SpecProof

get_relationship_graph

Get a relationship graph centered on a specification, expanding N hops.

    Returns JSON with nodes and edges suitable for graph visualization.
    Each node includes title, series, working group, and reference counts.
    Each edge includes source, target, reference type, and count.

    Args:
        spec_number: Center specification number
        depth: Number of hops to expand (1-3, default: 1)
        reference_type: Filter by type - "normative", "informative" (optional)

Input Schema

TableJSON Schema

Name	Required	Description	Default
`depth`	No
`spec_number`	Yes
`reference_type`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.3/5.0

Behavior4/5

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

With no annotations, the description carries the transparency burden. It explains the exact returned graph structure (nodes with title, series, working group, reference counts; edges with source, target, reference type, count) and parameter constraints (depth 1-3, optional reference_type). It does not mention error cases or rate limits, but the read-only nature is evident from 'Get' and 'Returns JSON'.

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 concise (around 120 words), front-loaded with a clear one-sentence purpose, then structured return details and Args. Each line adds useful information without repetition or filler.

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?

The tool has 3 parameters and an output schema, and the description covers parameter semantics and return fields. However, it does not provide usage context relative to sibling tools or mention potential edge cases (e.g., missing spec_number, depth limit behavior). This is a minor gap, so not a perfect 5.

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

Parameters5/5

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

The JSON schema only provides types and defaults, so schema description coverage is 0%. The description's Args section adds full semantics: spec_number as center, depth as number of hops with valid range/default, reference_type as filter with allowed values. This fully compensates for the schema gap.

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: 'Get a relationship graph centered on a specification, expanding N hops.' It clarifies the graph traversal nature and distinguishes it from sibling tools like get_spec_references or get_spec_dependents by highlighting multi-hop expansion and node/edge output.

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

Usage Guidelines3/5

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

There is no explicit comparison to alternatives or exclusions. The phrase 'expanding N hops' implies use for relationship graph exploration rather than direct reference lookup, but the description does not state when to prefer this tool over get_spec_references or get_rfc_lineage. This is implied usage, not explicit guidance.

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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TDQS

A3.8/5.0

Disambiguation4/5

Tools are largely distinct but there are clusters of similar functionality, such as multiple search mechanisms (search_specifications, semantic_search, get_requirements with a search parameter) and reference-graph tools (get_spec_references, get_spec_dependents, get_relationship_graph). Descriptions do differentiate them, but an agent could still be uncertain which to use for a given query.

Naming Consistency4/5

Most tool names follow a snake_case verb_noun pattern (e.g., list_documents, search_specifications, get_requirements). However, semantic_search is adjective_noun rather than verb_noun, and get_database_stats is a generic outlier among the spec-focused tools. Overall the pattern is consistent with minor deviations.

Tool Count4/5

With 17 tools, the set is slightly above the ideal 3-15 range but not excessive for the breadth of functionality: search, metadata, requirements, test generation, relationships, and schemas. Each tool has a defined role, and the count feels warranted for the domain.

Completeness4/5

The tool set covers the core workflow well: discovering specs (search_specifications, list_documents), retrieving content (get_document_content, get_requirements), analyzing relationships (get_spec_references, get_spec_dependents), and generating tests (build_cross_spec_suite, generate_tests). Minor gaps exist, such as the lack of a direct tool to fetch a requirement's full surrounding clause (workaround via get_document_content), but no critical dead ends.

Resources

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