Skip to main content
Glama
214,678 tools. Last updated 2026-06-19 23:35

"How to read document contents" matching MCP tools:

  • Full-text search the ACC Docs module on a project for drawings, specs, submittals, and other documents matching a query string. Calls the APS Data Management v1 search endpoint scoped to a project. When to use: an agent needs to locate a spec section, a sheet, or a submittal by keyword (e.g. 'fireproofing', 'A-101', 'RFI 23'). When NOT to use: you already have the document URN/lineage — fetch it directly. You want the file contents — this returns metadata; download separately via Data Management. APS scopes: data:read account:read Rate limits: APS default ~50 req/min per app per endpoint; Model Derivative translation jobs ~60 req/min; OSS uploads size-limited per file to 100MB for direct upload, larger via resumable. Errors: 401 APS token expired/invalid — refresh; 403 scope or resource permission denied (Docs module access required); 404 project_id not found — check the ID (note: this endpoint re-prepends 'b.' so pass the UUID form); 429 rate limited — backoff and retry; 5xx APS upstream outage — retry with jitter. Side effects: READ-ONLY. Inserts a row into D1 usage_log. Idempotent.
    Connector
  • Get top-level Partle platform statistics. Use for size questions ("how big is Partle?", "how many stores does Partle cover?"). Aggregate counts only — no per-product or per-store data; use `search_products` / `search_stores` for that. Read-only. No authentication. Cheap, but rarely changes — long-running agents should cache the result. Returns: ``{"total_products": int, "total_stores": int, "description": str}``.
    Connector
  • Get auto-discovered structural type classifications from a discovery session. After running discover_patterns, returns the structural categories the platform identified in the data — without being told what categories exist. Each category includes document count, distinguishing fields, and domain hints inferred from the data shape. This is a read-only retrieval. If discover_patterns has not been run against the given blueprint namespace (or the session has expired), returns an empty type list with status="no_session". Use after discover_patterns when you want to understand how the platform grouped your data before deciding which patterns to promote via approve_rule. Args: api_key: GeodesicAI API key (starts with gai_) blueprint: Discovery session namespace (must match the namespace used in discover_patterns) Returns: status: "ok" or "no_session" structural_types: list of {type_id, document_count, distinguishing_fields, domain_hint} total_documents: total document count across all types
    Connector
  • Return the complete UploadKit quickstart walkthrough for Next.js — install, API key env, route handler, provider, first component, optional BYOS — in one markdown document. When to use: the user is brand new to UploadKit and asks "how do I get started?", "set this up for me", or any variation that signals zero prior context. Prefer scaffold_route_handler + scaffold_provider + get_install_command when you already know which specific step they need. Returns: a plain-text markdown document. Takes no parameters. Read-only, static content, idempotent.
    Connector
  • Returns the canonical guide for using TMV from a coding-agent context. Covers the fix-test-retest loop, how to write a good test prompt, how to read the actionTrail / consoleErrors / failedRequests outputs, and common gotchas. Call this first if you're a new agent on a project — it'll save you a debug session. The same content is served at https://testmyvibes.com/docs/coding-agents.
    Connector
  • List the repository's generated documentation as a browsable table of contents — every doc page, not a query-filtered subset. Read-only; no side effects. Returns Markdown grouped by section, each entry with its title, slug, repository path, and source paths, plus the total count and a pagination cursor so you can tell whether more pages remain (no silent truncation). Use this to see what docs already exist before adding one (so you don't duplicate) or to find the slug to pass to propose_doc_update; when you are hunting for a specific topic, search_docs is more direct.
    Connector

Matching MCP Servers

Matching MCP Connectors

  • AI reasoning checks any document against known international standards before your agent acts on it.

  • Transform any blog post or article URL into ready-to-post social media content for Twitter/X threads, LinkedIn posts, Instagram captions, Facebook posts, and email newsletters. Pay-per-event: $0.07 for all 5 platforms, $0.03 for single platform.

