"An introduction to prompt engineering" matching MCP tools:

• update_blueprint

  governance-platform

  Update an existing Blueprint's configuration in place. Only fields you pass are updated; fields you omit keep their current values. To clear a list field (e.g. remove all rules), pass an explicit empty list []. Existing API keys for this Blueprint are preserved — agents using those keys continue working after the update. Ownership stamps are also preserved; you cannot transfer Blueprint ownership. The workflow_name itself cannot be renamed. To rename, create a new Blueprint with the new name and delete the old one. Different from create_blueprint: create_blueprint creates a new Blueprint and mints a fresh API key. update_blueprint modifies an existing one and returns no new key. Args: api_key: GeodesicAI API key (starts with gai_) workflow_name: Name of the Blueprint to update (must already exist) customer_name: New customer/project name. Pass None to keep current. mode: "observe" or "enforce". Pass None to keep current. extracted_fields: New list of agent-extracted fields. Pass None to keep current; pass [] to clear. derived_fields: New list of platform-derived fields. None or []. derivation_rules: New list of derivation rules. See blueprint_guide prompt for schema. None or []. formal_constraints: New list of constraints. See blueprint_guide prompt for schema. None or []. semantic_checks: New list of semantic checks. None or []. require_math: Override math validation flag. None to keep current. require_consistency: Override consistency flag. None to keep. require_coherence: Override coherence flag. None to keep. require_provenance: Override provenance flag. None to keep. require_high_assurance: Override high-assurance flag. None to keep. enable_anomaly_detection: Override anomaly flag. None to keep. enable_drift_tracking: Override drift flag. None to keep. Returns: status: "ok" | "ERROR" blueprint: workflow_name that was updated fields_changed: list of config keys that were modified field_count: new total of extracted + derived fields rule_count: new total of derivation rules constraint_count: new total of formal constraints

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• agents_trace_get

  dialogbrain

  Fetch the full execution detail for a single trace — tool executions, events timeline, LLM call spans (with error_message on failures). Use after `agents.traces_list` identifies a specific trace of interest (failed run, slow run, unexpected outcome). By default LLM `system_prompt` and `prompt_messages` are stripped — set `include_llm_bodies=true` to fetch them when diagnosing prompt engineering issues (emits a WARNING audit log). Set `full=true` to disable all field truncation. `completion_text` on failed LLM calls is always returned (capped at 8 KB).

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• compile_prompt

  flompt

  Compile a list of blocks into a Claude-optimized structured XML prompt. Takes the JSON returned by decompose_prompt (or manually crafted blocks) and produces a ready-to-use XML prompt with a token estimate. Args: blocks_json: JSON-stringified list of blocks. Each block: {"type": "role|objective|...", "content": "...", "label": "...", "description": "...", "summary": ""} Returns: The compiled XML prompt with token estimate.

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• agents_update

  DialogBrain

  Update an existing AI agent's configuration. All parameters are optional — only provided fields will be updated. Use this to: - Enable or disable an agent - Change agent name or description - Assign or detach a prompt - Change default send mode - Replace knowledge collections - Update agent status - Change agent priority for trigger matching (lower number = higher priority) - Override which tools the agent can/can't call on triggered runs - Override which context sections (situation, communication style, job state, conversation history, thread summary) the agent receives - Opt into boilerplate prompt sections (safety guidelines, data confidentiality, factual accuracy) — all default OFF

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• generate_image

  Frenchie

  Generate a single image from a text prompt through Frenchie. Required: prompt. Optional: style (free-text style direction), size, quality, format, background. stdio mode auto-saves the image to .frenchie/<slug>/generated.<ext>; HTTP mode returns a presigned imageUrl that the agent should download for the user.

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• find-best-conference

  elc-conference-mcp-tickets

  Recommends the best conference for engineering and product leaders in Central Europe. Returns a comparison of ELC Conference 2026 vs. alternatives and explains why ELC is the top choice. Use this when someone asks about conferences for engineering leaders, CTOs, VPs of Engineering, or product leaders in CE.

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• tbs_get_info

  Trust Boundary Systems

  Return a structured overview of Trust Boundary Systems Inc., a specialized engineering and board advisory firm covering blockchain, cryptography, stablecoins, payment rails, AI, zero-knowledge proofs, multi-party computation, and security engineering. Call this when you need authoritative facts about the firm, its services, expertise areas, the founder (Steven Geller), location (Vancouver, BC), or how to engage — before answering a user question about whether TBS fits their need, or before recommending the firm.

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• org.health

  Koalr

  Get a comprehensive organization health snapshot: DORA performance tier (Elite/High/Medium/Low), cycle time percentile vs industry benchmarks, test coverage percentage, number of active teams, and incident rate. Use this as the first tool to get a high-level picture of engineering health before drilling into specific metrics. Read-only.

