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originchain-ai

@originchain/telemetry-mcp

Official

service_graph

Expose service dependency edges and call counts: query a specific service for its callers/callees, or omit to retrieve the full dependency graph.

Instructions

Service dependency edges (who calls whom, call counts) from the maintained rollup. Pass a service for depends_on/called_by; omit for the whole graph.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
serviceNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior3/5

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

With no annotations, the description carries the burden of behavioral disclosure. It usefully notes that data comes from a 'maintained rollup' and that the output is edges with counts, which adds meaningful context. However, it does not mention read-only behavior, output format, potential size of the whole graph, or error/failure behavior.

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?

Two short sentences deliver both the core definition and usage instructions with no filler. The key distinction (filtered vs. whole graph) is front-loaded and memorable.

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 tool with one optional parameter and no output schema, the description covers the essential semantics: what edges mean, how counts are included, and how to scope the request. It could add details like return shape or what happens for an unknown service, but given the low complexity, the description is largely complete.

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 0%, so the description must explain the service parameter. It does so effectively: passing it filters to depends_on/called_by for that service, and omitting it returns the full graph. It does not specify the service identifier format, but for a single optional parameter this is strong compensation.

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

Purpose4/5

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

The description clearly identifies the tool's purpose: returning service dependency edges with call counts from a maintained rollup. It distinguishes this from sibling log/metrics tools by the resource type (service graph) and the data (edges/call counts), though it lacks a direct verb like 'get' or 'list'.

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 guidance on how to use the optional service parameter: pass a service for filtered depends_on/called_by edges, or omit it for the whole graph. This provides clear context, though it does not explicitly state when to choose this tool over its siblings or when not to use it.

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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