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pessini

Sales MCP Server

by pessini
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
LANGGRAPH_URLYesURL of the LangGraph server
OPENAI_API_KEYNoOpenAI API key (optional if using Ollama)
LANGGRAPH_ASSISTANT_IDYesAssistant ID for the skills agent

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": true
}
logging
{}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
extensions
{
  "io.modelcontextprotocol/ui": {}
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
ask_agentA

Ask the LangGraph skills agent a natural-language question and render its response as a Prefab dashboard card.

Call this tool whenever the user asks a question that matches any of the loaded skills below — even when the user does not explicitly say "use the skills agent". Do not answer from memory or invent data; route the question through this tool so the agent can query the live database, detect anomalies, and pause for human review when needed.

Loaded skills:

  • sales-analytics: Answer questions about monthly sales performance from the local sales database. Query revenue, deals, and gross margin by year/region/segment; detect significant revenue drops; escalate anomalies for human review. Keywords: sales, revenue, deals, margin, anomaly, region, segment, enterprise, mid-market, EMEA, North America, monthly performance.

Pass an optional thread_id to continue a prior conversation; omit for a fresh thread. The agent runs on LangGraph at LANGGRAPH_URL and returns either a final answer or a paused state with an Investigate / Dismiss review card.

resume_reviewB

Resume a paused thread with the human's decision.

Returns a Prefab component tree (NOT a PrefabApp) so the dashboard's Slot("decision_result") can inject it inline without a remount.

check_review_statusB

Return {reviewed, decision_result} for a thread.

Called from the dashboard's on_mount so the rendered card stays in sync with the latest LangGraph checkpoint after iframe remount.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription
Prefab Renderer (ask_agent)
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.9/5.0

Scored across 3 tools

Disambiguation5/5

Each tool has a clearly distinct purpose: ask_agent for querying the agent, resume_review for resuming paused threads, and check_review_status for querying thread state. No overlap.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern with underscores (ask_agent, resume_review, check_review_status), making them predictable and clear.

Tool Count5/5

Three tools is well-suited for a focused agent interface server. Each tool covers a core interaction step (ask, resume, check status) without unnecessary extras.

Completeness5/5

The tool set provides full lifecycle support for the sales analytics agent: asking questions, handling human reviews, and checking status. No obvious gaps for the intended use case.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources