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Little Green Light MCP Server

Little Green Light MCP Server

by WillHeadlee
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Hybrid

call_lgl_api

Destructive

Execute raw HTTP requests to the Little Green Light REST API for endpoints not covered by typed tools, enabling custom data operations.

Instructions

Raw passthrough to the LGL REST API for endpoints not covered by a typed tool. PREFER the typed tools whenever they cover your use case — they validate inputs, normalize field names, and return summarized payloads. Reach for this only when no typed tool fits; do not use it as a retry path when a typed tool fails.

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": {
      "method": "<method>",
      "path": "<path>"
    },
    "name": "call_lgl_api"
  }
}

Input Schema

NameRequiredDescriptionDefault
bodyNoOptional JSON payload body for POST/PATCH
pathYesEndpoint path (e.g. /constituents/1/memberships or /types/constituent_types)
methodYesHTTP Method

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.7.0

TDQS

A4.4/5.0
Behavior4/5

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

The description states 'raw passthrough,' implying no input validation or output normalization, which aligns with the annotations (readOnlyHint=false, destructiveHint=true). It adds context about the tool's limitations compared to typed tools, but does not explicitly list all behaviors. Annotations already cover some aspects.

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, front-loaded with the core purpose, and every sentence adds essential information without waste.

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

Completeness4/5

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

The description is complete for an open-ended passthrough tool: it explains the tool's purpose, when to use it, and what not to do. No output schema exists, but that is acceptable for this type of tool. Slight lack of info on authentication or error handling, but not critical.

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 coverage is 100%; the parameters are well-described in the schema. The description does not add additional semantics beyond what the schema provides, so baseline 3 is appropriate.

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 states it is a raw passthrough to the LGL REST API for endpoints not covered by typed tools. It uses a specific verb ('passthrough') and resource, and explicitly distinguishes from siblings by noting it is for uncovered endpoints.

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?

Explicit guidance is provided: 'PREFER the typed tools whenever they cover your use case' and 'Reach for this only when no typed tool fits; do not use it as a retry path when a typed tool fails.' This clearly tells when to use and when not to use.

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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