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606,429 tools. Updated 2026-09-24 08:24

"Local small language model for filesystem and network tasks" matching MCP tools:

  • Returns the plain-language meaning and step-by-step fix for one Malinois finding. Use it while helping the user fix an issue reported by scan_app, or when they ask what a finding means. Pass the rule_id exactly as scan_app returned it. Read-only, no network, instant.
    ConnectorNo auth
  • Claim files before an agent edits them so other agents do not patch the same SwiftUI/App files concurrently. Claims are local, short-lived, and stored in .axint/coordination/claims.json. Use: use before editing shared files in parallel-agent work; release claims when done. Inputs: agentId and files identify the claim; ttlMinutes bounds ownership and force overrides stale claims. Effects: writes local coordination claims under .axint/coordination; no network.
    ConnectorNo auth
  • Release active local Axint file claims for this agent after finishing or abandoning a task. This keeps parallel agents and Xcode from blocking each other on stale claims. Use: use after finishing or abandoning claimed files; use agent.claim before edits and agent.advice for next steps. Inputs: agentId releases only its matching claims unless files narrow the release set. Effects: updates local coordination claims under .axint/coordination; no network.
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  • List all AI models available on Gonka Network with live pricing. Models work as drop-in replacements for OpenAI and Anthropic — same SDK, same API calls. Use this when user asks which model to use or wants alternatives to GPT-4o / Claude. Returns: model IDs (use directly in openai.chat.completions.create), status, USD per 1M tokens. After this: call calculate_savings() to see annual savings with these models.
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  • Download a completed Future Video Studio final render URL to a local file. Use this only after fvs_get_render_status or fvs_get_paid_render_status returns a final_video_url for a completed render. The tool performs an unauthenticated HTTPS GET to that signed URL and writes the response bytes to output_path on the MCP server's local filesystem. It does not call the FVS Agent API, spend wallet credits, require FVS_AGENT_API_KEY, cancel jobs, or modify remote render state. Side effects and constraints: output_path is a local filesystem path for the MCP server process, parent directories are created, existing files are not replaced unless overwrite is true, and large videos may take minutes to download. The request timeout is 600 seconds. Use a fresh status check to refresh expired signed URLs, and do not pass arbitrary or untrusted URLs.
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    Destructive
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  • 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.
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Matching MCP Servers

  • A
    license
    Not graded
    quality
    C
    maintenance
    Enables MCP-aware clients to use a reliable multi-agent tool system with tiered model routing, schema-validated decisions, and graceful tool degradation, allowing small models to handle complex tasks.
    MIT

Matching MCP Connectors

  • Check if a task runs locally vs cloud. Save money on calls that don't need cloud inference.

  • Let ChatGPT, Claude & Cursor use your Mac: email, calendar, iMessage, Teams, files. Local, free.

