Skip to main content
Glama
304,955 tools. Last updated 2026-07-22 00:06

"Techniques for Writing Files More Efficiently" matching MCP tools:

  • Get the full AI analysis for a single exploit by its platform ID. Returns classification (working_poc, trojan, suspicious, scanner, stub, writeup), attack type, complexity, reliability, confidence score, authentication requirements, target software, a summary of what the exploit does, prerequisites, MITRE ATT&CK techniques, deception indicators for trojans, and the standalone backdoor-review verdict with operator-risk notes when available. Use this to check if an exploit is safe before reviewing its code. Example: exploit_id=61514 returns a TROJAN warning with deception indicators.
    Connector
  • Auto-detect geometry file format and extract metadata statistics. Accepts a 3D geometry file via URL or base64 and returns structured metadata: bounding boxes, triangle counts, manifold analysis, point cloud statistics, and more. This is a read-only analysis tool — it does not perform mesh repair, format conversion, or boolean operations. Supported formats: STL, OBJ, PLY, PCD, LAS/LAZ, glTF/GLB. STEP and IGES support is planned. Provide either file_url (preferred for large files) or file_b64 (for files under 200KB). Include filename for format detection if using file_b64. When using file_url, the format is detected from the URL path extension; filename is not required. Files under 150KB are free. Larger files cost $0.02/MB via x402 (USDC on Base) or card via MPP (Stripe; adds $0.35 surcharge). If payment is required, the response includes payment details. Retry with the payment argument containing the payment proof. Privacy policy: https://caliper.fit/privacy
    Connector
  • Compile TypeScript source (defineIntent() call) into native Swift App Intent code. Returns { swift, infoPlist?, entitlements? } as a string — no files written, no network requests. On validation failure, returns diagnostics (severity, AX error code, position, fix suggestion) instead of Swift. Use: use when TypeScript DSL source should become Swift; use validate for cheaper preflight only. Inputs: source is TypeScript DSL text; options add sandbox, format, plist, or entitlement proof without writing files. Effects: read-only generated Swift/diagnostics; writes no files and uses no network.
    Connector
  • USE WHEN reading the full content of a Pine Script v6 documentation file. Returns the file content; when limit is set, a header shows the char range and offset to continue reading. AFTER calling this tool, use offset=<end> to continue if the header indicates more content is available. For large files (ta.md, strategy.md, collections.md, drawing.md, general.md), prefer list_sections() + get_section() instead. Data sourced from bundled Pine Script v6 documentation.
    Connector
  • Look up a MITRE ATLAS technique — the AI/ML adversarial attack catalog. ATLAS catalogues TTPs targeting machine learning systems: prompt injection, model evasion, training data poisoning, model theft, etc. Roughly 80% of ATLAS techniques are AI/ML-specific (no ATT&CK bridge); 20% mirror an enterprise ATT&CK technique via attack_reference_id — use that to pivot to D3FEND defenses (d3fend_defense_for_attack) and CVE search. Sub-techniques inherit `tactics` from the parent (inherited_tactics=true flag) when ATLAS upstream leaves them empty. Use this tool when the user asks about AI/ML threats, LLM red-teaming, or adversarial ML; for multiple techniques in one call (e.g. drilling into a case study's techniques_used), prefer bulk_atlas_technique_lookup. Returns 404 when the id is not in the synced ATLAS catalog. Free: 30/hr, Pro: 500/hr. Returns {technique_id, name, description, tactics, inherited_tactics, maturity (demonstrated|feasible|realized), attack_reference_id, attack_reference_url, subtechnique_of, created_date, modified_date, next_calls}.
    Connector
  • Bulk ATLAS technique lookup — retrieve full records for up to 50 techniques in a single request instead of N separate atlas_technique_lookup calls. Designed as the natural follow-up to atlas_case_study_lookup, whose techniques_used array can be passed directly. Each item is the same shape as atlas_technique_lookup, including parent-tactics inheritance for sub-techniques (inherited_tactics=true flag) and per-item next_calls (D3FEND bridge when attack_reference_id present, sibling-technique search by tactic, parent lookup for sub-techniques). Free: 30/hr (1 per item), Pro: 500/hr. Returns {results [{technique_id, status (ok|not_found|invalid_format), technique, error}], total, successful, failed, partial, summary}.
    Connector

