Skip to main content
Glama
pminervini
by pminervini

research_with_context

Conduct targeted research by providing answers to clarifying questions, generating an enriched query that yields a comprehensive report with citations.

Instructions

Perform research using an enriched query based on clarification answers.

Use after:

  • Calling deep_research with request_clarification=True

  • Receiving clarifying questions and a session ID

  • Gathering answers from the user

What it does:

  • Takes your answers to clarifying questions

  • Creates an enriched, more specific research query

  • Performs comprehensive research with the enhanced query

Returns: Complete research report with citations and metadata

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
session_idYes
answersYes
system_instructionsNo
include_analysisNo
callback_urlNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations provided, so description bears full burden. It explains the process (answers to enriched query to research) and return type, but omits behavioral traits like whether the tool is synchronous/asynchronous, potential side effects, or state changes. The callback_url parameter hints at async but is not mentioned.

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?

Description is compact, uses headings and bullet points, and is front-loaded with the main purpose. Every sentence adds value, though it could be slightly more concise by merging lines. No superfluous content.

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?

Given the presence of an output schema, the return description is adequate. However, the tool is part of a multi-step workflow (deep_research → research_with_context → research_status), and the description does not explain that results may be polled via research_status. The callback_url parameter hints at async but is not contextualized.

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

Parameters2/5

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

Schema coverage is 0% from description. Description only indirectly explains 'session_id' and 'answers' via usage context, but does not describe 'system_instructions', 'include_analysis', or 'callback_url'. The two required params are partially clarified, but the three optional ones are ignored.

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?

Description clearly states 'Perform research using an enriched query based on clarification answers' and differentiates from siblings by associating with deep_research's clarification flow. The tool's role in the workflow is unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly specifies 'Use after: Calling deep_research with request_clarification=True... Receiving clarifying questions... Gathering answers from user.' This provides clear when-to-use and when-not-to-use guidance relative to sibling tools.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/pminervini/deep-research-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server