"A server for writing tips and creative writing resources" matching MCP tools:

• rating_get_sheet

  website-search

  Get Lenny Zeltser's cybersecurity-writing rating sheet(s) so your AI can apply the rubric. Returns the structured rubric (groups, items, scoring bands) WITHOUT computing a score. Use `rating_score_writing` if you also want a numeric score, gap analysis, or rubric-anchored feedback. This server never requests your draft and instructs your AI to keep it local—rating sheets and scoring instructions flow to your AI.

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• validate_formula

  Dock

  Parse-check a formula expression server-side without writing anything. Returns { ok, error?, rewrittenFormula?, referencedFunctions, unknownFunctions }. Use BEFORE update_row / create_row when the formula references functions or syntax you're not 100% sure of: a `=SUMIFS(...)` with the wrong arg order or a misspelled `=AVERAG(...)` will round-trip into the cell as a stored carrier with no value, and the user will see #NAME? or #VALUE? on next view. Catch it here. `unknownFunctions` flags any identifier that isn't in the Dock Sheets catalog (including likely typos); `referencedFunctions` lists the canonical post-alias names the engine will see. Cheap, public, no auth, no workspace context needed.

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• socrata_get_dataset

  socrata-mcp-server

  Fetch full metadata and column schema for a Socrata dataset by ID. Returns field names, data types, descriptions, row count, and licensing. Always call this before writing a socrata_query_dataset — the column types determine correct WHERE clause syntax: Number columns accept bare literals (year=2023) while Text columns require single-quoted strings (year='2023').

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• validate_doc_markdown

  Dock

  Pre-flight check on markdown BEFORE writing it via update_doc / append_doc_section. Returns { ok, errors, warnings, parsed } with parsed counts per format type (imageCount, videoCount, mermaidCount, mathCount, svgCount, calloutCount, crossRefCount, mentionCount, embedCount, detailsCount, headingCount, byteSize, nodeCount, depth) plus structured DocGuardError-equivalent errors (cap breaches) and non-blocking warnings (cross-refs that don't resolve, mention ids that don't resolve, oversize sources, cap-approaching counts). NEVER writes anything; pure parse + analysis. Use when iterating on rich-format markdown to catch problems before burning a write. Cross-ref + mention resolution is gated on caller's accessible workspace set, so unresolved tokens surface in warnings.

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• cover_letter_tool

  Workopia

  Write a cover letter for a SPECIFIC job — TWO steps. STEP 1 (default; action omitted or 'prepare'): the server returns the job's JD and the candidate's background, plus writing instructions. YOU (the model) then WRITE the cover letter (250–350 words, specific to the role, mapping the candidate's real achievements to the JD — never fabricate). STEP 2: call this tool again with action:'save', cover_letter_text:<your letter>, and job_id — the server renders a PDF and saves it to the candidate's Workopia dashboard (requires sign-in). Use whenever the user asks for a cover letter for a specific job. Resolving job_id (same rules as tailor_resume_tool / job_detail_tool): pass the **Job Id** value from the most recent prior search/refine result VERBATIM; no placeholders like 'JOB_1' or '#1'. For STEP 1 supply ONE of job_id (preferred — server fetches the JD from Mongo) OR job_description, plus the candidate's resume via resume_text / resume_content / json_resume / user_profile.

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• tf_premium_agent_context

  terminalfeed

  The "always start here" premium call for autonomous agents. Composes 13 upstream sources into a curated world-state snapshot: BTC ticker, Fear and Greed, VIX, Fed funds rate, USD-base forex (EUR/JPY/GBP/CHF), HN front page top 5, significant earthquakes 24h, upcoming space launches, top Polymarket markets, and infrastructure status (GitHub, Cloudflare, OpenAI, Anthropic). Returns BOTH a structured JSON `context` object for parsers AND a pre-formatted `system_prompt` string (~350 tokens) the agent pastes verbatim into its LLM context. Saves the agent from making 13 separate calls and writing a formatter. Curation choice (which signals matter, how to compress them) is the moat. Costs 2 credits ($0.04 USDC). 5-min cache. Bearer auth required.

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• about_emora

  mental-health-care

  Identity, services, states served, insurance accepted, age ranges, key facts, crisis resources, and links. Combined site-info + services catalog.

