io.github.stand-out-of-my-sun/diogenes-dev-utilities-mcp

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Méthode : MCP Hub analyse la description de chaque outil (couche 1) et, pour le verdict fort, exécute le serveur dans une micro-VM isolée avec leurres et réseau bloqué (couche 2). On publie des noms et des compteurs, jamais le contenu brut renvoyé par un outil.

Les 10 outils

• arbitrate_agent_deliverable_escrow≈ 566 tokens

  Evaluates a worker agent's submitted task deliverable against agreed contract acceptance criteria to issue a deterministic escrow settlement verdict ('RELEASE_FUNDS_TO_WORKER_AGENT', 'REQUEST_REVISION', or 'REFUND_CLIENT'). Use when settling multi-agent bounties or verifying subcontractor deliverables before releasing MPP/L402 escrow funds; for checking syntactic JSON Schema property types without evaluating semantic contract terms, use enforce_json_schema_types instead. Read-only call (32 KB, 60 RPM): accepts 'deliverable' (with enum defaults), uses '?demo=true' or MPP (150 sats); HTTP 400 if invalid.

  Paramètres, retour & exemple (3)

  Paramètres

  • terms · string · optionnel
    Contract acceptance criteria that the deliverable must satisfy; defaults to 'Complete verified JSON deliverable' when omitted.
  • deliverable · string · requis
    JSON string or text deliverable submitted by the worker agent.
  • arbitration_standard · string · optionnel · strict_all_criteria | substantial_completion | schema_only
    Strictness of contract verification; defaults to 'strict_all_criteria' when omitted.

  Exemple d'appel

  {"name": "arbitrate_agent_deliverable_escrow", "arguments": {"deliverable": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}], "structuredContent": {"escrow_action": "texte", "quality_score": 1, "arbitration_notes": "texte", "contract_satisfied": true}}

• clean_tabular_dataset≈ 481 tokens

  Deduplicates repetitive lines or records, normalizes Unicode whitespace, strips HTML/control noise, and canonicalizes tabular text or JSON arrays. Use when cleaning noisy scraped lists, CSV rows, or duplicate log lines before indexing or analysis; for extracting typed JSON fields from free-form prose paragraphs, use extract_structured_json_entities instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'data' ('normalization_mode' defaults to 'exact_match'), supports '?demo=true' or L402 (100 sats), and returns HTTP 400 if invalid.

  Paramètres, retour & exemple (2)

  Paramètres

  • data · string · requis
    Raw newline-delimited text, CSV rows, or JSON array string to deduplicate and normalize.
  • normalization_mode · string · optionnel · exact_match | case_insensitive_trim | alphanumeric_canonical
    Deduplication key sensitivity; defaults to 'case_insensitive_trim' when omitted.

  Exemple d'appel

  {"name": "clean_tabular_dataset", "arguments": {"data": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}], "structuredContent": {"cleaned_data": [], "cleaned_count": 1, "original_count": 1, "duplicates_removed": 1}}

• compile_xml_agent_prompt≈ 497 tokens

  Compiles a raw task description into a structured XML system prompt (<role>, <context>, <objective>, <constraints>, <workflow>, <output_format>) engineered to prevent instruction drift. Use when authoring or upgrading a system prompt for an autonomous agent or sub-agent workflow; for mapping a task objective to a concrete sequence of MCP tool calls, use route_mcp_workflow_plan instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'task' ('prompt_style' defaults to 'anthropic_xml_tags'), supports '?demo=true' or L402 (100 sats), and returns HTTP 400 if invalid.

  Paramètres, retour & exemple (2)

  Paramètres

  • task · string · requis
    Raw task instructions, persona goal, and operational constraints to compile.
  • prompt_style · string · optionnel · anthropic_xml_tags | markdown_sections | strict_json_responder
    Structural formatting template; defaults to 'anthropic_xml_tags' when omitted.

