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HermitAgent

HermitAgent

by cafitac
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

run_task

Execute coding tasks with a local LLM by submitting a task and working directory, then poll for status. Handles questions during execution and flags results needing review.

Instructions

Run a coding task using a local LLM (default: qwen3-coder:30b). Immediately returns {status:'running', task_id}; poll with check_task(task_id). Return values: {status:"running", task_id} — task is running. {status:"waiting", task_id, question, options} — HermitAgent is asking a question. Reply with reply_task(task_id, message). {status:"done", result} — task completed. {status:"needs_review", result} — execution finished but Hermit found a likely issue.

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": {
      "task": "<task>",
      "cwd": "<cwd>"
    },
    "name": "run_task"
  }
}

Input Schema

NameRequiredDescriptionDefault
cwdYes
taskYes
modelNo
strategyNosingle
max_turnsNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv0.4.2
    • removedInput schema / properties / background
      Removed value: -{
-  "default": false,
-  "title": "Background",
-  "type": "boolean"
-}
    • addedInput schema / properties / strategy
      Added value: +{
+  "default": "single",
+  "title": "Strategy",
+  "type": "string"
+}
  2. First observedv0.3.11

TDQS

A3.9/5.0
Behavior4/5

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

With no annotations provided, the description carries the full behavioral burden. It discloses asynchronous execution, immediate return of a task_id, and four distinct status states including the interactive 'waiting' state and the 'needs_review' outcome. However, it omits error handling, timeout behavior, cancellation semantics, and whether the task modifies the filesystem, which are relevant for safe invocation.

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?

The description is front-loaded with the core action and async behavior, and the return-state list is compact and scannable. There is minor redundancy between the first sentence's 'Immediately returns {status:'running', task_id}' and the subsequent enumerated return values, but overall the structure is effective.

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?

The state machine and output values are well documented, which covers a major part of the tool's complexity. Still, with 5 parameters, 0% schema coverage, and no annotations, the absence of parameter semantics and failure/error handling leaves significant gaps for an agent attempting to invoke the tool correctly without relying on external documentation.

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%, and the description adds only minimal parameter meaning: 'task' is a coding task and the model defaults to qwen3-coder:30b. The required cwd parameter and the optional strategy and max_turns parameters are not explained, so the description does not compensate for the absent schema documentation.

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 opens with a specific verb and resource: 'Run a coding task using a local LLM.' It also names the default model and immediately distinguishes itself from sibling tools by directing the agent to poll with check_task(task_id) and reply via reply_task(task_id, message). This makes it unambiguous that run_task is the initiating tool in the workflow.

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 provides clear context for when to use this tool versus its siblings: it launches a task, then the agent should poll with check_task and reply with reply_task when a question is waiting. It names alternatives explicitly, though it does not explicitly say 'use check_task instead of run_task for status checks.' The workflow guidance is strong but not exhaustive.

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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