BigContext MCP
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| ingest_documentA | Load, segment, and index a document for search. Supports txt, md, pdf, epub, and html formats. Automatically detects chapters and sections. Args: path: Absolute path to the document file. title: Optional title for the document (defaults to filename). chunk_size: Target size in words for each chunk (default: 2000). overlap: Number of words to overlap between chunks (default: 100). force: Force re-indexing even if document already exists. Returns: Ingestion result with document ID and structure. |
| search_segmentB | Search for relevant segments using TF-IDF. Returns snippets with matched terms highlighted. Args: query: Search query (keywords or phrases). document_id: Optional: limit search to a specific document. segment_id: Optional: search within a specific segment only. limit: Maximum number of results to return (default: 5). context_words: Number of words around matches in snippets (default: 50). Returns: Search results with scores and snippets. |
| get_metadataB | Get metadata, structure, and statistics for a document or segment. Includes top terms by TF-IDF. Args: document_id: ID of the document to get metadata for. segment_id: ID of the segment to get metadata for. include_structure: Include document structure in response. top_terms: Number of top terms to return (default: 10). Returns: Metadata including structure and top terms. |
| list_documentsB | List all indexed documents with their metadata. Args: limit: Maximum number of documents to return (default: 20). offset: Number of documents to skip (for pagination). Returns: List of documents with metadata. |
| compare_segmentsB | Compare two segments to find shared themes, unique terms, and similarity. Useful for understanding relationships between chapters. Args: segment_id_a: ID of the first segment to compare. segment_id_b: ID of the second segment to compare. find_bridges: Find intermediate segments that connect the two. max_bridges: Maximum number of bridge segments to return. Returns: Comparison result with similarity and themes. |
| get_source_capabilitiesA | CRITICAL: Analyze what a document CAN and CANNOT support. Returns detected languages, whether original Hebrew/Greek/Aramaic is present, textual variant availability, and epistemological limitations. MUST be called before making claims about morphology, etymology, or textual criticism. Args: document_id: ID of the document to analyze. Returns: Source capabilities analysis. |
| validate_claimB | Check if a specific claim can be grounded in the source document. Returns whether the claim requires capabilities the document lacks. Use this BEFORE making scholarly assertions. Args: document_id: ID of the document to validate against. claim: The claim or assertion to validate. Returns: Claim validation result. |
| get_epistemological_reportA | Generate complete epistemological analysis before making scholarly claims. Returns: language hard stops, canonical frame detection, auto-critique, confidence decay calculation, and recommendations. Use BEFORE any complex textual analysis. Args: document_id: ID of the document to analyze. query: The research question or claim being investigated. Returns: Epistemological report. |
| check_language_operationB | Check if a specific linguistic operation is allowed. Use before performing morphological, etymological, or text-critical analysis. Args: document_id: ID of the document. operation: The operation to check (e.g., "root analysis"). language: The language involved (hebrew, greek, aramaic). Returns: Language operation permission result. |
| detect_semantic_framesC | Detect conceptual frameworks in a text segment. Identifies causal, revelational, performative, and invocative frames. Prevents reductive analysis by identifying non-causal categories. Args: segment_id: ID of the segment to analyze. query: The research question being investigated. Returns: Semantic frame detection result. |
| analyze_subdeterminationC | Analyze whether textual ambiguity is total indeterminacy or directed subdetermination. Returns what the text CLOSES (excludes) vs. what it LEAVES OPEN, and detects asymmetric relations. Args: segment_id: ID of the segment to analyze. Returns: Subdetermination analysis result. |
| detect_performativesB | Detect performative speech acts where divine speech IS the creative act. Identifies "And God said... and it was so" patterns that resist causal analysis. Args: segment_id: ID of the segment to analyze. Returns: Performative detection result. |
| check_anachronismsA | Check if a research question imports post-biblical conceptual categories. Detects Aristotelian causes, Neoplatonic emanation, Trinitarian doctrine. Args: query: The research question or claim to check. Returns: Anachronism check result. |
| audit_cognitive_operationsA | CRITICAL: Run before ANY response. Validates cognitive constraint compliance. Detects unauthorized operations (synthesis, explanation, causality inference). Returns compliance status and safe fallback if needed. Args: document_id: ID of the document being queried. query: The user query to analyze. planned_output: The planned response text to validate. Returns: Cognitive audit result. |
| detect_inference_violationsA | Scan text for inferential connectors and prohibited abstract nouns. Detects: therefore, thus, implies, means that, ontology, mechanism, structure. These signal unauthorized cognitive operations. Args: text: The text to scan for inference violations. Returns: Inference violation detection result. |
| get_permitted_operationsC | Get permitted cognitive operations based on text genre. Different genres allow different operations (narrative, poetry, wisdom, etc.). Args: segment_id: ID of the segment to check. Returns: Permitted operations result. |
| generate_safe_fallbackA | Generate a safe, compliant response when query requires unauthorized operations. Use when audit_cognitive_operations returns violations. Args: question_type: Type of unauthorized operation (synthesis, explanation, etc.). document_title: Title of the document for the fallback message. Returns: Safe fallback response. |
| build_document_vocabularyB | Build closed vocabulary from document. Creates lexicon of all tokens. Required before using validate_output_vocabulary. Args: document_id: ID of the document to build vocabulary from. Returns: Vocabulary build result. |
| validate_output_vocabularyB | Check if output uses only vocabulary present in the source document. Detects terms imported from outside the text. Args: document_id: ID of the document. output: The output text to validate against document vocabulary. Returns: Vocabulary validation result. |
