"Tool for saving context and understanding repository structure" matching MCP tools:

• me.validation_history

  AI Design Blueprint Doctrine

  Pro/Teams — return the authenticated user's architect.validate run history with the Blueprint Readiness Score (0-100), letter grade (A-F), and tier (draft, emerging, production_ready). Three lookup modes: (1) `run_id=<id>` returns a SINGLE run with the full persisted result_json — use this to RECOVER a result when your MCP client tool-call timed out before architect.validate returned. The run completes server-side and persists; the run_id is surfaced in the first progress notification of every architect.validate call so you have the recovery handle even when your client gives up early. (2) `repository=<name>` returns the full per-run trend for that repository plus a regression diff between the latest two runs. (3) No arguments returns one summary per repository the user has validated, sorted by most recent. Use modes (2) or (3) BEFORE calling architect.validate again on the same repository — they tell you which principles regressed since the last run, so you can focus the new review on what is actually changing. Auth: Bearer <token>. Pro or Teams plan required.

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• update_form

  Brieform

  Modify an existing form. Two modes: MODE A (structured, preferred): pass the full updated `form` structure. Use this when you have the form in conversation context — fetch it via `get_form` first if needed, apply the user's changes, send the new structure. PUT semantics: replaces all fields/theme/etc. Include each field's `id` from `get_form` to preserve response history for existing fields. MODE B (prompt, fallback): pass `prompt` describing the change ("add a budget field", "make the message field optional", "change theme to dark blue"). Brieform's AI uses the current form as context and applies the modification. ⚠️ WARNING: prompt mode may silently modify unrelated parts of the form (options, isMultiStep, steps) beyond what was requested. Use MODE A for precise, targeted changes. If the form is currently published, the changes go LIVE IMMEDIATELY — the `summary` returned will include a warning. Inform the user. Returns the same shape as `create_form`, with the actual `status` reflecting if the form is still draft or already published.

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• company_search

  Drillr — The financial MCP for AI agents

  Use for qualitative company discovery (industry, business model, supply chain, competitors, management background). For numerical screening (revenue, margins, ratios, growth rates) use run_sql on company_snapshot instead. Drillr's company knowledge base — searchable across industry classification, product offerings, business model, segment structure, competitive landscape, supply chain, management background, and customer profile. Pass a natural language description (e.g. "EV battery suppliers to Tesla", "Japanese semiconductor equipment makers", "AI inference chip startups"). Returns a structured list of matching companies with context snippets. ONLY for finding a LIST of companies by description.

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• deltasignal_issuer_deep_report

  DeltaSignal ATLAS-7

  Use this read-only composite workflow tool for a paid filing-backed issuer drilldown when a daily brief pressure or opportunity row needs causality, not just a headline score. It server-enforces a broad issuer evidence plan: readiness, company_fundamentals, covenant_stress, peer_ranking, alpha_signals, SPECTRA field-map, ATLAS history, ATLAS-7 calculation history, CompanyFacts history, point-in-time history, daily_changes, risk_distribution, and top_stressed rank context. Parameters: ticker is required and normalized to uppercase; source_date, source_date_from, source_date_to, as_of_date_from, as_of_date_to, and output_mode=compact are optional reproduction controls. Behavior: read-only and idempotent; it has no destructive side effects, performs bounded internal fan-out, preserves partial failures, and explicitly reports missing evidence instead of inventing filing, liquidity, covenant, crypto-exposure, market-structure, or scenario facts. Use it for GME-style paid reports that must explain why a CRITICAL stress row exists, what filing evidence supports it, what changed, what peer context says, what historical stress path is available, and which sections still require external or future data.

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• get_market_analogs

  mcp

  Nearest historical market-state analogs to right now: k-NN over the cross-asset state (SPX/NDX momentum, VIX level + term structure, DXY, yield curve) with what SPX/NDX/BTC actually did over the following 1d/5d (median, quartiles, hit-rate) per analog and in aggregate. k = 3-25 (default 12), episode-separated. Same data as REST /analogs. Conditioning context, NOT a prediction.

