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pipeshub-ai

PipesHub MCP Server

Official

pipeshub_sources

Read-onlyIdempotent

Fetch configured chat sources and AI models in one call. Get source IDs and model keys to use for PipesHub chat and search.

Instructions

Discover available chat sources and AI models in one call.

Returns up to three sections:

  • sources — every connector instance the org has wired up plus the synthetic knowledgeBase_<orgId> entry for the org's KB. Each item's id is exactly the value to put in pipeshub_chat's or pipeshub_search's apps filter.

  • llmModels — chat / generation models. Each item's modelKey is the value to pass on pipeshub_chat / pipeshub_search as modelKey. Pick isDefault: true unless the user asks for a specific model.

  • embeddingModels — vector embedding models (only fetched when explicitly requested via include).

Call this once at the start of a session and cache the result — sources and models change infrequently. sources and llmModels are returned by default; pass include to override.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
includeNoWhich sections to fetch. Default: `["sources", "llmModels"]`. Add `embeddingModels` if the user is configuring re-embedding.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.3.3

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already mark this read-only and idempotent, and the description goes further: it discloses the synthetic knowledgeBase_<orgId> entry, the default sections, the conditional fetching of embeddingModels, and the stable-but-changeable nature of the data. It also says exactly which returned field to pass to which sibling parameter.

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

Conciseness5/5

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

The description is front-loaded with the one-line purpose, uses bulleted sections and code formatting, and every sentence earns its place. The only slight length is justified by the lack of an output schema, so it needs to describe return structure.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only discovery tool with no output schema, it fully explains the three return sections, the special knowledgeBase source, the exact field-to-parameter mappings, the default selections, and when to request embedding models. Nothing essential is missing for an agent to call it correctly.

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%; the schema already documents include's enum, default value, and the re-embedding use case. The description reinforces that and adds an isDefault selection hint, but it adds no material parameter meaning beyond the schema, so the high-coverage baseline of 3 applies.

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: 'Discover available chat sources and AI models in one call.' It clearly enumerates the three returned sections and distinguishes this discovery tool from chat/search siblings by explaining that its ids feed pipeshub_chat and pipeshub_search.

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 gives explicit usage timing: 'Call this once at the start of a session and cache the result.' It also explains when include should be varied and how returned values are consumed by sibling tools. It does not explicitly name sibling alternatives to exclude, but the discovery-vs-action separation is clear.

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