does NOT write to LGL directly.

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": {
      "appeal_name": "<appeal_name>"
    },
    "name": "submit_appeal_request_for_review"
  }
}

Input Schema

Name	Required	Description
`email`	No	Constituent's email, for matching
`last_name`	No	Constituent's last name, for matching
`first_name`	No	Constituent's first name, for matching
`appeal_name`	Yes	Required to create or associate with an appeal
`appeal_status`	No	Open, Called, Considering, Declined, Donated
`appeal_ask_amount`	No
`appeal_team_member`	No
`appeal_segment_code`	No
`appeal_segment_name`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.7.0Jul 29, 2026

TDQS

A3.9/5.0

Behavior4/5

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

The description explicitly states that the tool does NOT write to LGL directly, which is a key behavioral trait beyond annotations. annotations set readOnlyHint=false and destructiveHint=false, but the description adds context about queuing for human review. However, it does not detail whether the submission is idempotent or what happens on duplicate submissions.

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 two sentences long, front-loads the purpose, and has no redundant words. Every sentence adds value.

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?

Given the tool has 9 parameters and no output schema, the description lacks information about the integration queue process, expected response, error handling, and what happens after submission. It does not cover many aspects important for an AI agent to use the tool effectively.

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

Parameters2/5

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

The description only explains three parameters (first_name, last_name, email) for matching, but does not describe the other six appeal_* parameters (appeal_ask_amount, appeal_team_member, etc.). With schema coverage at 56%, the description should compensate for undocumented parameters but fails to do so.

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 tool name and description clearly indicate it submits appeal requests for human review, distinguishing it from sibling tools like submit_constituent_for_review. It specifies the target system (LGL Integration Queue) and the action (submit, not direct write).

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

Usage Guidelines4/5

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

The description advises providing first_name, last_name, and email for constituent matching, and clarifies that the tool does not write to LGL directly. This gives guidance on when to use it (for appeal review submissions) and what to provide, but does not explicitly mention when not to use it or compare to alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server