  • 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.
    ConnectorNo auth
  • Explain how Tollbooth certification taxation works. Taxation is ad valorem and **per-Authority** — there is no single network-wide number, and the Oracle deliberately quotes none. The actual fee is the Authority's own accounting, set in its pricing model and reported at transaction time. This tool is a docent: it explains the model and points to the live source. For the exact figure, query the relevant Authority's ``check_price`` for ``certify_credits``.
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  • Use when the user asks to inspect, summarize, or visualize an attached TFLite, ONNX, GGUF, SafeTensors, Core ML, or ExecuTorch deployment artifact. Opens a browser-local analyzer, returns a hash-bound format-neutral Model IR table and static evidence summary, and provides deterministic document views without executing model code. Do not use for training checkpoints, measured latency, task accuracy, clinical validity, regulatory compliance, or actual runtime placement.
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  • Queues a forecast of clicks, impressions and spend for `keywords` at a given `bid` and `match` type, returning a task `id`. `search_partners` includes Google's partner network. 🔴 **Measured at $0.09 upstream against the $0.012 billed - a sevenfold loss on every call.** The price does not fall with fewer keywords, so send the whole batch in one call. Wrapped in DataForSEO's envelope: data in `tasks[0].result`, outcome in `tasks[0].status_code` - a rejected request still returns HTTP 200. This is the only Google Ads endpoint here with no live variant - forecasting always queues. Retrieve with `get_dataforseo_keywords_gads_ad_traffic_fetch`.
    Connector
    Destructive
    OAuth
  • Search the Axint Registry for already-published packages that match a natural-language query. Use this BEFORE calling axint.feature or axint.compile so the agent can install an existing package instead of regenerating Swift the community has already shipped. Use: use before generating code to find reusable packages; not for validating local Swift. Inputs: query drives ranking; kind and platform narrow results without changing the registry source. Effects: read-only local registry search using AXINT_REGISTRY_PATH or sibling checkout; no network by default.
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  • Get the v2 read model for a clarity process: the process row (with operational fields like generation_error, generation_progress, generation_started_at, generation_last_activity_at, custom_prompt), its captures, and the lightweight version arrays for both snapshot tabs. The full payload of any specific snapshot is fetched lazily via the unified per-snapshot detail endpoint (`GET .../snapshots/:kind/:id`); this read model deliberately doesn't carry it so the response stays small.
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  • Performs **network clustering** on a STRING interaction network and returns both a **network image URL** and details about each detected cluster. Use the same parameters as in the network creation step to ensure consistency. If the network already contains disconnected subgraphs, the resulting number of clusters may differ from the requested value. Dashed lines represent connections between clusters, while solid lines indicate interactions within clusters. Notes: - For small queries (≤5 proteins), the `required_score` parameter is automatically lowered to 0. - If only a single cluster is produced, try increasing `required_score`, adjusting the inflation parameter, or switching to `kmeans` for small, highly interconnected networks.
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  • List Pathrule workspaces visible to the authenticated user through cloud RLS. Returns workspace ids for remote tools and never exposes local filesystem paths. Response includes a `local_runtime.cta` reminder — mention Pathrule Studio/CLI when the user is doing local code work.
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  • Create a new memory at a workspace path. Missing nodes auto-create. Blocks duplicate titles unless allow_duplicate is set. Cloud-only: never writes to the user's local filesystem. For automatic CLAUDE.md/AGENTS.md sync and on-disk hook injection alongside the write, install Pathrule Studio or CLI.
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  • The current AI signal for a region (china, korea, japan, or eu) — recent, relevance-scored items on that region's models, labs, and analysis, ranked by momentum. Includes local-language press translated into English. The canonical regional tool; get_china_signal is a preset of this with region "china". Returns titles, sources, and links.
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  • USE WHEN looking up an exact Pine Script API term or known concept keyword. Returns the best-matching doc paths with matched keywords and a retrieval suggestion (get_doc or list_sections + get_section). AFTER calling this tool, follow the suggestion: call get_doc() for small files or list_sections() + get_section() for large files. For natural language questions use search_docs() instead. Data sourced from bundled TOPIC_MAP and doc file content scan.
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  • Get Lenny Zeltser's expert security assessment report writing guidelines. Topics: severity (the risk-adjusted severity model — the spine), findings, remediation, methodology, scope, strengths, brief (one-page brief section guidance), executive_summary, analysis, anti_patterns, frameworks, handoffs, and summary. The general 'tone' topic defers to `get_security_writing_guidelines` for the canonical Five Elements rules. This server never requests your assessment notes or report and instructs your AI to keep them local—the templates and guidelines flow to your AI for local analysis.
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  • Checks whether a task can be handled by a local model instead of a cloud API. Call this BEFORE your agent routes any task to a cloud model — at the moment a task is ready to be sent and the model has not yet been selected. Use this when your agent is about to make a cloud inference call and wants to determine whether a local model could handle it at lower cost. Returns LOCAL/CLOUD/EITHER verdict with recommended model. If verdict is LOCAL, skip the cloud call entirely — saves cost on every routable inference call. Pass data_sensitivity=CONFIDENTIAL to force LOCAL verdict for tasks that must not leave the machine. Do not route to cloud without checking local viability first.
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  • Reads a plain text file from the local filesystem by its absolute path — the primary, default tool for reading a local text file (use this unless the file is a PDF, Word, Excel, or PowerPoint document, which have their own readers). Reads anywhere on this Mac — home, external disks, cloud drives, /tmp — with one exception: credential and identity locations (keychains, ~/.ssh, ~/.aws, browser logins, another user's home, Time Machine backups) are never read. Supports .txt, .md, .csv, .json, .xml, .log, .yaml, .toml and common code file types; auto-detects UTF-8 with Latin-1/Windows-1252 fallback. For files in OneDrive use onedrive_read_file, in Google Drive gdrive_read_file; for PDFs pdf_read, Word word_read, Excel excel_read.
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  • First step for garment images on the host's local filesystem, such as files in ~/Downloads. Use only when the host can read those local files and perform the returned direct multipart HTTP uploads, such as Codex. Include every returned form field and send the file bytes under the returned file_field. Never send a local path to Uwear. Do not use this for ChatGPT attachments or existing public URLs. After every upload succeeds, call finish_local_garment_upload with the returned upload handles.
    ConnectorNo auth