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ariadne

ariadne

by whyy9527
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

query_chains

Find related GraphQL, HTTP, Kafka, and frontend operations across microservices by searching with a business term or endpoint name.

Instructions

Query cross-service chains by business term or endpoint name. Returns candidate clusters of related GraphQL operations, HTTP endpoints, Kafka topics, and frontend queries across all services indexed by the local Ariadne DB. Use this when you need to understand which APIs, topics, or frontend operations are involved in a business feature.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "hint": "<hint>"
    },
    "name": "query_chains"
  }
}

Input Schema

NameRequiredDescriptionDefault
hintYesBusiness term or endpoint name (e.g. 'createOrder', 'userProfile', 'subscription')
top_nNoNumber of clusters to return (default 3)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.0.3
  2. Removedv1.0.1
  3. First observedv1.0.0

TDQS

A4.1/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes what the tool does (queries and returns clusters) and the scope ('across all services indexed by the local Ariadne DB'), but lacks details on permissions, rate limits, error handling, or response format. For a query tool with no annotations, this is adequate but has gaps in behavioral context.

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 appropriately sized and front-loaded, with two sentences that efficiently convey purpose and usage guidelines without unnecessary details. Every sentence adds value, making it concise and well-structured for quick understanding.

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

Completeness3/5

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

Given the tool's complexity (querying cross-service chains) and lack of annotations and output schema, the description is moderately complete. It covers purpose and usage well but misses behavioral details like response format, pagination, or error cases. For a tool with no output schema, more information on return values would improve 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%, with both parameters well-documented in the schema. The description adds minimal value beyond the schema by mentioning 'business term or endpoint name' for 'hint' and implying clustering for results, but doesn't provide additional syntax or format details. Baseline 3 is appropriate as the schema handles most parameter documentation.

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 clearly states the tool's purpose with specific verbs ('query', 'returns') and resources ('cross-service chains', 'candidate clusters of related GraphQL operations, HTTP endpoints, Kafka topics, and frontend queries'). It distinguishes this tool from siblings by specifying it queries by 'business term or endpoint name' and returns clusters across services, unlike expand_node, rate_result, rescan, or show_help.

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?

The description explicitly states when to use this tool: 'Use this when you need to understand which APIs, topics, or frontend operations are involved in a business feature.' This provides clear context for usage versus alternatives, though it doesn't name specific sibling tools, the guidance is sufficient for distinguishing use cases.

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

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