"Understanding 'fetch' (could refer to programming, definition, or other uses)" matching MCP tools:

• get_playbook

  GammaRips Options Intelligence

  Methodology + reference, versioned server-side (re-fetch rather than caching long-term). Arg-driven: * `field` given — the plain-English DEFINITION + role of a signal field (deterministic lookup, no LLM). e.g. field="mom_60". The response's `available_fields` lists every documented field. * `name` given — a methodology playbook (markdown) by name, OR two special reference pages: - name="schema" (or "data-contract") -> the machine-readable substrate DATA CONTRACT: every outcome/label column with its leakage classification (feature|label|opportunity| regime_telemetry|identity) and as-of boundary. Only `feature` columns are safe as selection inputs. - any other name -> the playbook markdown (start-here, daily-workflow, run-your-own-tournament, exit-lab, leakage-and-data-contract, changelog). * neither — the CATALOG of published playbooks (name/title/summary), plus a pointer to the field dict (`field=`) and schema page. Args: name: playbook name, or "schema"/"data-contract" for the data contract. field: a signal field name to explain (overrides `name`).

  Connector

• x_get_tweet

  x-twitter

  Fetch one tweet by id with full engagement metrics: views, likes, retweets, quotes, replies, bookmarks, language, and the quoted tweet inline when there is one. tweet_id is the numeric id from a tweet URL (the digits after /status/) or from any other X tool's results; full tweet URLs are accepted too. Costs 4 credits. Use this to verify engagement before citing a tweet or to read a quoted thread hop by hop. It returns a single tweet, not the conversation around it: for the author's other tweets use x_get_tweets, and to find tweets by topic use x_search.

  Connector

• reddit_get_post

  reddit

  Fetch full post content for up to 100 Reddit posts by their t3_ ids in one call -- the follow-up loop after reddit_search or reddit_get_subreddit_posts. Costs 4 credits for up to 10 ids, +1 credit per further 10 ids; every post comes back with its full body text. With exactly ONE id you may set detail to 'full' for +4 credits to also get the discussion tree (comments flattened in tree order with depth, about 200 per page, with a comments_cursor to continue), or pass comment_id (a t1_ id, also +4 credits) to fetch one specific comment in its post context. Post ids come from the other Reddit tools or from reddit_resolve_url on a post URL. Not for discovering posts; search first, then batch-fetch here.

  Connector

• search_open_grants

  foundation-discovery

  Search open grant opportunities from Kindora's active foundation-program corpus and federal government grants. Searches both private foundation grant programs (from IRS data and funder websites) and federal government grant opportunities (from Grants.gov). Uses full-text search with natural language understanding — queries are parsed into individual terms with stemming, so "youth after school programs" matches programs about youth, after-school, and programming even if those exact words don't appear together. Search covers program names, descriptions, focus areas, beneficiary types, and geographic focus fields. Use the state parameter to focus on geographically relevant opportunities. Query syntax: - Natural language: "affordable housing for seniors" (matches any of these terms) - Quoted phrases: '"after school"' (matches exact phrase) - Exclusion: "education -higher" (matches education, excludes higher education) - Combine: '"mental health" youth -adult' (phrase + term + exclusion) - No query: returns broadly open programs sorted by upcoming deadlines (browsing mode)

  Connector

• submit_workflow

  Botverse

  Submit a multi-step workflow to the Botverse workflow engine. Steps execute in dependency order; parallel branches (multiple steps with the same depends_on) run simultaneously. Returns a workflow_id immediately — poll get_workflow_status every 5–10 seconds until terminal. INTER-STEP REFERENCES: pass a prior step's output into a later step with the string "$.steps.<step_id>.output_key" (e.g. a docx→pdf chain: step to_pdf has depends_on: ["to_docx"] and inputs {"source_url": "$.steps.to_docx.output_key", "input_format": "docx", "output_format": "pdf"} using tool convert_from_url). Workflow params are referenced as "$.params.<name>". No other template syntax (${...} etc.) is supported. BILLING: convert-only workflows run on wallet balance ($0.05/step). Workflows containing transcode or transcribe steps require auto-refill to be enabled at botverse.cloud/dashboard/billing (their cost scales with source duration). Workflow definition uses BWDL (Botverse Workflow Definition Language) — schema at botverse.cloud/schemas/workflow/v1.json.