  Exemple d'appel

  {"name": "compile_xml_agent_prompt", "arguments": {"task": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}], "structuredContent": {"compiled_prompt": "texte", "estimated_tokens": 1, "key_enhancements": [], "optimization_score": 1}}

• enforce_json_schema_types≈ 502 tokens

  Validates a raw LLM output object against a target JSON Schema, automatically coercing mismatched scalar types (e.g. string '42' to integer 42), injecting schema defaults, and pruning undeclared hallucinated fields. Use when enforcing schema compliance and type safety on structured LLM outputs before passing them to typed APIs; for healing unparseable raw JSON text with broken quotes or trailing commas, use heal_malformed_json instead. Read-only edge call (32 KB, 60 RPM): accepts 'raw_output' (with enum defaults), uses '?demo=true' or L402 (75 sats); HTTP 400 if invalid.

  Paramètres, retour & exemple (2)

  Paramètres

  • raw_output · string · requis
    JSON string or object produced by an LLM to validate and coerce.
  • coercion_policy · string · optionnel · prune_undeclared_and_coerce | coerce_only_keep_extra | strict_reject_on_mismatch
    Policy for handling extra undeclared properties; defaults to 'prune_undeclared_and_coerce' when omitted.

  Exemple d'appel

  {"name": "enforce_json_schema_types", "arguments": {"raw_output": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}], "structuredContent": {"valid": true, "enforced_object": {}, "coercions_applied": [], "unrecognized_fields_pruned": []}}

• extract_structured_json_entities≈ 491 tokens

  Extracts structured JSON records, named entities, dates, and financial figures from unstructured prose, emails, or HTML/Markdown dumps. Use when converting unstructured natural-language text into a structured JSON array of entities; for validating and coercing an existing JSON payload against a formal JSON Schema, use enforce_json_schema_types instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'text' ('extraction_target' defaults to 'general_entities_and_metrics'), supports '?demo=true' or L402 (150 sats), and returns HTTP 400 if invalid.

  Paramètres, retour & exemple (2)

  Paramètres

  • text · string · requis
    Unstructured document, email body, or webpage text to extract structured entities from.
  • extraction_target · string · optionnel · general_entities_and_metrics | contacts_and_organizations | financial_line_items | timeline_events
    Primary entity domain to extract; defaults to 'general_entities_and_metrics' when omitted.

  Exemple d'appel

  {"name": "extract_structured_json_entities", "arguments": {"text": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}], "structuredContent": {"model": "texte", "success": true, "records_count": 1, "extracted_data": {}}}

• generate_x402_vector_embeddings≈ 460 tokens

  Generates normalized high-dimensional semantic vector embeddings settled via Solana SPL USDC ('x402' protocol headers) for decentralized RAG pipelines. Use when computing text embeddings paid via Solana USDC x402 signatures rather than Bitcoin Lightning; for extracting discrete JSON entities from unstructured text, use extract_structured_json_entities instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'text' ('encoding_format' defaults to 'float_array'), supports '?demo=true' or x402 (100 sats), and returns HTTP 400 if invalid.

  Paramètres, retour & exemple (2)

  Paramètres

  • text · string · requis
    Input text string or document chunk to convert into a normalized embedding vector.
  • encoding_format · string · optionnel · float_array | base64_float32
    Vector serialization format; defaults to 'float_array' when omitted.

  Exemple d'appel

  {"name": "generate_x402_vector_embeddings", "arguments": {"text": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}], "structuredContent": {"model": "texte", "embedding": [], "dimensions": 1, "normalized": true}}

• heal_malformed_json≈ 460 tokens

  Deterministic JSON syntax repair: normalizes quotes, unclosed brackets, and boolean literals into compliant RFC 8259 JSON structure without data deletion. Use when fixing syntax-broken JSON strings that fail JSON.parse(); for coercing field types and verifying properties on already-parseable JSON objects against a target JSON Schema, use enforce_json_schema_types instead. Read-only edge call (32 KB, 60 RPM): accepts 'malformed_json' (with enum defaults), uses '?demo=true' or L402 (50 sats); HTTP 400 if invalid.

  Paramètres, retour & exemple (2)

  Paramètres

  • repair_mode · string · optionnel · heuristic_fast | strict_rfc8259 | close_truncated_stream
    Bracket-closing and literal-normalization strategy; defaults to 'heuristic_fast' when omitted.
  • malformed_json · string · requis
    Raw broken, truncated, or markdown-wrapped JSON string to repair.

