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atriumn
by atriumn

compare_models

Filter models by provider, min context window, or mode. Get the top 5 most cost-effective matches for your needs.

Instructions

Filter and compare models by provider, minimum context window, or mode. Returns top 5 most cost-effective matches.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
providerNoFilter by provider (e.g. 'anthropic', 'openai', 'google', 'amazon')
min_contextNoMinimum context window size in tokens
modeNoFilter by mode (e.g. 'chat', 'embedding', 'completion', 'image_generation')

Implementation Reference

  • The core handler for the compare_models tool. Parses input (provider, min_context, mode), filters models from the pricing data, sorts by input cost, and returns the top 5 most cost-effective matches with details.
    case "compare_models": {
      const { provider, min_context, mode } = compareModelsSchema.parse(args);
      const models = await getModels();
    
      let filtered = Object.values(models);
    
      if (provider) {
        const lowerProvider = provider.toLowerCase();
        filtered = filtered.filter(
          (m) =>
            m.litellm_provider.toLowerCase().includes(lowerProvider) ||
            m.key.toLowerCase().includes(lowerProvider),
        );
      }
    
      if (min_context !== undefined) {
        filtered = filtered.filter(
          (m) => m.max_input_tokens !== null && m.max_input_tokens >= min_context,
        );
      }
    
      if (mode) {
        const lowerMode = mode.toLowerCase();
        filtered = filtered.filter((m) => m.mode.toLowerCase() === lowerMode);
      }
    
      if (filtered.length === 0) {
        return {
          content: [
            {
              type: "text",
              text: "No models match the given criteria. Try broadening your filters.",
            },
          ],
        };
      }
    
      // Sort by input cost (most cost-effective first)
      filtered.sort((a, b) => a.input_cost_per_token - b.input_cost_per_token);
      const top = filtered.slice(0, 5);
    
      const header = `Top ${top.length} most cost-effective models${provider ? ` (provider: ${provider})` : ""}${min_context ? ` (min context: ${formatTokenCount(min_context)})` : ""}${mode ? ` (mode: ${mode})` : ""}:\n`;
    
      const rows = top.map(
        (m, i) =>
          `${i + 1}. ${m.key}\n` +
          `   Provider: ${m.litellm_provider} | Mode: ${m.mode}\n` +
          `   Input: ${formatCost(m.input_cost_per_million)}/1M | Output: ${formatCost(m.output_cost_per_million)}/1M\n` +
          `   Context: ${formatTokenCount(m.max_input_tokens)} in / ${formatTokenCount(m.max_output_tokens)} out` +
          (m.cache_read_input_token_cost_per_million != null
            ? `\n   Prompt caching: ${formatCost(m.cache_read_input_token_cost_per_million)}/1M cached input`
            : ""),
      );
    
      const total = `\n(${filtered.length} models matched total)`;
    
      return {
        content: [{ type: "text", text: header + rows.join("\n\n") + total }],
      };
    }
  • Zod validation schema for the compare_models tool's input parameters. All fields (provider, min_context, mode) are optional.
    const compareModelsSchema = z.object({
      provider: z.string().optional(),
      min_context: z.number().optional(),
      mode: z.string().optional(),
    });
  • src/tools.ts:50-83 (registration)
    Registration of the compare_models tool in the tools array, defining its name, description, and JSON Schema input schema.
    {
      name: "compare_models",
      description:
        "Filter and compare models by provider, minimum context window, or mode. Returns top 5 most cost-effective matches.",
      inputSchema: {
        type: "object" as const,
        properties: {
          provider: {
            type: "string",
            description: "Filter by provider (e.g. 'anthropic', 'openai', 'google', 'amazon')",
          },
          min_context: {
            type: "number",
            description: "Minimum context window size in tokens",
          },
          mode: {
            type: "string",
            description:
              "Filter by mode (e.g. 'chat', 'embedding', 'completion', 'image_generation')",
          },
        },
        required: [],
      },
    },
    {
      name: "refresh_prices",
      description:
        "Force a re-fetch of pricing data from the LiteLLM registry. Use this if you suspect the cached data is stale.",
      inputSchema: {
        type: "object" as const,
        properties: {},
        required: [],
      },
    },
  • src/index.ts:21-23 (registration)
    MCP server registration: the ListTools request returns the tools array (including compare_models), and the CallTool request dispatches to executeTool, which handles compare_models via its switch-case.
    server.setRequestHandler(ListToolsRequestSchema, async () => {
      return { tools };
    });
  • Helper functions used by the compare_models handler: formatCost formats dollar amounts with appropriate precision, and formatTokenCount formats token counts with K/M suffixes.
    function formatCost(amount: number): string {
      if (amount < 0.0001) return `$${amount.toFixed(8)}`;
      if (amount < 0.01) return `$${amount.toFixed(6)}`;
      if (amount < 1) return `$${amount.toFixed(4)}`;
      return `$${amount.toFixed(2)}`;
    }
    
    function formatTokenCount(n: number | null): string {
      if (n === null) return "unknown";
      if (n >= 1_000_000) return `${(n / 1_000_000).toFixed(1)}M`;
      if (n >= 1_000) return `${(n / 1_000).toFixed(0)}K`;
      return n.toString();
    }
Behavior3/5

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

No annotations provided, so description carries full burden. It discloses read behavior and returns top 5 matches, but does not mention side effects, authentication needs, or behavior when no matches found.

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 sentence, 20 words, with no waste. It front-loads the purpose and is appropriately sized for the tool's simplicity.

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 tool's simplicity (3 optional params, no output schema), the description covers filtering and output summary. Minor gap: does not clarify if filters combine or define 'cost-effective'. Still adequate for selection.

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%, so baseline is 3. The description repeats parameter names but does not add meaningful usage context beyond what the schema already provides.

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 the tool filters and compares models by provider, min context, or mode, returning top 5 cost-effective matches. It uses specific verbs and resources, distinguishing it from siblings like get_model_details.

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 usage for comparing models but lacks explicit guidance on when to use this tool versus alternatives (e.g., get_model_details for single model info). No exclusions or prerequisites are mentioned.

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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