Skip to main content
Glama
lindoai

mcp-lindoai

Official

Allocate Credits

allocate_credits

Allocate credits to a client by specifying client ID, credit type, amount, source, and notes.

Instructions

Allocate credits to a client.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
client_idYesClient ID
credit_typeYesCredit type
amountYesNumber of credits
sourceNoSource (e.g. bonus)
notesNoNotes

Implementation Reference

  • Registration of the 'allocate_credits' tool with title, description, input schema, and annotations.
    server.registerTool(
      "allocate_credits",
      {
        title: "Allocate Credits",
        description: "Allocate credits to a client.",
        inputSchema: {
        client_id: z.string().describe("Client ID"),
        credit_type: z.enum(["monthly", "purchased", "daily"]).describe("Credit type"),
        amount: z.number().describe("Number of credits"),
        source: z.string().optional().describe("Source (e.g. bonus)"),
        notes: z.string().optional().describe("Notes"),
      },
        annotations: { readOnlyHint: false, destructiveHint: false, openWorldHint: false },
      },
      async ({ client_id, credit_type, amount, source, notes }) => {
        const body = { client_id, credit_type, amount };
        if (source) body.source = source;
        if (notes) body.notes = notes;
        const data = await apiCall("/v1/ai/credits/client/allocate", "POST", body);
        const payload = data?.result || data;
        return { content: [{ type: "text", text: JSON.stringify(payload, null, 2) }] };
      }
    );
  • The async handler function for 'allocate_credits' that constructs the body and calls the API endpoint POST /v1/ai/credits/client/allocate.
    async ({ client_id, credit_type, amount, source, notes }) => {
      const body = { client_id, credit_type, amount };
      if (source) body.source = source;
      if (notes) body.notes = notes;
      const data = await apiCall("/v1/ai/credits/client/allocate", "POST", body);
      const payload = data?.result || data;
      return { content: [{ type: "text", text: JSON.stringify(payload, null, 2) }] };
    }
  • Input schema for 'allocate_credits': requires client_id (string), credit_type (enum: monthly/purchased/daily), amount (number), with optional source and notes fields.
      inputSchema: {
      client_id: z.string().describe("Client ID"),
      credit_type: z.enum(["monthly", "purchased", "daily"]).describe("Credit type"),
      amount: z.number().describe("Number of credits"),
      source: z.string().optional().describe("Source (e.g. bonus)"),
      notes: z.string().optional().describe("Notes"),
    },
  • The apiCall helper function used by the handler to make authenticated HTTP requests.
    async function apiCall(path, method, body) {
      const url = `${BASE_URL}${path}`;
      const res = await fetch(url, {
        method,
        headers: {
          Authorization: `Bearer ${API_KEY}`,
          "Content-Type": "application/json",
        },
        ...(body ? { body: JSON.stringify(body) } : {}),
      });
      return res.json();
    }
Behavior2/5

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

Annotations indicate a non-destructive write operation, but the description adds no additional behavioral traits (e.g., whether amounts can be negative, limits, or effects on existing credits). Minimal added value.

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?

Single sentence with no extraneous words. Perfectly concise and front-loaded.

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?

Given the simple parameter set (5 params, one enum) and no output schema, the description is nearly complete. Could mention constraints like positive amount, but not essential.

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%, so the description does not need to elaborate on parameters. It adds no new meaning beyond the schema, achieving the baseline.

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?

Description clearly states the verb 'allocate' and resource 'credits to a client', making it distinct from sibling tools like get_credits or get_client_credits. It is concise and avoids tautology.

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 on when to use this tool versus alternatives (e.g., when to allocate vs. check credits). Lacks explicit context or exclusions.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/lindoai/mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server