  Exemple d'appel

  {"name": "heal_malformed_json", "arguments": {"malformed_json": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}], "structuredContent": {"status": "texte", "fixes_applied": [], "parsed_object": {}, "repaired_json": "texte"}}

• infer_mpp_streaming_session≈ 479 tokens

  Initializes a Base USDC Machine Payments Protocol (MPP) metered inference session, allocating a streaming token quota and returning a step-by-step execution plan. Use when running session-metered agent planning settled via Base EVM MPP 'Payment-Challenge' headers; for mapping a task goal to L402 MCP tool calls without opening an MPP streaming session, use route_mcp_workflow_plan instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'task' ('token_quota_tier' defaults to 'compact_1024'), supports '?demo=true' or MPP (500 sats), and returns HTTP 400 if invalid.

  Paramètres, retour & exemple (2)

  Paramètres

  • task · string · requis
    Agent workflow task to execute within the metered MPP session.
  • token_quota_tier · string · optionnel · compact_1024 | standard_4096 | extended_16384
    Allocated streaming token ceiling for the session; defaults to 'standard_4096' when omitted.

  Exemple d'appel

  {"name": "infer_mpp_streaming_session", "arguments": {"task": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}], "structuredContent": {"plan": [], "session_id": "texte", "session_status": "texte", "streaming_tokens_allocated": 1}}

• route_mcp_workflow_plan≈ 501 tokens

  Maps a natural-language agent goal to an ordered sequence of Diogenes MCP tool calls with exact endpoint paths, JSON-RPC 2.0 payloads, and cumulative satoshi cost estimates. Use when planning which MCP tools to chain together to solve a multi-step objective within a satoshi budget; for compiling a static XML system prompt rather than an executable MCP tool call sequence, use compile_xml_agent_prompt instead. Read-only edge call (32 KB, 60 RPM): accepts 'objective' ('budget_preference' defaults to 'lowest_sats_deterministic'), uses '?demo=true' or L402 (100 sats); HTTP 400 if invalid.

  Paramètres, retour & exemple (2)

  Paramètres

  • objective · string · requis
    Natural-language description of the workflow goal to route across MCP tools.
  • budget_preference · string · optionnel · lowest_sats_deterministic | max_verification_depth | single_tool_fast
    Cost vs depth routing trade-off; defaults to 'lowest_sats_deterministic' when omitted.

  Exemple d'appel

  {"name": "route_mcp_workflow_plan", "arguments": {"objective": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}], "structuredContent": {"objective": "texte", "estimated_total_sats": 1, "recommended_sequence": [], "deterministic_confidence": 1}}

• summarize_technical_document≈ 462 tokens

  Condenses long technical specifications, RFCs, research papers, or meeting transcripts into high-density executive bullet points preserving exact figures and decisions. Use when producing a human-readable executive summary of a single provided document; for researching a topic across the live web with Google Search citations, use synthesize_web_intelligence_brief instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'text' ('summary_format' defaults to 'executive_bullets'), supports '?demo=true' or L402 (100 sats), and returns HTTP 400 if invalid.

  Paramètres, retour & exemple (2)

  Paramètres

  • text · string · requis
    Full document text, RFC, or transcript to summarize.
  • summary_format · string · optionnel · executive_bullets | decision_log_table | one_paragraph_abstract
    Output presentation structure; defaults to 'executive_bullets' when omitted.

  Exemple d'appel

  {"name": "summarize_technical_document", "arguments": {"text": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}], "structuredContent": {"model": "texte", "success": true, "summary": "texte", "compression_achieved": true}}

• synthesize_python_automation_code≈ 487 tokens

  Synthesizes PEP 8 compliant, self-contained Python 3 scripts, data parsers, and CLI utilities complete with type hints, docstrings, and inline assertion tests. Use when generating a standalone Python automation script or parser from a functional specification; for summarizing existing documentation or codebases into executive bullet points, use summarize_technical_document instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'prompt' ('dependency_policy' defaults to 'stdlib_only'), supports '?demo=true' or L402 (300 sats), and returns HTTP 400 if invalid.