  • Rollback a site to a previous snapshot. WARNING: This is destructive. The current state of the container will be replaced with the snapshot contents. Requires: API key with admin scope. Args: slug: Site identifier snapshot_id: UUID of the snapshot to rollback to Returns: {"success": true, "message": "Rolled back to snapshot ..."} Errors: NOT_FOUND: Snapshot not found or not in completed state
    Connector
  • Download a PDF from a URL and extract all text content, page by page. Use this to read the full text of a specific document — for example, an annual report PDF linked from a search_filings result. Best combined with search_filings: use search_filings to locate the document, then parse_pdf_to_text for the full text. Do not use for PDFs that are already well-represented in the database — search_filings is faster and returns pre-ranked, relevant excerpts. Not suitable for scanned (image-only) PDFs without embedded text; those pages will be returned as "(no extractable text)". Args: pdf_url: Direct HTTPS URL to the PDF file, e.g. https://example.com/report.pdf. Must be publicly accessible; authentication-protected URLs will fail. Returns: All text from the PDF with "--- Page N ---" separators between pages. Returns an error string if the download fails, the URL does not point to a valid PDF, or the document exceeds the 60-second download timeout.
    Connector
  • Classify a FINANCIAL document's type and issuing country. Specialised in financial-services documents: payslip, tax_invoice, bank_statement, salary_certificate, payg_summary, receipt. USE THIS WHEN someone shares a document (or a link to one) and asks: what kind of document is this? is this a payslip / invoice / bank statement? route this document. Also use it as the FIRST step before verify_document, so the right checks run. Provide the document ONE way: `url` (a public http(s) link to a PDF or image — fetched server-side, the cheapest call) OR `bytes_b64` (inline base64, plus `filename` for PDF-vs-image routing). Returns `{document_type, country_code, confidence, is_financial_document, evidence, ...}`. HONEST SCOPE: type classification only — NOT an authenticity or fraud judgment (use verify_document for that). Below the confidence threshold it abstains with 'unknown' rather than guessing; non-financial documents classify as 'other'. The document is never stored.
    Connector
  • Get top-level Partle platform statistics. Use for size questions ("how big is Partle?", "how many stores does Partle cover?"). Aggregate counts only — no per-product or per-store data; use `search_products` / `search_stores` for that. Read-only. No authentication. Cheap, but rarely changes — long-running agents should cache the result. Returns: ``{"total_products": int, "total_stores": int, "description": str}``.
    Connector
  • Check the user's current MDMagic credit balance: subscription credits (renewable monthly), purchased credits (permanent), plan name, and plan status. CALL THIS PROACTIVELY when: - The user asks 'how many credits do I have' or similar - After a conversion, if the user wants to know what's left (also returned by convert_document directly) - Before a conversion of an unusually large document, to warn the user if balance is borderline
    Connector
  • Opens a live Trident document and returns its full contents as Trident markup DSL — the human-readable text format used to author diagrams. Use this to READ and UNDERSTAND the diagram: its structure, labels, connections, and layout. Do NOT rely on this to enumerate entity IDs for programmatic use — the DSL can be very large and the output may be truncated. To get a complete, structured list of all entity IDs and counts, use get_document_summary instead. Requires a valid access token.
    Connector
  • Read / write / clear the agent's freeform UI taste notes (a small markdown document of presentation preferences learned from human feedback — 'denser layout', 'no rounded corners'). ONE tool with an `action` enum: get | set | clear. Call `get` BEFORE generating a pane so prior feedback shapes the output; `set` does a whole-document replace (not append). Keep entries about UI/presentation only.
    Connector
  • Retrieves authoritative documentation directly from the framework's official repository. ## When to Use **Called during i18n_checklist Steps 1-13.** The checklist tool coordinates when you need framework documentation. Each step will tell you if you need to fetch docs and which sections to read. If you're implementing i18n: Let the checklist guide you. Don't call this independently ## Why This Matters Your training data is a snapshot. Framework APIs evolve. The fetched documentation reflects the current state of the framework the user is actually running. Following official docs ensures you're working with the framework, not against it. ## How to Use **Two-Phase Workflow:** 1. **Discovery** - Call with action="index" to see available sections 2. **Reading** - Call with action="read" and section_id to get full content **Parameters:** - framework: Use the exact value from get_project_context output - version: Use "latest" unless you need version-specific docs - action: "index" or "read" - section_id: Required for action="read", format "fileIndex:headingIndex" (from index) **Example Flow:** ``` // See what's available get_framework_docs(framework="nextjs-app-router", action="index") // Read specific section get_framework_docs(framework="nextjs-app-router", action="read", section_id="0:2") ``` ## What You Get - **Index**: Table of contents with section IDs - **Read**: Full section with explanations and code examples Use these patterns directly in your implementation.