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• validate_html

  Dock

  Pre-flight check on html / css / js BEFORE writing via update_html. Returns { ok, errors, warnings, parsed } where parsed has byte counts per field and `dropped` (true if the sanitizer would strip anything from `html`). Errors cover cap breaches (`html_too_large`, `css_too_large`, `js_too_large`, `total_too_large`) and sanitizer rejection (`html_sanitize_rejected`, `html_sanitize_empty`). At v2 the sanitizer accepts `<script>` and `<link>` — those used to be smells but are now first-class agent markup; isolation lives in the opaque render iframe, not the sanitizer. The smells still stripped: inline `on*=` attributes, `javascript:`/`data:text/html` URIs, `<meta http-equiv>` tags. NEVER writes anything. Use when iterating on a payload so you don't burn a write on something the surface would reject.

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• list_sheet_functions

  Dock

  List the Dock Sheets formula functions an agent can use in a cell carrier. Returns the canonical name, signature, one-sentence description, category (Math/Logic/Text/Date/Lookup/Predicates), rollout slice (v1/v2/v3/v4), and at least one worked example per function. Use this before writing a formula via update_row / create_row so you only reference functions that actually exist (no #NAME? errors). Also returns the alias map (e.g. CONCAT → CONCATENATE) so you can pick the canonical name even when writing the alias the UI accepts. Optional filters: `category` narrows to one category, `slice` narrows to one rollout slice, `name` substring-matches names + descriptions + signatures. Public, no auth, no rate limit beyond global.

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• predict_moment_quality

  Trillboards DOOH Advertising

  Predict the VAS (Viewability Attention Score) a specific creative would achieve at a given moment, based on historical data and causal modeling. Uses the CausalPredictionService which: 1. Embeds the moment description to find historically similar moments 2. If >= 5 similar moments exist with the same creative, uses weighted-average prediction 3. If insufficient data, falls back to Gemini generative prediction 4. Always decomposes the prediction into causal factors WHEN TO USE: - Evaluating whether a creative will perform well in a specific context - A/B testing creative placement hypotheses before committing budget - Understanding which causal factors drive VAS for a creative - Comparing expected performance across different moment types RETURNS: - prediction: { predictedVAS (0-1), confidence (0-1), method ('historical'|'model'), sampleSize } - causal_factors: { audienceMatch, contextMatch, attentionState, socialPotential } (each 0-1) - metadata: { creative_id, moment_description } - suggested_next_queries: Follow-up queries EXAMPLE: User: "How would a coffee ad perform at a transit station during morning rush?" predict_moment_quality({ moment_description: "transit venue, morning commute, 12 viewers, high attention, mostly 25-34 age range", creative_id: "coffee-brand-morning-30s" })

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• generate_xpp_template

  ALM X++ MCP Server

  WHEN: writing an extension or customization -- generates ready-to-use X++ code. Triggers: 'génère un CoC', 'crée une extension', 'generate extension', 'write a CoC class', 'event handler pour', 'template pour'. Uses REAL metadata from the KB (actual field names, method signatures). 'coc' = Chain of Command class, 'table_extension' = extend table with fields/methods, 'event_handler' = pre/post event handler, 'job' = runnable class, 'find_method' = find/exist pattern. ALWAYS call get_object_details first to verify the object exists.

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• list_prebuilds

  mcp-server

  Lists pre-configured reports (prebuilds) available for a connector. **What is a prebuild?** A prebuild is a standardized report maintained by Quanti for a given connector (e.g., Campaign Stats for Google Ads). It defines the BigQuery table structure (columns, types, metrics) and the associated API query. **When to use this tool:** - When the user asks "what reports are available for [connector]?" - When the user doesn't know which data or metrics exist for a connector - BEFORE get_schema_context, to explore available reports for a connector - To understand the data structure before writing SQL **Difference with get_schema_context:** - list_prebuilds → discover which reports/tables EXIST for a connector (catalog) - get_schema_context → get the actual BigQuery schema for the client project (effective data) **Response format:** Returns a JSON with for each prebuild: its ID, name, description, BigQuery table name, and the list of fields (name, type, description, is_metric). Fields marked is_metric=true are aggregatable metrics (impressions, clicks, cost...), others are dimensions (date, campaign_name...). **SKU examples**: googleads, meta, tiktok, tiktok-organic, amazon-ads, amazon-dsp, piano, shopify-v2, microsoftads, prestashop-api, mailchimp, kwanko

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• lookup_authoring_skill

  kernelcad

  Return the kernelcad-authoring SKILL.md body — conventions for writing .kcad.ts scripts (imports, parameters, evaluation contract, common pitfalls). Use this tool BEFORE generating CAD code if your MCP client does not list resources. Clients that do list resources should instead read `kernelcad://skills/authoring` directly — the contents are identical. INPUT: none. OUTPUT: { uri, mimeType, text } where `text` is the SKILL.md body.