  Paramètres, retour & exemple (2)

  Paramètres

  • prompt · string · requis
    Functional specification, input/output formats, and edge cases for the Python script.
  • dependency_policy · string · optionnel · stdlib_only | allow_requests_and_pydantic | async_httpx_tokio
    Allowed library dependencies in generated code; defaults to 'stdlib_only' when omitted.

  Exemple d'appel

  {"name": "synthesize_python_automation_code", "arguments": {"prompt": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}], "structuredContent": {"model": "texte", "output": "texte", "success": true, "stdlib_only": true}}

• synthesize_safe_regex≈ 501 tokens

  Synthesizes linear-time O(n) regular expressions free of catastrophic backtracking (ReDoS) and validates them against positive and negative test strings. Use when generating and verifying a regular expression pattern for input validation or lexical parsing; for repairing broken JSON syntax directly without writing custom regexes, use heal_malformed_json instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'description' ('flavor' defaults to 'ecmascript_js'), supports '?demo=true' or L402 (50 sats), and returns HTTP 400 if invalid.

  Paramètres, retour & exemple (2)

  Paramètres

  • flavor · string · optionnel · ecmascript_js | python_re | rust_regex_dfa | pcre2
    Target regular expression engine syntax; defaults to 'ecmascript_js' when omitted.
  • description · string · requis
    Natural-language specification of the text pattern to match (e.g. 'RFC 5322 email addresses excluding plus-aliases').

  Exemple d'appel

  {"name": "synthesize_safe_regex", "arguments": {"description": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}], "structuredContent": {"pattern": "texte", "redos_safe": true, "test_results": [], "time_complexity": "texte"}}

• synthesize_web_intelligence_brief≈ 516 tokens

  Synthesizes a real-time market or technical intelligence brief grounded in live Google Search, returning strict JSON ('executive_summary', 'key_developments', 'sentiment', 'sources'). Use when fetching a structured JSON market/news brief with live web citations and sentiment classification; for summarizing caller-supplied text offline without live web search, use summarize_technical_document instead. Read-only edge call (32 KB, 60 RPM): accepts 'topic' ('focus' defaults to 'technical_and_market_catalysts'), supports '?demo=true' or L402 (200 sats), and returns HTTP 400 if invalid.

  Paramètres, retour & exemple (2)

  Paramètres

  • focus · string · optionnel · technical_and_market_catalysts | security_incidents | regulatory_and_macro
    Analytical angle applied to the brief; defaults to 'technical_and_market_catalysts' when omitted.
  • topic · string · requis
    Company, protocol, market sector, or technical topic to brief.

  Exemple d'appel

  {"name": "synthesize_web_intelligence_brief", "arguments": {"topic": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}], "structuredContent": {"topic": "texte", "sources": [], "sentiment": "texte", "key_developments": [], "executive_summary": "texte"}}

• verify_context_compaction_fidelity≈ 562 tokens

  Audits a compacted conversation summary against the original full context to detect dropped user constraints, lost file paths, altered numbers, or missing pending tasks. Use when verifying that an LLM context-compaction summary retained all task-critical facts before truncating history; for generating a new executive summary from a raw technical document, use summarize_technical_document instead. Read-only edge call (32 KB, 60 RPM): accepts 'original_context' ('compacted_summary' defaults to 'standard'), supports '?demo=true' or L402 (90 sats), and returns HTTP 400 if invalid.

  Paramètres, retour & exemple (3)

  Paramètres

  • audit_focus · string · optionnel · constraints_paths_and_numbers | code_identifiers_only | chronological_decisions
    Entity class prioritized during retention scoring; defaults to 'constraints_paths_and_numbers' when omitted.
  • original_context · string · requis
    Full pre-compaction conversation history or task log.
  • compacted_summary · string · requis
    Candidate compacted summary to audit for dropped constraints or entities.

  Exemple d'appel

  {"name": "verify_context_compaction_fidelity", "arguments": {"original_context": "texte", "compacted_summary": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}], "structuredContent": {"safe_to_resume": true, "retention_score": 1, "preserved_entities": [], "missing_critical_entities": []}}

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Source

https://diogenes-micro-gateway.corinth.workers.dev/mcp/dev-utilities