    Connector
  • Read the contents of a file from a site's container. Max file size: 512KB. Binary files are rejected — use the site's file manager or SSH for binary files. Requires: API key with read scope. Args: slug: Site identifier path: Relative path to the file Returns: {"path": "wp-config.php", "content": "<?php ...", "size": 1234, "encoding": "utf-8"} Errors: NOT_FOUND: File doesn't exist VALIDATION_ERROR: File is binary or exceeds 512KB
    Connector
  • Fetch the full markdown content of a single UploadKit docs page by its path, formatted with title, description, source URL, and the body. When to use: after search_docs identifies a relevant page and you need its full contents to answer a deep question — prefer search_docs first, then get_doc on the top result. Reading the full page avoids relying on snippets that may omit critical context (callbacks, env vars, edge cases). Returns: a plain-text string — "# {title}\n\n> {description}\n\nSource: {url}\n\n---\n\n{content}". If the path is unknown, returns a not-found message suggesting list_docs. Read-only, idempotent.
    Connector
  • Detect and MASK personally identifiable information in a document (PDF or image). USE THIS WHEN you need to know what PII a document contains, or to get a redacted copy before forwarding / logging / passing it to another model. Two layers: a deterministic regex+checksum pass for structured identifiers (emails, payment cards, SSN, PAN, ABN) and a vision model for the unstructured PII — names, addresses, dates of birth, phone numbers, and photo/signature presence. Provide the document ONE way: `url` (a public http(s) link, fetched server-side) or `bytes_b64` (inline base64, plus `filename`). `max_pages` caps how many pages are read (default a few; ceiling 10). Returns `{pii_found, by_type, items[] (type, masked preview, method), redacted_text, has_photo, has_signature}`. Values are MASKED in the response — the raw PII is never returned. DETECTION coverage, not a guarantee: it may miss PII or over-flag, so review before relying on it for compliance. The document is never stored.
    Connector
  • Match one source document against the user's ALREADY-INDEXED corpus and return the best-matching, ranked candidates (RChilli Search & Match Engine). Requires a populated index. Uses RChilli's purpose-built matching engine — more reliable than manually comparing documents. Use this when the user wants to: find the best/top matching resumes for a JD, find matching candidates from their pool, or rank their indexed resumes/JDs against a given document — e.g. "find the best candidates in my database for this job". Also phrased as: shortlist from my pool, top matches for this JD, rank my candidates. Do NOT use for: scoring a single resume against a single JD with no index (use ``search_one_match``); plain keyword lookup (use ``search_simple_search``). Supports all four match directions by combining ``index_type`` and ``doc_type``: - **JD to Resume** — ``index_type='Resume'``, ``doc_type='JD'``: Search the Resume index using a JD as the source document. - **Resume to Resume** — ``index_type='Resume'``, ``doc_type='Resume'``: Search the Resume index using a Resume as the source document. - **Resume to JD** — ``index_type='JD'``, ``doc_type='Resume'``: Search the JD index using a Resume as the source document. - **JD to JD** — ``index_type='JD'``, ``doc_type='JD'``: Search the JD index using a JD as the source document. The ``document_text`` is automatically parsed using the RChilli Resume or JD parser (driven by ``doc_type``), and the resulting structured JSON is base64-encoded and submitted as the match source — no manual encoding is required. Args: index_type: Index to search — ``Resume`` (default) or ``JD``. index_key: Same as ``userkey`` — the RChilli API user key. Leave blank; the authenticated session userkey is injected automatically. doc_type: Type of the source document — ``Resume`` (default) or ``JD``. This determines which parser processes ``document_text``. document_text: Plain-text content of the source document. Parsed and encoded to base64 JSON internally.
    Connector
  • 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.
    Connector
  • Get the compact briefing an agent should read before editing this repository: index status, verified commands, agent tips, top conventions, open documentation gaps, and queued documentation opportunities. Read-only; no side effects. Returns a single Markdown document. Call this first at the start of a task; once you know which files you'll change, follow up with get_doc_impact for path-scoped guidance.
    Connector