Skip to main content
Glama

Server Details

Backend platform for AI agents: one agentstack.execute tool runs batched steps over +700 actions (hosting, storage, auth, payments, CRM, app builder).

Ownership verified
Status
Healthy
Uptime
91.1% over 21 days
Last Tested
Transport
Streamable HTTP · MCP 2025-11-25
URL

TDQS

B3.2/5.0

Scored across 1 tool

Disambiguation3/5

With only one tool there is no risk of an agent choosing the wrong tool, so selection-time ambiguity is zero. However, the single tool's description reveals a dense internal action space (agents.work_next, plan_claim, plan_execute, catalog/discovery.search, auth.get_profile) that is not exposed as separable tools, leaving the agent to disambiguate among actions purely from prose.

Naming Consistency3/5

A set of one cannot show inconsistency, but the single name 'agentstack.execute' is a generic verb that does not predict the operations hidden behind it (auth, context, planning, discovery). The dotted namespace prefix is readable, yet the verb is too broad for a control-plane gateway.

Tool Count2/5

The server describes sessions, auth flows, project context, plan lifecycle, discovery, and domain actions, but exposes all of it through a single mega-tool. This is a significant mismatch: one tool for a platform that clearly needs several distinct operations.

Completeness2/5

The multi-step spine (plan_get -> work_next -> plan_claim -> plan_execute) and auth/context setup are described but not surfaced as distinct tools, so lifecycle coverage is implicit and unverifiable from the tool surface. An agent cannot discover capabilities except by reading dense prose or trial-and-error.

Available Tools

1 tool
agentstack.executeAgentStack executeAInspect

AgentStack MCP = Work Graph control plane. After session + context.project_id: agents.work_next (mcp_work_loop_v1) -> plan_claim -> plan_execute (CAS/leases). Follow packet.next_action; catalog/discovery.search only if no packet. One-shot CRM/upload may call domain actions directly. Spine: agents.plan_get -> agents.work_next -> agents.plan_claim -> agents.plan_execute. Auth: Connect (G-A174) or Device Code, then auth.get_profile (bootstrap: include_settings false, include_subscriptions false; recipe mcp_session_setup), then context.project_id on every batch. Prompts: agentstack_session_setup, agentstack_execute_budget.

NameRequiredDescriptionDefault
stepsYesSteps; one heavy LLM per sync execute.
contextNosession.v1; project_id required.
optionsNobatch.v1 controls.

Output Schema

NameRequiredDescription
okNoFalse on total failure.
job_idNoPoll id.
agent_osNoFollow next_step.
recoveryNoNext action when blocked.
trace_idNoSupport trace.
error_codeNoStable failure code.
execution_modeNoasync when queued.
partial_successNoSome steps failed.

TDQS

A3.5/5.0
Behavior4/5

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

Annotations already declare readOnly=false, openWorld=true, idempotent=false, destructive=false; the description adds required auth via Connect/Device Code, project_id on every batch, CAS/leases, and async polling. It does not contradict annotations and adds meaningful operational constraints, though it omits some mutation impact details.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact but packed with internal jargon and repeated spine/agent sequences, and it does not front-load the tool's own purpose. Some auth/prompt details are useful context, but the structure is hard to parse.

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?

For a complex nested batch executor with an output schema and annotations, it supplies auth prerequisites, project_id scoping, work-loop routing, and async behavior. Missing a plain-language statement of the tool's core action keeps it from full completeness.

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 description coverage is 100%, so the schema already documents steps/context/options. The description adds only limited semantic guidance (project_id required, sync vs async, packet usage), so baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description frames the tool as the AgentStack work-graph control plane and names related planning/execution calls, but it never directly states that agentstack.execute runs a batch of catalog actions. The purpose is inferable from the tool name and schema, yet the description itself remains jargon-heavy and indirect.

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?

It prescribes an explicit sequence after session/project_id and says to follow packet.next_action, with discovery.search only when no packet and direct domain actions for one-shot CRM/upload. Alternatives are named, though the routing remains terse.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 1 tool update
    • Changedagentstack.execute6 fields changed
      • removedInput schema / properties / context / additionalProperties
        Removed value: -true
      • removedInput schema / properties / options / additionalProperties
        Removed value: -true
      • removedInput schema / properties / steps / items / properties / params / additionalProperties
        Removed value: -true
      • removedOutput schema / additionalProperties
        Removed value: -true
      • removedOutput schema / properties / agent_os / additionalProperties
        Removed value: -true
      • removedOutput schema / properties / recovery / additionalProperties
        Removed value: -true

Related MCP Connectors

Related MCP Servers

  • A
    license
    Not graded
    quality
    C
    maintenance
    Enables AI agents to create and run a backend with database, auth, file storage, server-side functions, and a site, all without opening a browser.
    12 npm
    MIT
  • A
    license
    Not graded
    quality
    B
    maintenance
    Enables AI agents to build and deploy an app from a GitHub repository to a managed live server and then operate it — logs, captured errors, health checks, managed Postgres, secrets, custom domains, email authentication, and Stripe checkout links. It also exposes safe, read/write-labelled tools so agents can rebuild, start/stop, and inspect projects without ever editing source code.
    MIT
Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources