Infranodus MCP Server Infranodus vs Agenda Intelligence Md

generate_knowledge_graph

Generate a knowledge graph with main topics, topical clusters, concepts, concepts (nodes) relations (edges) and structural gaps. Only use when explicitly asked to analyze a text or generate a knowledge graph. Do not use for short clarifying questions that you already have an answer to from the context of the conversation.

create_knowledge_graph

Create a knowledge graph in InfraNodus from text or from a URL, save it, and provide its name and a link to it for future use.

generate_ontology_graph

Use AI to generate a reasoning ontology knowledge graph (entities and the relations between them) and optionally save it as a InfraNodus graph. Three sources, provide exactly one: prompt (a topic — one AI call), text (a long document or a structural digest of a project, chunked server-side), or sourceGraphName (an existing graph — e.g. a fully ingested repo, vault, or corpus — whose statements are read back, chunked, and condensed into an ontology). Set ontologyMode: 'codebase' for software projects, or 'procedural' to write a DIGEST of how the project works (prose statements with [[wikilinks]], not relation triples) from an already-uploaded graph — save it as <repo|vault>-<project>-digest for optimize_knowledge_base. Use to get a rich overview, a reasoning map of a topic, or a condensed 'how it fits together' graph of a large corpus.

memory_add_relations

Add relations to the InfraNodus memory from text, save it, and provide its name and a link to it for future use.

memory_get_relations

Provide a list of relations from the InfraNodus memory for a given concept or entity

analyze_existing_graph_by_name

Extract and analyze the content of an existing InfraNodus graph from your account.

analyze_text

Extract and analyze a graph from text, URL, YouTube video transcript, or an existing InfraNodus graph.

generate_content_gaps

Generate content gaps from text, URL, or an existing graph using knowledge graph analysis.

generate_topical_clusters

Generate topics and clusters of keywords from text, URL, or an existing graph using knowledge graph analysis.

generate_research_questions

Analyze text or an existing graph and generate innovative research questions based on the content gaps identified between the topical clusters. Provide either text, url, or graphName. Can be used to improve the text and the discourse it relates to

generate_research_ideas

Analyze text or an existing graph and generate innovative research ideas based on the content gaps identified between the topical clusters inside the text that can be used to improve the text and the discourse it relates to.

generate_responses_from_graph

Use text, URL, or an existing InfraNodus knowledge graph and generate responses and expert advice based on a prompt provided.

validate_brief

Validate a caller-provided agenda brief against agenda-brief.schema.json. Use before running scoring, evidence audit, or publication steps to catch missing sections and schema drift. Pass the parsed brief object as brief_json. Returns validation status and schema errors only; it does not judge factual truth, retrieve sources, or improve the brief.

validate_evidence

Validate a caller-provided evidence pack against evidence-pack.schema.json. Use when you need to confirm that claims, evidence IDs, provenance fields, and optional source_category metadata are structurally usable by Agenda Intelligence. Pass the parsed evidence pack as evidence_json. Returns schema validity and errors; it does not verify whether evidence is true, current, or sufficient.

check_evidence_packet

Check a caller-provided evidence packet before human review. Use when an AI output declares claims, source IDs, optional verbatim quotes, and the full supplied source text. Returns packet_complete, source_review_required, or packet_incomplete per claim and overall, with broken references, quote mismatches, lexical-support gaps, unmatched numbers, and owner actions. Deterministic and local-text only: it does not retrieve sources, score source authority, assess factual truth, or authorize an action.

audit_claims

Validate a claim-level evidence audit and summarize support quality. Use after drafting or receiving a memo to check whether important claims point to evidence IDs with explicit support levels, uncertainty hooks, and risk-if-wrong notes. Pass audit_json matching evidence-audit.schema.json. Returns validity, support-level distribution, orphan evidence references, and unsupported-claim counts. It does not verify factual truth or source reputation.