SpecProof

Get Schema Definitions

get_schema_definitions

Read-onlyIdempotent

Look up formal, machine-checkable structure defined in the specifications: ASN.1 types (3GPP RRC/NAS message grammars) and JSON Schema / OpenAPI models (5G Service Based Interfaces).

Prefer this over prose search when the question is about a message's *structure* — field
names, types, ranges, optionality. The definition is returned verbatim from the spec, so
it is exact rather than recalled.

Args:
    name: Type or schema name, e.g. "RRCSetup", "MeasConfig", "SmContextCreateData"
    spec_number: Restrict to a spec, e.g. "38.331"
    kind: "asn1", "openapi" or "json_schema"
    limit: Maximum definitions to return (default: 10, max: 25)

Input Schema

TableJSON Schema

Name	Required	Description	Default
`kind`	No
`name`	No
`limit`	No
`spec_number`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.6/5.0

Behavior4/5

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

Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds that definitions are returned verbatim from the spec and are exact rather than recalled, which is valuable behavioral context beyond annotations. It also mentions the limit constraint.

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 and well-structured: a clear opening sentence, a usage recommendation, and a straightforward args list. Every sentence contributes meaning without unnecessary verbosity.

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?

Given the tool's moderate complexity (4 optional parameters) and the presence of an output schema, the description sufficiently covers what the tool does, how to use it, and what to expect (verbatim spec definitions). The usage guidance and parameter details make it complete.

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 input schema provides only titles and defaults with no descriptions (0% coverage). The description compensates fully by explaining each parameter with examples (e.g., 'RRCSetup', '38.331'), allowed values for 'kind', and default/max for 'limit'. This is essential and well done.

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 precise action ('Look up formal, machine-checkable structure') and names the exact resource types (ASN.1 types, JSON Schema/OpenAPI models). It also distinguishes itself from prose search by focusing on structural queries, making the tool's role clear.

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?

The description explicitly recommends using this tool over prose search when the question is about structure (field names, types, ranges, optionality). It provides clear context for when to use it, though it doesn't list specific alternative siblings or state 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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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.

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