Matching MCP Servers

Matching MCP Connectors

  • Writing Style Checker (WSC) is a prose linter with an AI-tells detector: alongside classic checks (weasel words, passive voice, duplicate words, long sentences, nominalizations, hedging, filler adverbs) it flags 190+ research-cited words, phrases, and structural patterns overrepresented in AI-generated text — each with an explanation and source.

  • Give your AI agent a phone. Place outbound calls to US businesses to ask, book, or confirm.

  • Return canonical synthesis / patching techniques with role-keyed module realizations drawn from the corpus. Use this when the user asks "how do I do X?" with X being a recognisable technique (low-pass-gate plucks, pinged-filter percussion, parallel multiband processing, complex-oscillator FM, karplus-strong pluck, clocked-delay feedback, modal-resonator excitation, wavefolder harmonics, envelope-follower ducking, Maths-style function-generator omnibus). It's also the right tool when the user has a module and asks "what's this good for?" — pass filter.module_id to retrieve every technique that references the module via its role_realizations. Each technique declares role_definitions (the roles the technique uses, each with required and optional affordances) and role_realizations (concrete modules that fill each role, with the affordances they provide). The model substitutes modules from the user's rack into roles by affordance match — DO NOT treat the realization list as exhaustive or as a recipe. Args: - filter (optional): { capability?, module_id?, text? } - capability: kebab-case capability id (see search_modules _meta.taxonomy). Returns techniques whose required *or* optional capability list includes this id. - module_id: "<manufacturer>/<module-slug>". Returns techniques that have a role_realization referencing this module. - text: free-text phrase. Substring-matches against technique id/label/description AND a curated alias table (technique_aliases) — that's the right surface when a user types evocative prose like "stuttering delay", "plucked string", "source of uncertainty" that doesn't grep against any kebab-case id. Two-way alias match: long alias ("source of uncertainty") matches short query ("uncertainty"), and vice versa. - When multiple filters supplied, AND-intersects. - Omit filter entirely to list all techniques. Returns: { "techniques": [ { "id": "low-pass-gate-pluck", "label": "Low-Pass Gate Pluck", "description": "Send a short envelope...", "required_capabilities": ["lowpass-gate"], "optional_capabilities": ["envelope-generator", "function-generator"], "role_definitions": [ { "role_id": "lpg", "description": "The vactrol-based or vactrol-emulating element. Strictly required...", "required_affordances": ["lowpass-gate"], "optional_affordances": [] }, ... ], "role_realizations": [ { "role_id": "lpg", "module_id": "make-noise/optomix", "affordances_provided": ["lowpass-gate"], "notes": "Two-channel vactrol-based LPG..." }, ... ], "canonical_instance": { "rationale": "...", "lineage": [ { "position": 1, "label": "Buchla 292 (1970)", "module_id": null, "notes": "..." }, { "position": 2, "label": "Tiptop Audio Buchla 292t", "module_id": "tiptop-audio/buchla-292t" }, ... ] }, "counter_canonical_notes": [ { "claim_pushed_back_against": "Optomix is the canonical pairing with Plaits...", "evidence": "The corpus catalogs 19 LPG-capable modules..." } ], "coverage": [ { "role_id": "voice", "realizations_count": 3 }, { "role_id": "lpg", "realizations_count": 19 }, { "role_id": "env", "realizations_count": 6 }, { "role_id": "clock", "realizations_count": 2 } ] } ], "_meta": { "filter": {...}, "feedback_hint"?: string } } How to use role data: - role_realizations are CURATORIAL SAMPLES, not exhaustive lists. The coverage[].realizations_count tells you how many are documented; other modules may fill the same role. - To find modules in the user's rack that can fill a role, use find_role_realizations(technique_id, role_id, available_modules). - canonical_instance is opt-in and sparse. Most techniques don't have one; that absence is information. When present, it documents a documented historical lineage (e.g., Buchla 292 → 292t → MMG → Optomix for low-pass-gate-pluck) — NOT a prescription. - counter_canonical_notes push back on likely training-data priors. When the user invokes a canonical-sounding claim that has a counter_canonical_note, surface the pushback. Errors: - "Module not found: <id>" if filter.module_id is supplied and unknown. - Empty techniques[] with a feedback_hint when filters produce no matches — call report_gap if the user expected coverage.
