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fasterv410

logflare-mcp

by fasterv410

Run a saved endpoint

query_endpoint

Execute a Logflare endpoint by providing its UUID or name and passing query parameters as key/value pairs.

Instructions

Execute a Logflare endpoint by UUID or name. Pass endpoint parameters as a flat key/value object.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
token_or_nameYesEndpoint UUID or name.
paramsNoQuery parameters forwarded to the endpoint.

Implementation Reference

  • The tool handler function that receives token_or_name and optional params, then delegates to client.queryEndpoint().
    async ({ token_or_name, params }) => {
    	try {
    		return text(await client.queryEndpoint(token_or_name, params ?? {}))
    	} catch (err) {
    		return errorText(err)
    	}
    },
  • Input schema for the query_endpoint tool: token_or_name (string) and optional params (record of string/number values).
    inputSchema: {
    	token_or_name: z.string().describe('Endpoint UUID or name.'),
    	params: z
    		.record(z.string(), z.union([z.string(), z.number()]))
    		.optional()
    		.describe('Query parameters forwarded to the endpoint.'),
    },
  • src/index.ts:133-154 (registration)
    Registration of the 'query_endpoint' tool with the MCP server, including its title, description, input schema, and handler.
    server.registerTool(
    	'query_endpoint',
    	{
    		title: 'Run a saved endpoint',
    		description:
    			'Execute a Logflare endpoint by UUID or name. Pass endpoint parameters as a flat key/value object.',
    		inputSchema: {
    			token_or_name: z.string().describe('Endpoint UUID or name.'),
    			params: z
    				.record(z.string(), z.union([z.string(), z.number()]))
    				.optional()
    				.describe('Query parameters forwarded to the endpoint.'),
    		},
    	},
    	async ({ token_or_name, params }) => {
    		try {
    			return text(await client.queryEndpoint(token_or_name, params ?? {}))
    		} catch (err) {
    			return errorText(err)
    		}
    	},
    )
  • The LogflareClient.queryEndpoint method that makes an HTTP GET request to /api/endpoints/query/{tokenOrName} with the provided query parameters.
    queryEndpoint(tokenOrName: string, params: Record<string, string | number | undefined>) {
    	return this.request<unknown>(
    		`/api/endpoints/query/${encodeURIComponent(tokenOrName)}`,
    		{ query: params },
    	)
    }
Behavior2/5

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

With no annotations, the description should disclose behavioral traits. It only mentions parameter format, but nothing about side effects, idempotency, error behavior, or required permissions. This is a significant gap for a mutation-like tool.

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?

A single sentence that is concise and front-loaded with the core action. No unnecessary words; every part earns its place.

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 lack of output schema and annotations, the description should explain what the tool returns or any notable behaviors. It only covers input parameters, leaving users unaware of the output format or potential errors.

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?

The schema already covers both parameters with descriptions (100% coverage). The description adds the nuance that params should be a 'flat key/value object', which reinforces schema constraints but does not add entirely new information.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Execute a Logflare endpoint') and the input methods (UUID or name), which is specific and matches the tool name. However, it does not differentiate from the sibling tool 'execute_query', which might have similar behavior.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives like 'execute_query' or 'list_endpoints'. The description only states what the tool does, without any context on appropriate usage scenarios.

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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