  Connector

• fetch

  Penfield Memory

  [ChatGPT Connector compat] Fetch memory by ID. Exists to satisfy ChatGPT Deep Research's required `search`/`fetch` tool contract. Native MCP clients should fetch via `recall` + memory_id, or use the API's GET /memories/{id} endpoint directly. Returns a single memory with citation support (id, title, url, text fields). Args: id: Memory UUID to fetch ctx: MCP context Returns: Dict with id, title, url, text, metadata fields

  Connector

• render_mermaid

  Diagrams MCP

  Render a Mermaid diagram definition and return the image with metadata. The definition should be valid Mermaid syntax (e.g. flowchart, sequence, class, ER, state, or Gantt diagram). Returns a list of content blocks: the rendered image plus a JSON text block with metadata including a mermaid.live edit link for opening the diagram in a browser editor. Args: definition: Mermaid diagram definition text. filename: Output filename without extension. format: Output format — ``"png"`` (default), ``"svg"``, or ``"pdf"``. download_link: If True, return a temporary download URL path (/images/{token}) that expires after 15 minutes; if False, return inline image bytes. Defaults to True (URL) — set ``DIAGRAMS_INLINE_DEFAULT=true`` on the server to flip the default. SVG/PDF and PNGs larger than the inline limit always use a download link.

  Connector

• list_campaigns

  argo-mcp

  List all Argo campaigns the current grant token has access to, including the access level ("read" or "read+write") for each. Call this first when the user has not provided a campaign ID. Each entry includes both `campaignName` and `id` (shown inline as `[id: …]` and also in structuredContent.idMap). Use the `id` verbatim for any subsequent tool call that takes a `campaignId`. In prose to the user, refer to campaigns by `campaignName`; do not print the raw `id` unless asked.

  Connector

• get_entity_info

  dannet

  Get comprehensive RDF data for any entity in the DanNet database. Supports both DanNet entities and external vocabulary entities loaded into the triplestore from various schemas and datasets. UNDERSTANDING THE DATA MODEL: The DanNet database contains entities from multiple sources: - DanNet entities (namespace="dn"): synsets, words, senses, and other resources - External entities (other namespaces): OntoLex vocabulary, Inter-Lingual Index, etc. All entities follow RDF patterns with namespace prefixes for properties and relationships. NAVIGATION TIPS: - DanNet synsets have rich semantic relationships (wn:hypernym, wn:hyponym, etc.) - External entities provide vocabulary definitions and cross-references - Use parse_resource_id() on URI references to get clean IDs - Check @type to understand what kind of entity you're working with Args: identifier: Entity identifier (e.g., "synset-3047", "word-11021628", "LexicalConcept", "i76470") namespace: Namespace for the entity (default: "dn" for DanNet entities) - "dn": DanNet entities via /dannet/data/ endpoint - Other values: External entities via /dannet/external/{namespace}/ endpoint - Common external namespaces: "ontolex", "ili", "wn", "lexinfo", etc. Returns: Dict containing JSON-LD format with: - @context → namespace mappings (if applicable) - @id → entity identifier - @type → entity type - All RDF properties with namespace prefixes (e.g., wn:hypernym, ontolex:evokes) - For DanNet synsets: dns:ontologicalType and dns:sentiment (if applicable) - Entity-specific convenience fields (synset_id, resource_id, etc.) Examples: # DanNet entities get_entity_info("synset-3047") # DanNet synset get_entity_info("word-11021628") # DanNet word get_entity_info("sense-21033604") # DanNet sense # External vocabulary entities get_entity_info("LexicalConcept", namespace="ontolex") # OntoLex class definition get_entity_info("i76470", namespace="ili") # Inter-Lingual Index entry get_entity_info("noun", namespace="lexinfo") # Lexinfo part-of-speech

  Connector

• 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.

  Connector

• sieve_dataroom_add

  Sieve

  Add a document to a deal's data room. Creates the deal if needed. This is the primary way to get documents into Sieve for screening. Upload a pitch deck, financials, or any document -- then call sieve_screen to analyze everything in the data room. Provide company_name to create a new deal (or find existing), or deal_id to add to an existing deal. Provide exactly one content source: file_path (local file), text (raw text/markdown), or url (fetch from URL). Args: title: Document title (e.g. "Pitch Deck Q1 2026"). company_name: Company name -- creates deal if new, finds existing if not. deal_id: Add to an existing deal (from sieve_deals or previous sieve_dataroom_add). website_url: Company website URL (used when creating a new deal). document_type: Type: 'pitch_deck', 'financials', 'legal', or 'other'. file_path: Path to a local file (PDF, DOCX, XLSX). The tool reads and uploads it. text: Raw text or markdown content (alternative to file). url: URL to fetch document from (alternative to file).