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• axint.workflow.check

  Axint

  Read-only agent workflow gate. Requires the current Axint session token from axint.session.start unless requireSession=false is explicitly set. Use this at session start, after context compaction, before planning, writing, building, or... Use: use at stage gates to prove Axint workflow coverage; not a final build/test substitute. Effects: read-only gate but may update tiny workflow freshness stamps; no network.

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• load_filing_workflow

  Shibui Finance

  Load filing workflow for SEC/EDGAR, insider trades, 8-K, Form 4, 10-K queries. REQUIRES get_database_schema then get_query_patterns to be called first (in that order). Call BEFORE writing SQL whenever the user asks about filings, "who filed", "filed a form", filing dates, filing activity, SEC filings, EDGAR, insider trading/buys/sells (Form 3/4/5), 8-K events, 10-K/10-Q reports, ownership filings (SC 13G/13D), proxy statements, or any query involving the sec_filings table. Can be combined with other workflow tools.

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• register

  AI Agents Overflow

  Register your agent to start contributing. Call this ONCE on first use. After registering, save the returned api_key to ~/.agents-overflow-key then call authenticate(api_key=...) to start your session. agent_name: A creative, fun display name for your agent. BE CREATIVE — combine your platform/model with something fun and unique! Good examples: 'Gemini-Galaxy', 'Claude-Catalyst', 'Cursor-Commander', 'Jetson-Jedi', 'Antigrav-Ace', 'Copilot-Comet', 'Nova-Navigator' BAD (too generic): 'DevBot', 'CodeHelper', 'Assistant', 'Antigravity', 'Claude' DO NOT just use your platform name or a generic word. Be playful! platform: Your platform — one of: antigravity, claude_code, cursor, windsurf, copilot, other

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• rating_load_context

  website-search

  Load Lenny Zeltser's complete cybersecurity-writing rating toolkit: all 7 sheets, scoring policy, scoring playbook, and cross-references to the writing guidelines. This server never requests your draft and instructs your AI to keep it local—rating sheets and scoring instructions flow to your AI.

  Connector

• wikidata_sparql_query

  wikidata-mcp-server

  Execute a SPARQL SELECT query against the Wikidata Query Service. Full graph power: multi-hop traversals, aggregations, subqueries, OPTIONAL, FILTER, UNION, BIND. Standard Wikidata prefixes (wd:, wdt:, p:, ps:, pq:, wikibase:, bd:) are auto-injected. The wikibase:label SERVICE is also auto-injected when language is set and the query includes ?<var>Label variables — so you can use ?itemLabel without writing the boilerplate. Hard server timeout is 60s; use LIMIT to keep queries fast. Bindings use the SPARQL 1.1 JSON format: each value is { type, value, "xml:lang"? }. Use wikidata_get_labels to humanize QID results from this tool.

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• rating_score_writing

  website-search

  Get Lenny Zeltser's scoring playbook so your AI can score a draft locally against a cybersecurity-writing rating sheet. THIS IS THE ONLY TOOL THAT PRODUCES NUMERIC SCORES — the writing-coach tools (`get_security_writing_guidelines`, `ir_*`, `product_*`) never score. Returns the rubric plus step-by-step instructions for applying it. This server never requests your draft and instructs your AI to keep it local—rating sheets and scoring instructions flow to your AI.

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• get_site_status

  BorealHost

  Get detailed status of a hosted site including resources, domains, and modules. Requires: API key with read scope. Args: slug: Site identifier (the slug chosen during checkout) Returns: {"slug": "my-site", "plan": "site_starter", "status": "active", "domains": ["my-site.borealhost.ai"], "modules": {...}, "resources": {"memory_mb": 512, "cpu_cores": 1, "disk_gb": 10}, "created_at": "iso8601"} Errors: NOT_FOUND: Unknown slug or not owned by this account

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