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Agent.ai MCP Server

Agent.ai MCP Server

by OnStartups
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JavaScript
Remote

company_research_v2_get_status

Check research report progress by polling its status until completion.

Instructions

Poll for research report progress. Use in a loop until is_complete is true.

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": {
      "report_id": "<report_id>",
      "output_variable_name": "<output_variable_name>"
    },
    "name": "company_research_v2_get_status"
  }
}

Input Schema

NameRequiredDescriptionDefault
report_idYesUUID of the report to check. Usually {{research_result.report_id}}.{{research_result.report_id}}
output_variable_nameYesVariable name for the status result. Check {{report_status.is_complete}}.report_status

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior2/5

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

No annotations are provided, so description must carry full burden. It only says 'poll' (implying non-destructive read) and mentions checking 'is_complete', but does not disclose behavior on invalid report_id, rate limits, or potential errors.

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 sentences: first states purpose, second gives usage. No unnecessary words, well front-loaded.

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?

No output schema exists. Description hints at is_complete boolean in response, but does not mention other potential fields like progress percentage or error states. Adequate but not complete.

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?

Schema descriptions cover both parameters with 100% coverage. Description adds context about using in a loop and checking is_complete, but does not add significant meaning beyond schema.

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 'Poll for research report progress' clearly states the verb (poll) and resource (research report progress). It distinguishes from siblings like start_research (initiates) and get_report (retrieves final report).

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 in a loop until is_complete is true', providing clear usage pattern. It implies polling context but does not mention alternatives or when not to use.

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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