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Multi-Agent Deep Researcher MCP

Multi-Agent Deep Researcher MCP

by yashavsarmal30
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Hybrid

list_research_reports

List all saved research reports with filenames, queries, and creation dates to quickly retrieve archived findings from the reports directory.

Instructions

List all previously generated research reports saved in the reports directory.

Returns:
    JSON list of available reports with filename, query, and creation date.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_research_reports"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the burden and it does disclose the action (list) and outcome shape (JSON with filename, query, creation date). It does not mention ordering, empty-directory behavior, or error conditions, leaving minor ambiguity.

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?

Two front-loaded sentences; the purpose appears in the first sentence and the return format is cleanly separated. No filler or repeated schema information.

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?

For a parameterless listing tool, the description is nearly complete: it names the source, scope, and return fields. With no output schema, explaining the return fields is essential and done well; it only lacks an explicit pointer to sibling tools.

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?

The tool has zero parameters, so parameter semantics are trivially complete; per calibration, baseline 4. The description reinforces that no arguments are needed by focusing entirely on the output.

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?

States a specific verb ('List'), resource ('previously generated research reports'), and location ('reports directory'). Differentiates from siblings by explicitly scoping to saved reports rather than generating or reading one.

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?

Usage context is implied through 'previously generated' — it is the inventory step before read_research_report and separate from deep_research/quick_search. However, it does not explicitly state when to use it or name alternatives, so the agent must infer placement.

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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