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• get_market_analogs

  n0brains

  Nearest historical market-state analogs to right now: k-NN over the cross-asset state (SPX/NDX momentum, VIX level + term structure, DXY, yield curve) with what SPX/NDX/BTC actually did over the following 1d/5d (median, quartiles, hit-rate) per analog and in aggregate. k = 3-25 (default 12), episode-separated. Same data as REST /analogs. Conditioning context, NOT a prediction.

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• scan_skill

  SecurityScan

  Scan a GitHub repository or skill URL for security vulnerabilities. This tool performs static analysis and AI-powered detection to identify: - Hardcoded credentials and API keys - Remote code execution patterns - Data exfiltration attempts - Privilege escalation risks - OWASP LLM Top 10 vulnerabilities Requires a valid X-API-Key header. Cached results (24h) do not consume credits. Args: skill_url: GitHub repository URL (e.g., https://github.com/owner/repo) or raw file URL to scan Returns: ScanResult with security score (0-100), recommendation, and detected issues. Score >= 80 is SAFE, 50-79 is CAUTION, < 50 is DANGEROUS. Example: scan_skill("https://github.com/anthropics/anthropic-sdk-python")

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• code_get_file

  searchcode

  Get code from a remote public git repository — either a specific function/class by name, a line range, or a full file. PREFERRED WORKFLOW: When search results or findings have already identified a specific function, method, or class, use symbol_name to extract just that declaration. This avoids fetching entire files and keeps context focused. Only fetch full files when you need a broad understanding of a file you haven't seen before. For supported languages (Go, Python, TypeScript, JavaScript, Java, C, C++, C#, Kotlin, Swift, Rust) the response includes a symbols list of declarations with line ranges. This is not a first-call tool — use code_analyze or code_search first to identify targets, then extract precisely what you need.

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• vector_quantize

  IA-QA — 130+ QA & Dev Tools for AI Agents

  Simulate int8 or int4 quantization of float32 embedding vectors. Reduces storage by 4x (int8) or 8x (int4). Returns quantized values, scale factor, and precision loss (MSE). Useful for understanding vector DB compression trade-offs.

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• system_reliability

  ReliaStats

  Given per-component reliabilities and a structure ('series' or 'parallel'), return the system reliability. Series = product (all must work). Parallel = 1 − product(1−Rᵢ) (at least one works). Useful for back-of-envelope RBD calcs before reaching for full RBD tooling. For mixed-structure systems (series with parallel sub-blocks), call this tool repeatedly on the sub-blocks. ANTI-FABRICATION: exact closed-form. Quote verbatim.

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• get_context_setup_status

  paid-ads

  Data tool for the current user's saved client context, including client setup status, advertiser profiles, synced account/campaign counts, and any open setup questions. For the user-facing setup UI, prefer render_context_onboarding.

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• get_signup_link

  Gonka Network Pricing

  Get Gonka Network signup link with referral bonus (12M nGNK free tokens). Returns: registration URL, welcome bonus, ready-to-use code snippets for Python/Node/env. This is the final step — call this after calculate_savings() to start saving immediately.

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• dataflow_structure

  Statec Lu

  Get the structure (Data Structure Definition) of one STATEC dataset: its ordered dimensions and, for each, the valid codes. Use this BEFORE get_data to learn how to build the dot-separated SDMX `key`. The key has one position per dimension, in `dimension_order`; an empty position is a wildcard. Example: dataflow_structure({ dataflow_id: "DF_A1100" }).

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• moxie.get_conventions

  Moxie Docs

  Get the coding conventions Moxie inferred for the repository. Read-only; no side effects. Returns a Markdown list grouped by category (e.g. testing, structure, docs, review); each convention has a title, summary, confidence score, agent guidance, and the source file paths that evidence it. Use this for the general rules to follow; when you already know the files you're about to edit, prefer moxie.get_doc_impact for conventions scoped to those paths.

