server-specs

Configuration instructions for connecting an AI agent to local or remote tool servers. Local servers communicate through a running program, while remote servers communicate over HTTP.

In plain words
What is it for?
Use it to configure which MCP servers—services that provide tools to an AI agent—are available to an agent, including their commands, URLs, and access tokens.
Why use it?
It provides a consistent way to describe server connections and catches unknown configuration fields. It also separates local development connections from shared or production services.

Agent

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add agents/promptise-com/foundry/server-specs
Clone the repo
git clone --depth 1 https://github.com/promptise-com/Foundry
Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,802 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00000 $0.01802
Opus 5 $0.00000 $0.00901
Sonnet 5 $0.00000 $0.00360
Haiku 4.5 $0.00000 $0.00180

Measured 2d ago against content hash dbd260842063, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

server-specs scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

docs/core/agents/server-specs.md · 222 lines

How it starts

The opening of the file, as written. The whole thing — 222 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Server Configuration

Configure how agents connect to MCP servers using StdioServerSpec for local servers and HTTPServerSpec for remote ones.

Quick Example

=== "Stdio"

```python
from promptise.config import StdioServerSpec

servers = {
    "math": StdioServerSpec(
        command="python",
        args=["-m", "math_server"],
    ),
}
```

=== "HTTP"

```python
from promptise.config import HTTPServerSpec

servers = {
    "api": HTTPServerSpec(
        url="http://localhost:8000/mcp",
        bearer_token="eyJhbGciOiJIUzI1NiIs...",
    ),
}
```

Concepts

Every Promptise agent receives a servers dict that maps a human-readable name to a server specification. The agent connects to each server at startup, discovers its tools, and makes them available to the LLM.

There are two spec types:

  • StdioServerSpec -- launches a local process and communicates over stdin/stdout. Best for local development and bundled servers.
  • HTTPServerSpec -- connects to a remote server over HTTP, Streamable HTTP, or SSE. Best for production deployments and shared services.

Both are Pydantic models with strict validation (extra="forbid"), so typos in field names are caught immediately.

StdioServerSpec

Use StdioServerSpec when the MCP server is a local process that the agent should launch and manage.

from promptise.config import StdioServerSpec

spec = StdioServerSpec(
    command="python",
    args=["-m", "mypackage.server", "--port", "0"],
    env={"API_KEY": "sk-..."},
    cwd="/path/to/project",
    keep_alive=True,
)

Fields

Field Type Default Description
command str required Executable to launch (e.g. "python", "node", "npx").
args list[str] [] Positional arguments for the process.
env dict[str, str] {} Environment variables set for the child process.
cwd str | None None Working directory for the process.
keep_alive bool True Whether the client should maintain a persistent session.

Read the full file on GitHub · 222 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 222 lines · 0 tokens per session scan A dbd260842063

Subscribe to this mod's changes

server-specs is an agent published in the GitHub repository promptise-com/Foundry (869 stars, last pushed 12d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 1,802 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other agents, from other repositories

lg-react-named-services-config

Agent "lg-react-named-services-config" from kdcube/kdcube, covering lg-react: connecting named services — two config shapes, shape a — per-service connections, shape b — whole surface, choosing and keeping the three declarations honest.

kdcube/kdcube · 0 tokens

agent-orchestration-context-manager

Elite AI context engineering specialist mastering dynamic context management, vector databases, knowledge graphs, and intelligent memory systems. Orchestrates context across multi-agent workflows, enterprise AI systems, and long-running projects with 2024/2025 best practices. Use PROACTIVELY for complex AI…

wshobson/agents · 66 tokens

backend-development-tdd-orchestrator

Master TDD orchestrator specializing in red-green-refactor discipline, multi-agent workflow coordination, and comprehensive test-driven development practices. Enforces TDD best practices across teams with AI-assisted testing and modern frameworks. Use PROACTIVELY for TDD implementation and governance.

wshobson/agents · 61 tokens

team-lead

Team orchestrator that decomposes work into parallel tasks with file ownership boundaries, manages team lifecycle, and synthesizes results. Use when coordinating multi-agent teams, decomposing complex tasks, or managing parallel workstreams.

wshobson/agents · 46 tokens

basic-agents

A basic agent uses a predefined strategy with a simple execution flow that works for most common use cases. It accepts a string input (a question, request, or task description) and sends this input to the configured LLM. The LLM may decide to call provided tools. The agent will execute the tools and send the results…

JetBrains/koog · 0 tokens

functional-agents

With functional agents, you implement the logic as a function that handles user input, interacts with LLMs, calls tools if necessary, and produces the final output. Compared to graph-based agents, this usually means faster prototyping with the following downsides.

JetBrains/koog · 0 tokens