Skip to main content
Glama
nirvana6

hermes-mcp-bridge

by nirvana6

hermes_ask

Delegate tasks to a local agent for actions an LLM cannot perform directly, such as scheduling jobs, web searches, sending emails, or editing documents.

Instructions

Delegate a task to Hermes Agent on this user's machine.

Use for things the calling LLM cannot do directly: scheduling cron jobs, browser-driven web search, sending email, creating/editing local documents, anything that should persist after this chat ends.

Args: prompt: Natural-language instruction for Hermes. session_id: Optional. Pass the same id across multiple calls in one chat to let Hermes remember prior steps (draft → refine → save). profile: Optional. Hermes profile to use (e.g. 'default', 'general_researcher'). Overrides the model in config.toml. Discover available profiles via the gateway's /v1/models endpoint.

Returns: Hermes's final answer text.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYes
profileNo
session_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are provided, so the description carries full burden. It discloses that tasks persist after the chat and that session_id enables memory across calls. However, it does not mention potential side effects, permissions, rate limits, or whether the operation is synchronous (the return statement suggests it waits for final answer).

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 well-structured with a title, usage paragraph, and argument list. It is front-loaded with the core purpose and includes necessary details without excessive verbosity. The inclusion of a method to discover profiles is useful but slightly extraneous.

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?

The tool has three parameters, one required, and an output schema. The description covers the return value ('Hermes's final answer text') and distinguishes from siblings. While it does not discuss error handling or timeouts, the overall clarity is adequate for an AI agent to use the tool effectively.

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

Parameters5/5

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

Schema description coverage is 0%, but the description thoroughly explains all three parameters. 'prompt' is described as a natural-language instruction, 'session_id' as optional for step memory, and 'profile' as optional with a method to discover available profiles. This adds substantial meaning beyond the schema's bare structure.

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 clearly states the tool's purpose: 'Delegate a task to Hermes Agent on this user's machine.' It lists specific examples of tasks (scheduling, web search, email, editing) that distinguish it from siblings (hermes_cancel, hermes_check, hermes_reset), which handle cancellation, status checking, and resetting respectively.

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 to use this tool 'for things the calling LLM cannot do directly' and provides a list of use cases. While it does not explicitly state when not to use it or name alternatives, the context of sibling tools and the emphasis on actions that persist after the chat ends gives clear guidance.

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/nirvana6/hermes-mcp-bridge'

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