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agent_advisor

Guides analysis and interprets results for business data from Shopify, Stripe, WooCommerce, and other platforms. Ask questions to get interactive reports.

Instructions

Conversational AI that guides analysis and interprets results.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
messageYesYour question or request

Implementation Reference

  • The implementation of 'agent_advisor' (and all other tools) is dynamic, proxied through the 'remoteClient' in the 'tools/call' request handler. The tools are fetched from a remote API at startup.
    server.setRequestHandler(CallToolRequestSchema, async (request) => {
      try {
        const result = await remoteClient.callTool({
          name: request.params.name,
          arguments: request.params.arguments || {},
        });
        return result;
      } catch (err) {
        return {
          content: [{ type: "text", text: `Error: ${err.message}` }],
          isError: true,
        };
      }
    });
Behavior2/5

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

No annotations provided, so description carries full burden. While 'guides' and 'interprets' hint at behavior, missing critical details: conversation statefulness, whether it can execute actions or only advise, data access scope, and any side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Extremely concise at 6 words, single sentence. No redundancy or filler. However, given zero annotations and ambiguous positioning among concrete data tools, it may be underspecified rather than optimally concise.

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?

For a conversational advisor tool among concrete data tools, the description fails to clarify scope (what analysis? what results? which tool's outputs?). No output schema exists to compensate. Needs explicit statement of advisory role and relationship to sibling data tools.

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 has 100% coverage with 'message' parameter fully described as 'Your question or request'. Description adds no parameter-specific context, but baseline is 3 when schema coverage exceeds 80%.

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 states specific actions (guides analysis, interprets results) and identifies the resource (conversational AI). It implicitly distinguishes from siblings like datasets_read or reports_view by emphasizing the advisory/conversational nature rather than data retrieval.

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 the concrete data tools (datasets_*, reports_*, connectors_*). Missing crucial context about whether to use this for help interpreting other tools' outputs or for general assistance.

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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