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Glama

fanar_deep_research

Start a deep research job on an Islamic topic to generate a structured report with headings and cited sources, then poll the result.

Instructions

Start a Fanar-Sadiq-2 deep research job: a structured report on an Islamic topic, with headings and cited sources. It takes minutes (quick about 3-6, standard about 7-10); poll it with fanar_deep_research_result. Requires Fanar-Sadiq-2 on the key. About 20 jobs a day.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
depthNoquick
topicYes
web_searchNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does so well: it discloses async/latency behavior (quick 3-6, standard 7-10), a key prerequisite ('Requires Fanar-Sadiq-2 on the key'), and a rate limit ('About 20 jobs a day'). It does not cover failure/timeout handling or cost, keeping it out of 5 territory.

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?

Front-loaded with the purpose, then timing, polling target, prerequisite, and quota, each in its own clause with no filler. The parenthetical latency figures are the densest part but they carry real information, so it stays efficient.

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 an async job tool with no output schema, the description adequately covers the return shape (structured report with headings and cited sources) and the polling workflow. The main shortfall is the undocumented parameters (topic, web_search, comprehensive depth), which the schema also fails to explain.

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 description coverage is 0%, so the description must compensate, and it only partially does. It explains the depth enum indirectly by giving latency for 'quick' and 'standard' (leaving 'comprehensive' unexplained) but says nothing about the 'topic' or 'web_search' parameters, leaving two of three undocumented.

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 and resource: 'Start a Fanar-Sadiq-2 deep research job' producing 'a structured report on an Islamic topic, with headings and cited sources.' It also names the sibling it hands off to (fanar_deep_research_result), so an agent can distinguish it from fanar_islamic_qa and fanar_chat without opening a schema.

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

Usage Guidelines4/5

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

Gives clear operating context: it is an async job taking minutes, and the agent should 'poll it with fanar_deep_research_result.' This tells the agent both when to reach for it and how to follow up. It stops short of explicit when-not guidance (e.g. preferring fanar_islamic_qa for quick single questions), so it is not a 5.

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