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Fund-z

FundzWatch MCP Server

get_market_brief

Generate an AI-powered strategic intelligence brief analyzing key market movements, patterns, and opportunities for informed decision-making.

Instructions

Get today's AI-generated strategic intelligence brief with narrative analysis of the most important market movements, patterns, and opportunities.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The get_market_brief tool handler, which fetches data from the /market/brief endpoint and formats it into a text result.
    case "get_market_brief": {
      const data = await apiRequest("GET", "/market/brief");
      const brief = data.brief;
      return textResult(
        `Strategic Intelligence Brief (${brief.date}):\n\n${brief.text}\n\n` +
        `Companies mentioned: ${(brief.companies || []).join(", ")}`
      );
    }
  • src/index.ts:120-126 (registration)
    Registration of the get_market_brief tool in the MCP server list.
    {
      name: "get_market_brief",
      description:
        "Get today's AI-generated strategic intelligence brief with narrative analysis " +
        "of the most important market movements, patterns, and opportunities.",
      inputSchema: { type: "object" as const, properties: {} },
    },
Behavior3/5

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

Adds valuable context that content is 'AI-generated' and scoped to 'today', which helps set expectations about source and temporality. However, with no annotations provided, the description carries full disclosure burden and omits critical behavioral details: caching behavior, idempotency (will the same brief be returned on repeated calls?), side effects, or rate limits.

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 well-structured sentence with zero waste. Front-loaded with action verb and immediately describes the distinct nature (AI-generated, narrative) and content scope (movements, patterns, opportunities). Every clause earns its place.

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

Completeness3/5

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

Adequate for a parameterless tool, but lacks description of return structure given no output schema exists. Mentions 'brief' and 'narrative analysis' but doesn't describe format, length, or specific data fields returned, which would help the agent consume the result correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Zero input parameters present. Per scoring rules, 0 params = baseline 4. No parameter documentation required or provided.

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?

Clear verb 'Get' and specific resource 'AI-generated strategic intelligence brief'. Describes content type well (narrative analysis of movements, patterns, opportunities). However, does not explicitly differentiate from sibling 'get_market_pulse' which likely also retrieves market data.

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?

Implies usage context through 'strategic intelligence' and 'narrative analysis' (vs raw data), suggesting when to use this over alternatives. However, lacks explicit when/when-not guidance or named alternatives for different use cases.

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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