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Get SOP schema

get_sop_schema

Fetch the SOP domain's generated JSON Schema as raw text to discover constraints, answer structural questions, or resolve validation errors without diffing the full document.

Instructions

Return the SOP domain's generated JSON Schema (2020-12 dialect) as raw text -- the machine-readable, parsed-JSON view of the document structure, not the markdown view (call get_sop_template for the authoring starting point). Fetch it for constraint discovery, structural questions, or an unresolved validation error -- never as a mandatory pre-authoring fetch. Generated by specmgr schema and kept current by a pre-commit hook/CI step; includes a $comment schema-layout version marker for detecting a shape change without diffing the whole document.

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.37.0

TDQS

A4.6/5.0
Behavior4/5

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

With no annotations, the description carries the burden and does disclose meaningful traits: provenance (generated by `specmgr schema`), freshness guarantees (kept current by pre-commit hook/CI), the presence of a `$comment` layout-version marker, and that the payload is raw text rather than parsed/markdown. It stops short of stating the read-only/no-side-effect profile explicitly, but the omission is minor for a schema getter.

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

Conciseness4/5

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

Front-loaded with the verb and return type, then usage routing, then provenance — a sensible ordering. Slightly dense, with 'raw text' and 'machine-readable, parsed-JSON view ... not the markdown view' partially restating the same point, which keeps it from a perfect score.

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?

An output schema exists, so return values need not be described; the description adds the things the schema cannot express — provenance, freshness, version marker, and the tempting-but-wrong pre-authoring use case. Nothing an agent needs in order to call this 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?

Zero parameters, so there is no parameter semantics to explain; baseline is 4. The description correctly spends no words on parameters and instead invests in output-format distinctions.

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?

Specific verb (Return) plus a precisely scoped resource (the SOP domain's generated JSON Schema, 2020-12 dialect, raw text). It explicitly contrasts itself with the markdown view and names get_sop_template as the sibling for the authoring starting point, so an agent can route correctly without opening either tool.

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?

Gives concrete when-to-use triggers (constraint discovery, structural questions, an unresolved validation error) and an explicit when-not ('never as a mandatory pre-authoring fetch') with the alternative that should be used instead (get_sop_template). Nothing about selection is left to inference.

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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