    Connector
  • Analyze text for writing style issues: weasel words, passive voice, duplicate words, long sentences, nominalizations, hedging, filler adverbs, and research-cited AI tells. Read-only and stateless — text is analyzed in memory on the hosted server and never stored. Returns a plain-text report with each issue's line and column, the matched text, surrounding context, and the reason for AI tells; texts over 100,000 characters return an error message. This hosted server has no filesystem access — the wsc-mcp npm package adds a check_file tool for local files. It only reports issues — to auto-remove duplicate words, follow up with fix_duplicates.
    Connector
  • Run a source-free compiler smoke test through the real Axint pipeline. Use immediately after installing or connecting Axint so the current agent proves it did more than start the MCP server. Use: call immediately after install or first MCP connection; use validate or run for project checks. Inputs: format changes rendering only; the smoke test has no project inputs. Effects: read-only built-in compiler smoke test; writes no files and uses no network.
    Connector
  • List artifacts in a directory. Returns the immediate contents of a directory (not recursive). Separates folders and files for easy navigation. Args: path_prefix: Directory path to list (default: "/") name_pattern: Optional case-insensitive substring filter on file/folder names Returns: Formatted directory listing or error message Examples: >>> await list_artifacts("/") {'success': True, 'path': '/', 'folders': [...], 'files': [...]} >>> await list_artifacts("/", name_pattern="readme") {'success': True, 'path': '/', 'folders': [], 'files': [{'name': 'readme.md', ...}]}
    Connector
  • Get detailed compound information by CID. Returns physicochemical properties (molecular weight, SMILES, InChIKey, XLogP, TPSA, etc.), optionally with a textual description (pharmacology, mechanism, therapeutic use), all known synonyms, drug-likeness assessment (Lipinski/Veber rules), and/or pharmacological classification (FDA classes, MeSH classes, ATC codes). Efficiently batches up to 100 CIDs.
    Connector
  • Scan the local Apple project and write a compact .axint/context pack so Axint can reason over changed files, nearby SwiftUI surfaces, and interaction-risk files instead of only one source file at a time. Use: use before project-aware repair, multi-file SwiftUI work, or interaction-risk analysis. Inputs: changedFiles seed related-file discovery; dryRun returns the pack without writing .axint/context. Effects: writes .axint/context unless dryRun=true; reads local project files only.
    Connector
  • Load more contacts from a previous search result. Call when user says 'show more', 'load more', or 'more contacts/investors'. Routes automatically: query → direct waterfall; title+industry → company waterfall; investor=true → more investors. Pass offset = number of contacts already shown. Pass already_shown_urls for investors.
    Connector
  • Get Lenny Zeltser's expert CTI writing guidelines. Topics include tone, words, structure, executive_summary, voice, articles, summary, brief (one-page brief section guidance), handoffs (cross-server routing), methodology (the three subsections), fields (per-field guidance), and CTI-specific topics: attribution (full Six Signals prose), confidence (ICD-203 ladder), pyramid_of_pain, six_signals (signals table only), and anti_patterns. The general writing topics (tone/words/structure/executive_summary) now defer to `get_security_writing_guidelines` for the canonical Five Elements rules; CTI-specific content lives in the other topics. Pair the 'fields' topic with field_id for single-field guidance. This server never requests your campaign or threat-intel notes and instructs your AI to keep them local—templates and guidelines flow to your AI for local analysis.
    Connector
