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Monyeah777

NinFenz Content Gate

List pipelines

pipeline_list
Read-onlyIdempotent

List NF pipelines as path/title metadata from repository directories to discover the pipeline ID needed before reading full pipeline text.

Instructions

List NF pipelines as path/title metadata from 03_管线库 and community/*/pipelines. Use it to discover a pipeline ID before pipeline_read; do not use it to fetch pipeline text (use pipeline_read instead), module metadata (registry_search), or protocol packages (spec_list). The optional query is a case-sensitive substring of the repository-relative path or H1 title, not file content; wildcard characters are literal. Omit it to list every pipeline (examples: P04, 技术文档, 校园). Returns metadata only, never pipeline bodies, sorted by repository path; no matches yields an empty list, and each call rescans the repository. Local-only; no auth or rate limits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNoCase-sensitive substring matched against the relative path and H1 title; omit to list every pipeline.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
itemsYesPipelines matching query, sorted by repository path.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv2.11.3

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare read-only/idempotent/non-destructive, but the description adds substantive context beyond them: each call rescans the repository, results are sorted by repository path, no matches yields an empty list, and it is local-only with no auth or rate limits. It also reiterates that bodies are never returned, which is the key behavioral boundary.

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 lead sentence front-loads scope, the second routes usage, and the remainder packs only actionable detail with zero filler. Dense but every clause carries information an agent needs.

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?

With one optional parameter, full schema coverage, an output schema, and annotations, the description closes the remaining gaps (rescan cost, sort order, empty-result behavior, no auth). Nothing needed to invoke it correctly is missing.

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

Parameters4/5

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

Schema coverage is 100%, so baseline would be 3; the description earns extra by clarifying that the query matches the repository-relative path or H1 title and not file content, that wildcard characters are literal, and by giving concrete example values (P04, 技术文档, 校园). It stops short of 5 only because the core matching rule is already in the schema text.

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?

States a specific verb and resource (list NF pipelines), names the exact locations scanned (03_管线库 and community/*/pipelines), and clarifies it returns metadata only. An agent can distinguish it from pipeline_read, registry_search, and spec_list without opening the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly prescribes when to use it (discover a pipeline ID before pipeline_read) and enumerates three do-not-use cases with the correct alternative named for each. No inference required.

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