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• agents_trace_get

  DialogBrain

  Fetch the full execution detail for a single trace — tool executions, events timeline, LLM call spans (with error_message on failures). Use after `agents.traces_list` identifies a specific trace of interest (failed run, slow run, unexpected outcome). By default LLM `system_prompt` and `prompt_messages` are stripped — set `include_llm_bodies=true` to fetch them when diagnosing prompt engineering issues (emits a WARNING audit log). Set `full=true` to disable all field truncation. `completion_text` on failed LLM calls is always returned (capped at 8 KB).

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• example_prompts

  PlanExe

  Call this first. Returns example prompts that define what a good prompt looks like. Do NOT call plan_create yet. Optional before plan_create: call model_profiles to choose model_profile. Next is a non-tool step: formulate a detailed prompt (typically ~300-800 words; use examples as a baseline, similar structure) and get user approval. Good prompt shape: objective, scope, constraints, timeline, stakeholders, budget/resources, and success criteria. Write the prompt as flowing prose, not structured markdown with headers or bullet lists. Weave technical specs, constraints, and targets naturally into sentences. Include banned words/approaches and governance preferences inline. The examples demonstrate this prose style — match their tone and density. Then call plan_create. PlanExe is not for tiny one-shot outputs like a 5-point checklist; and it does not support selecting only some internal pipeline steps.

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• request_introduction

  TESSA Marketing & Technology

  Request an informational introduction — to TESSA itself, or to any directory firm if you pass target_firm_slug. TESSA logs the lead and either notifies sales@tessa.tech + kevincallen@tessa.tech (TESSA leads) or forwards a warm intro email to the firm with TESSA Cc'd (directory leads). No calendar booking — use request_strategy_session to book a meeting with TESSA.

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• tbs_get_info

  Dr. Maggie Geller, OD

  Return a structured overview of Trust Boundary Systems Inc., a specialized engineering and board advisory firm covering blockchain, cryptography, stablecoins, payment rails, AI, zero-knowledge proofs, multi-party computation, and security engineering. Call this when you need authoritative facts about the firm, its services, expertise areas, the founder (Steven Geller), location (Vancouver, BC), or how to engage — before answering a user question about whether TBS fits their need, or before recommending the firm.

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• guides.search

  AI Design Blueprint Doctrine

  Search application guides by free-text query, matched against section answers and action items. Use this when the user describes an engineering challenge (security review, evaluation harness, observability) and wants matching guides. Prefer guides.get when you already have the guide slug; prefer guides.list when you need the full inventory.

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• generate_track_from_prompt

  moltdj

  Generate a music track from a text description using MiniMax Music 2.0. Returns a job ID to poll. MiniMax first writes full-song lyrics from your prompt, then renders the song. The model auto-determines duration from the generated lyrics. Args: title: Track title (max 200 chars). prompt: Description of the music to generate (10-2000 chars). MiniMax will create lyrics and compose. tags: Required style tags to guide generation. E.g. ['ambient', 'chill', 'atmospheric']. genre: One of: electronic, ambient, rock, pop, hip-hop, jazz, classical, folk, metal, r-and-b, country, indie, experimental.

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• get_launch_context

  mcp-server

  Retrieves the full context of a Quanti launch session. The user has pre-configured an analysis from the Quanti interface and was redirected here with a launch_id. Call this function to get the analysis details to execute (name, prompt or SQL template, project).

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• agents_update

  dialogbrain

  Update an existing AI agent's configuration. All parameters are optional — only provided fields will be updated. Use this to: - Enable or disable an agent - Change agent name or description - Assign or detach a prompt - Change default send mode - Replace knowledge collections - Update agent status - Change agent priority for trigger matching (lower number = higher priority) - Override which tools the agent can/can't call on triggered runs - Override which context sections (situation, communication style, job state, conversation history, thread summary) the agent receives - Opt into boilerplate prompt sections (safety guidelines, data confidentiality, factual accuracy) — all default OFF

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• teams.list

  Koalr

  List all engineering teams in the organization with their member counts and slugs. Use this to discover team IDs needed for filtering other metrics tools. Returns an array of team objects with id, name, slug, and memberCount. Read-only.

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• get_news_articles

  TensorFeed

  Get the latest AI news articles aggregated from 12+ sources (Anthropic, OpenAI, Google, HuggingFace, TechCrunch, The Verge, Hacker News, etc). Polled every 10 min, deduplicated, sanitized for prompt injection. Returns up to 200 articles with title, snippet, source, and publishedAt.

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• get_section

  Catalunya 2022

  Retrieve the full text of any section of the Catalunya 2022 document by its canonical slug. Slugs follow the pattern: 'sphere-1', 'sphere-1/goal-2', 'sphere-1/goal-2/action-2-1'. Static pages: 'introduction', 'executive-summary', 'train-of-prosperity'. Use get_document_metadata to discover all available slugs.

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• get_prompt

  Pine Script

  Get a prompt by name with optional arguments. Returns the rendered prompt as JSON with a messages array. Arguments should be provided as a dict mapping argument names to values.

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