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• create_form

  Brieform

  Create a new draft form for the authenticated user. PREFER structured input: when the user describes a form in natural language, generate the form structure yourself (title, description, fields with proper types and labels, theme if requested) and pass it as `form`. This avoids extra AI inference on Brieform's side and gives you precise control. FALLBACK: pass `prompt` (the user's request verbatim) only when you don't have enough context to generate the structure — Brieform's AI will handle it. The form starts as a draft (not publicly accessible). Call `publish_form` to make it shareable. Returns `form_id` (save for subsequent operations), `preview_url` (signed, 24h, read-only — share with user), and `summary` for natural relay. Field types available: text, email, tel, textarea, select, radio, checkbox, rating, date, number, url, password, gdpr. File uploads are not yet supported.

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• score_sleepwalker_content

  Sleepwalker

  Score a URL with Content Intelligence without saving a run. Prefer passing compact trends from discover_sleepwalker_content_trends; omitting trends makes Sleepwalker discover them first and can be slower.

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• get_knowledge_stats

  mcp-klever-vm

  Get summary statistics of the Klever VM knowledge base. Returns total entry count, counts broken down by context type (code_example, best_practice, security_tip, etc.), and a sample entry title for each type. Useful for understanding what knowledge is available before querying.

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• me.validation_history

  AI Design Blueprint

  Pro/Teams — return the authenticated user's architect.validate run history with the Blueprint Readiness Score (0-100), letter grade (A-F), and tier (draft, emerging, production_ready). Three lookup modes: (1) `run_id=<id>` returns a SINGLE run with the full persisted result_json — use this to RECOVER a result when your MCP client tool-call timed out before architect.validate returned. The run completes server-side and persists; the run_id is surfaced in the first progress notification of every architect.validate call so you have the recovery handle even when your client gives up early. (2) `repository=<name>` returns the full per-run trend for that repository plus a regression diff between the latest two runs. (3) No arguments returns one summary per repository the user has validated, sorted by most recent. Use modes (2) or (3) BEFORE calling architect.validate again on the same repository — they tell you which principles regressed since the last run, so you can focus the new review on what is actually changing. Auth: Bearer <token>. Pro or Teams plan required.

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• deltasignal_issuer_deep_report

  DeltaSignal ATLAS-7

  Use this read-only composite workflow tool for a paid filing-backed issuer drilldown when a daily brief pressure or opportunity row needs causality, not just a headline score. It server-enforces a broad issuer evidence plan: readiness, company_fundamentals, covenant_stress, peer_ranking, alpha_signals, SPECTRA field-map, ATLAS history, ATLAS-7 calculation history, CompanyFacts history, point-in-time history, daily_changes, risk_distribution, and top_stressed rank context. Parameters: ticker is required and normalized to uppercase; source_date, source_date_from, source_date_to, as_of_date_from, as_of_date_to, and output_mode=compact are optional reproduction controls. Behavior: read-only and idempotent; it has no destructive side effects, performs bounded internal fan-out, preserves partial failures, and explicitly reports missing evidence instead of inventing filing, liquidity, covenant, crypto-exposure, market-structure, or scenario facts. Use it for GME-style paid reports that must explain why a CRITICAL stress row exists, what filing evidence supports it, what changed, what peer context says, what historical stress path is available, and which sections still require external or future data.

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• get_cif

  Crystallography

  Fetch the full CIF (Crystallographic Information File) for a COD structure by its numeric COD ID — the actual machine-readable structure: symmetry operations and the atom sites (element, fractional x/y/z coordinates, occupancy). Use after search_structures/get_structure when you need the real atomic structure, not just the metadata/link. Returns the CIF text plus a parsed summary (formula, space group, cell, atom count). Keyless.

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