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perplexity-deep-mcp

perplexity-deep-mcp

by Aakashanil67
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

pplx_deep_research_list

Read-onlyIdempotent

List recent deep research jobs to recover a lost job ID or monitor running tasks. Displays job IDs, models, and statuses.

Instructions

List recent deep research jobs with their ids, models and statuses. Use this to recover a job_id you lost, or to see what is still running.

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": "pplx_deep_research_list"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoMax jobs to show, newest first. Default 20.
statusNoOptionally show only jobs with this status.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.1/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds that it lists 'recent' jobs, implying a temporal ordering, and that it returns ids, models, and statuses, but it does not disclose additional behavioral traits such as pagination or error conditions. This is consistent with the annotations.

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?

The description is two sentences: the first defines the action and output, the second gives practical use cases. It is front-loaded with the verb and resource, with zero filler words.

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

Completeness5/5

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

The tool is simple with two optional parameters, and the schema plus annotations already provide the necessary context. The description explicitly lists the returned fields (ids, models, statuses), which compensates for the lack of an output schema, and the use cases make the tool's purpose clear. This is complete for a list-style tool.

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

Parameters3/5

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

Input schema coverage is 100%, with descriptions for both 'limit' and 'status', so the baseline is 3. The description does not add any extra explanation of the parameters beyond the schema; it only references the returned 'ids' which are not a parameter. Therefore no additional credit is warranted.

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?

The description uses a specific verb ('List') and identifies the resource ('deep research jobs') plus the returned attributes ('ids, models and statuses'). It also distinguishes itself from siblings by explaining the use case of recovering a lost job_id, which differs from starting or checking specific jobs.

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?

The description explicitly says 'Use this to recover a job_id you lost, or to see what is still running,' providing clear scenarios for when to invoke this tool. It does not name the sibling alternatives or state when not to use it, so it falls short of full exclusion guidance.

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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