| validate_literal_quoteA | Verify that a quoted string exists EXACTLY in a segment or document. Use BEFORE claiming any text appears in the source. Returns confidence: "textual" (exact match), "partial" (similar), "not_found". Prevents pattern completion hallucination. Args: quote: The exact quote to validate. document_id: Optional: document to search. segment_id: Optional: specific segment to check. fuzzy_threshold: Similarity threshold for partial matches (0-1). Returns: Literal quote validation result. |
| validate_proximityA | Check if two segments are adjacent (within allowed distance). Use to enforce "same verse or verse+1" constraints. Prevents narrative jump violations. Args: base_segment_id: The anchor segment ID. target_segment_id: The segment ID being referenced. max_distance: Maximum allowed segment distance (0 = same, 1 = adjacent). Returns: Proximity validation result. |
| get_adjacent_segmentsB | Get list of segment IDs within proximity constraint. Use for extraction queries that require adjacency. Args: base_segment_id: The anchor segment ID. max_distance: Maximum distance from base (default: 1). Returns: Adjacent segment IDs. |
| identify_speakerB | Identify who is speaking in a text segment. Returns speaker name, confidence level, and evidence. Domain-agnostic: works for any document type. Args: segment_id: ID of the segment to analyze. priority_patterns: Optional: Speaker names to prioritize. exclude_patterns: Optional: Speaker patterns to flag as ambiguous. expected_speaker: Optional: verify this specific speaker. Returns: Speaker identification result. |
| detect_pattern_contaminationB | Detect when output may be completing a known pattern not in source. Domain-agnostic: works for any genre (religious, fairy tales, legal, etc.). Agent provides patterns dynamically based on document genre. Args: claimed_output: What the agent claims is in the text. segment_id: ID of the segment to check against. patterns: Optional: Pattern definitions with trigger/expectedCompletion. Returns: Pattern contamination detection result. |
| validate_extraction_schemaA | Validate that extraction output follows a strict schema. Detects parenthetical comments, notes sections, evaluative language. Use when user requests pure data extraction. Args: output: The extraction output to validate. fields: Expected field names in output. allow_commentary: Whether commentary is allowed (default: False). Returns: Extraction schema validation result. |
| detect_narrative_voiceA | CRITICAL: Detect the narrative voice type of a text segment. Distinguishes:
Use BEFORE extracting "divine actions" to avoid confusing retrospective prayer with primary divine agency. Args: segment_id: ID of the segment to analyze. domain_vocabulary: Optional DomainVocabulary for enhanced detection. Returns: Narrative voice detection result. |
| validate_agency_executionA | Validates whether a divine action is EXECUTED in-scene vs merely REFERENCED. Key distinction:
The second describes same action but as human memory, NOT primary execution. Args: segment_id: ID of the segment to analyze. divine_agent_patterns: Optional: Patterns to identify divine agent. Returns: Agency execution validation result. |
| detect_text_genreB | Detect text genre to apply correct extraction rules. Genres: historical_narrative, narrative_poetry, prayer_praise, recapitulation, prophetic. DOMAIN-AGNOSTIC: Uses structural patterns by default. Provide domainVocabulary for domain-specific enhanced detection. Args: segment_id: ID of the segment to analyze. domain_vocabulary: Optional DomainVocabulary for enhanced detection. Returns: Text genre detection result. |
| detect_divine_agency_without_speechB | CRITICAL: Detect when an agent acts WITHOUT speaking. DOMAIN-AGNOSTIC: Agent provides agentPatterns dynamically. Separates SPEECH verbs (said, spoke) from ACTION verbs (caused, made, remembered). Examples:
Args: segment_id: ID of the segment to analyze. agent_patterns: Agent names to search for. domain_vocabulary: Optional DomainVocabulary for genre detection. Returns: Divine agency without speech detection result. |
| detect_weak_quantifiersA | Detects weak quantifiers that require statistical evidence. Quantifiers like "frequently", "typically", "always", "never" imply statistical claims that should not be made without counting evidence. Returns recommendation: "allow", "require_count", or "block". Use on agent output BEFORE returning to user. Args: text: Text to analyze (typically agent output). Returns: Weak quantifier detection result. |
| validate_existential_responseA | CRITICAL: Validates response to existential question ("Does X exist in text?"). VALID: "YES" + textual evidence, OR "NO" + explicit denial. INVALID: meta-discourse, hedging, questions, introducing categories not asked. Use AFTER generating response to existential questions to catch evasion. Args: response: The agent response to validate. Returns: Existential response validation result. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 31 tools
Multiple tools have overlapping purposes that could cause confusion. For example, 'audit_cognitive_operations', 'detect_inference_violations', and 'get_epistemological_report' all involve detecting or preventing unauthorized cognitive operations, making it hard for an agent to choose the right one. Similarly, 'validate_claim', 'validate_literal_quote', and 'validate_output_vocabulary' all validate different aspects of textual claims, but their boundaries are not clearly distinct, leading to potential misselection.
The naming is mostly consistent with a verb_noun pattern (e.g., 'analyze_subdetermination', 'detect_pattern_contamination', 'validate_agency_execution'), which aids readability. However, there are minor deviations like 'get_metadata' and 'list_documents' using simpler verbs, and 'ingest_document' using 'ingest' instead of a more common verb like 'load', but these do not significantly hinder understanding.
With 31 tools, the count is too high for a coherent set, making the server feel heavy and overwhelming. The tools cover a wide range of functions from document ingestion to complex epistemological analysis, but many could be consolidated or omitted without losing core functionality, indicating poor scoping and an excessive number that complicates agent usage.
The tool set provides comprehensive coverage for textual analysis and validation in a scholarly or theological domain. It includes ingestion, search, metadata retrieval, various detection and validation tools, and safe fallback mechanisms, ensuring no obvious gaps. The tools support a full lifecycle from document loading to rigorous claim validation, making the surface complete for its intended purpose.