  • Look up a MITRE ATT&CK technique by ID or keyword for authorized penetration testing and security research. Returns the full technique record: name, associated tactics, description, detection opportunities (log sources, behavioral indicators), real-world procedure examples from public reporting, recommended mitigations, and related sub-techniques. The detection and mitigation sections make this equally useful for defenders building detection coverage. Accepts exact IDs (T1190, T1059.001) or keyword search (e.g., "sql injection", "pass the hash", "web shell upload").
    Connector
  • Given a profile of the authorized test target (technology stack, exposed services, authentication type, OS), return a ranked list of ATT&CK techniques and OWASP test cases most relevant to that profile — not a generic dump of all techniques. Ranking factors: platform match, service match, auth type exposure, technique prevalence. Each result includes why it is relevant to this specific profile, the detection opportunity, and the recommended mitigation. Use when starting an authorized engagement to prioritize the testing scope; pair with pentest_guide to get the full methodology for each top-ranked vector.
    Connector
  • Search for a literal string or basic regex across all files in either the served dist or the editable source tree. Use this BEFORE batch-reading files to find candidates — saves the 'read 14 batches just to find which 3 files matter' round trip. Pass `target: "source"` to search the editable tree (requires Site.sourceStored=true).
    Connector
  • Call get_deploy_instructions first. Then call this once you've decided app_type and frontend_template. Returns starter files, deploy contract, and next steps for a new app. The returned files are the baseline that deploy_app will diff against. Next, call get_appdeploy_sdk_reference with the same features for SDK types, rules, and code reference.
    Connector
  • Deploy or update a website or web app to get a public URL. Text files only in files[]. files[] must be a JSON array, even for one file. Example: files: [{"filename":"src/App.tsx","content":"..."}]. Never pass a bare string or a single file object. Use files[] for inline text edits and diffs, not for copying large existing local file contents into tool params. Never inline or base64-encode binary assets/resources in files[]; use upload_assets first for images, fonts, media, PDFs, archives, and other client-supplied file assets, then pass upload_id. Inline deploy_app text payloads MUST be compact. For JavaScript/TypeScript/JSX/TSX string literals, use single quotes wherever valid. Keep inline HTML/CSS/JS/TS diff from/to values single-line wherever valid; do not include newline characters unless required for valid syntax. Template files from get_app_template are auto-included as the baseline — use diffs[] to modify them, content only for entirely new files. New apps: set app_id to null, provide app_name, description, app_type, frontend_template, and features. Updates: provide existing app_id, features, and either changed files/deletePaths or upload_id. If upload_id is provided, do not also send files[] or deletePaths[]; the upload manifest owns all text changes, diffs, and delete operations. Rules: do not add @appdeploy/client or @appdeploy/sdk to package.json (platform-injected). SPAs must use HashRouter. Frontend must never import @appdeploy/sdk; backend must never import @appdeploy/client. Frontend must use api from @appdeploy/client for backend calls, never fetch() or axios. If frontend realtime is used, @appdeploy/client websocket usage is ws.connect() only; do not call ws.subscribe/ws.publish/ws.send directly on ws. After deploy, poll get_app_status every 5s until status is 'ready' or 'failed'. If get_app_status returns QA/e2e/runtime errors, attempt automatic fixes and redeploy up to 3 times before asking the user for guidance.
    Connector
  • Put one or more media files into your workspace media library (max 10, uploaded sequentially). This is step one of attaching media to a node - follow up with clipform_attach_node_media to place the returned media_asset_id on a node. When a public URL is provided, the media is fetched and stored automatically. For video: ingested via Mux. For image: stored in Supabase. Captions from clipform_generate_tts enable per-word highlighting in the viewer once attached.
    Connector