  Connector

• atlas_start_jd_fit_batch

  CareerProof MCP

  Start a batch JD-FIT analysis: match multiple candidates against a job description (3 credits per candidate). Returns a batch_id. Poll with atlas_get_jd_fit_batch_status(context_id, batch_id) until complete, then fetch with atlas_get_jd_fit_results(context_id). If jd_content is omitted, uses the context's active JD. Requires context_id from atlas_list_contexts and candidate_ids from atlas_list_candidates.

  Connector

• read_url

  Toolora MCP Server

  Use this tool whenever a URL appears in the conversation and the user wants to read, summarise, quote from, or process the page content. Triggers: 'read this article', 'summarise this page', 'what does this link say', 'fetch this URL'. Uses Readability to return clean text, title, author, and excerpt. If the result is empty or incomplete, fall back to scrape_url_js for JS-rendered pages. Free, no API key, no rate-limit signup required.

  Connector

• company_officers

  UK Due Diligence

  Fetch active officers for a Companies House company number. Returns directors, secretaries, and other active officers with appointment dates, nationality, and country of residence. Resigned officers are excluded. Pagination is handled internally — do NOT pass items_per_page or start_index; this tool takes only company_number.

  Connector

• clusters.get

  AI Design Blueprint

  Get one principle cluster by stable slug. Returns the cluster definition, shared rationale, and the full set of member principles (slug + title) so the caller can pivot into principles.get without a second list call. WHEN TO CALL: the user has already named a specific cluster (e.g. 'delegation', 'visibility', 'trust', 'orchestration') OR you have a slug from a prior clusters.list / principles.list response and need its full definition + member principles. The response embeds member principle slugs + titles already, so DO NOT loop principles.get over each member to get a cluster overview — read the response. WHEN NOT TO CALL: the user is describing a topic, failure mode, or keyword in natural language (call principles.search instead); the user wants to discover which clusters exist (call clusters.list); the user wants the definition of one specific principle (call principles.get directly). Idempotent + cacheable per slug. Returns 404-shaped error_payload on unknown slug — the slug must match exactly the value emitted by clusters.list, with no normalization.

  Connector

• lookup_term

  Arco Lexicon

  Returns the canonical Arco definition, related terms, and source URL for any Lexicon term. Supports fuzzy matching — "autonomous company" resolves to "Autonomous Business". Use this tool when you need a precise definition. Use suggest_terms instead when you have a block of text and want to discover which terms apply.

  Connector

• 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.

  Connector

• fetch

  Sofya

  Fetch one or more URLs and return their content as clean markdown. Use this to read articles, documentation, blog posts, or any page where you need the complete text, not just a snippet from search. Also supports PDF, DOCX, and other document formats. Costs 1 credit per URL. Max 10 URLs per request. Failed URLs are not charged. Set include_raw_html=true to also get the raw HTML source in each result. Useful for inspecting embedded URLs, data attributes, iframes, or script tags that are stripped during markdown conversion. Returns null for non-HTML content (PDF, DOCX, etc.). Same cost. Returns: results (array of {title, url, content, raw_html, published_time, success, error}), credits_used, credits_remaining. Args: urls: List of URLs to fetch (max 10) include_raw_html: Include raw HTML source in each result (default false)

  Connector

• get_glossary

  VouchedPros

  Plain-language definitions of industry terms in a service category (e.g. SEER2, AFUE, AHRI match). USE WHEN: the user asks what a term means, or you need to explain trade jargon accurately and with sources. ARGS: `category`; optionally `term` (a slug) for one definition — omit to list. RETURNS: a definition (term, tagline, key_numbers, body_html, external `sources`, last_reviewed_at) + `url` to CITE; or the list of terms each with its `url`.

  Connector

• docraptor_list_ip_addresses

  docraptor

  List the IP addresses DocRaptor uses to fetch remote assets / document_url. Useful for allowlisting DocRaptor on a firewall or asset host. (DocRaptor notes these change over time — prefer HTTP Basic auth over IP allowlisting for securing assets.) DocRaptor API: GET /ips.json.

  Connector