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Multi-Agent Deep Researcher MCP

Multi-Agent Deep Researcher MCP

by yashavsarmal30
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Hybrid

read_research_report

Retrieve a saved research report by filename to access its full text for review, citation, or further analysis.

Instructions

Retrieve and read a saved research report from disk.

Args:
    filename: The filename of the report (e.g., '20260306_quantum_computing.md').

Returns:
    The full text of the research report.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "filename": "<filename>"
    },
    "name": "read_research_report"
  }
}

Input Schema

NameRequiredDescriptionDefault
filenameYes

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?

With no annotations, the description carries the burden. It discloses the core behavior (read from disk) and return value (full text), but does not explicitly state that it is read-only with no side effects, nor describe error behavior for missing files.

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?

Three short, front-loaded sections (summary, Args, Returns) with no redundant content. The example filename earns its place by illustrating the expected format.

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 one parameter and no output schema, the description adequately explains input and return. The only notable gap is the missing connection to list_research_reports for valid filenames, which is a usage-guidance issue rather than a completeness issue.

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?

Schema coverage is 0%, so the description compensates by providing an Args section that explains the purpose of 'filename' and includes a concrete date-format example. It could add where the filename comes from, but the meaning is clear.

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 ('Retrieve and read'), a resource ('saved research report'), and the storage location ('from disk'), making it clearly distinct from siblings like list_research_reports (which lists) and deep_research (which creates).

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?

Clear context – it is for saved reports – but does not explicitly say when not to use it or point to list_research_reports for discovering available filenames. The alternatives are inferable from sibling names but not stated.

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

Deploy Server

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