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SpecProof provides advanced tools for searching and retrieving specifications and documents, enabling users to perform semantic searches and access detailed document content. It is particularly useful for researchers, engineers, and product managers who need to analyze specifications and contributions in a structured manner.
Live probe refreshed Sep 14, 2026 · Endpoint host mcp.specproof.ai
Get Spec References
Get all specifications referenced by a given spec. Args: spec_number: Specification number (e.g., "23.501", "26.230") reference_type: Filter by type - "normative", "informative", "inline" (optional)
Get Spec Dependents
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Browse all toolsGet all specifications that reference a given spec (reverse lookup). Args: spec_number: Specification number to find dependents of max_results: Maximum results (default: 50)
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"
Get Spec Metadata
Get enriched metadata for specifications with filtering. Args: spec_number: Filter by spec number (partial match) series: Filter by series (e.g., "26" for 26.xxx codecs) working_group: Filter by working group (e.g., "SA4") doc_class: Filter by document class - "TS" or "TR" limit: Maximum results (default: 50)
Get Most Referenced Specs
Get the most referenced (most important) specifications ranked by incoming reference count. Args: doc_type: Filter by "3gpp" or "ietf" series: Filter by series number limit: Maximum results (default: 20)
Get Series Overview
Get an overview of all specification series with document counts, reference statistics, and working group assignments.
Search Specifications
Search across 3GPP, IETF, and Multimedia specification documents using full-text search. Args: query: Search query terms (required) doc_type: Filter by document type - "3gpp", "ietf", or "mpeg" (optional) spec_number: Filter by specification number (optional) max_results: Maximum number of results to return (default: 10, max: 50)
Semantic Search
Perform semantic search using vector embeddings with FAISS acceleration. Args: query: Natural language search query (required) max_results: Maximum number of results to return (default: 5, max: 20) similarity_threshold: Minimum similarity threshold 0.0-1.0 (default: 0.3) index_type: FAISS index type - "hnsw", "ivf", or "flat" (optional, auto-selected)
Get Document Content
Get specific content from a specification document. Args: filename: Document filename (required) page_range: Page range like "10-15" or single page "20" (optional) section: Section title filter (optional)
List Documents
List available specification documents. Args: doc_type: Filter by document type - "3gpp", "ietf", or "mpeg" (optional) search_pattern: Search pattern for spec number or title (optional) limit: Maximum number of results (default: 20, max: 100)
Get Requirements
List normative requirements (RFC 2119 / BCP 14 "shall/should/may" statements) extracted from specifications, each cited back to its source clause. Args: spec_number: Filter by spec number, e.g. "23.501" or "RFC 9110" (optional) obligation: Filter by strength - "must", "must_not", "should", "should_not", or "may" (optional) search: Keyword to search within the requirement text (optional) limit: Maximum number of requirements to return (default: 30, max: 10
Generate Conformance Tests
Generate a cited, runnable conformance test that verifies one normative requirement. The test is grounded in the requirement sentence plus its surrounding clause from the corpus, and every test cites the spec, section and page it enforces. If the requirement is not testable (boilerplate, scope text), the generator abstains rather than inventing a test. Args: requirement_id: The requirement's id, as shown by get_requirements target: "pytest" for a runnable Py
Diff Spec Versions
Diff two versions of a specification at the NORMATIVE level: which requirements were added, removed, or reworded. Not a textual diff — a clause that merely moved page or was recased counts as unchanged. Args: spec_base: Spec identity without the version, e.g. "38331" or "23501" from_version: Older version label, e.g. "j20" (default: the second-newest in the corpus) to_version: Newer version label, e.g. "j30" (default: the newest in the corpus) obligat
Get Schema Definitions
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", "SmCont
RFC Lineage
An RFC's revision history. RFCs are immutable — an RFC is never revised, it is obsoleted or updated by a *different* RFC — so this Obsoletes/Updates graph, not a version diff, IS its change history. Use it to find out whether an RFC you depend on has been superseded. Args: rfc: The RFC, e.g. "7231", "rfc7231" or "RFC 7231"
Build Cross-Spec Suite
Assemble the normative requirements for one procedure across a spec AND the specs it normatively references — a procedure like registration or handover is never described in a single document. Returns a cited suite plan; feed the requirement_ids to generate_tests. Args: spec_base: The anchor spec, e.g. "23502" or "38331" keyword: The procedure/topic, e.g. "registration", "handover", "authentication" include_references: Also pull matching clauses from specs th
Database Statistics
Get comprehensive database and system statistics.
SpecProof provides advanced tools for searching and retrieving specifications and documents, enabling users to perform semantic searches and access detailed document content. It is particularly useful for researchers, engineers, and product managers who need to analyze specifications and contributions in a structured manner. It provides MCP tools that your client discovers when it connects (this page may not list every tool name) that AI agents can use through the Model Context Protocol (MCP).
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