"Rust" matching MCP tools:

• grep_docsA

  RedM McpA

  Find an EXACT literal token in raw doc files (markdown + lua). Use for specific weapon/ped/animation/prop/interior/zone names (`weapon_pistol_volcanic`, `a_c_bear_01`, `p_campfire01x`), known hashes (`0x020D13FF`), walkstyles/clipsets (`MP_Style_Casual`, `mech_loco_m@`), or any string you'd `grep` for. NOT for behavior/concept queries (use `semantic_search`) or script-native hash/name lookup (use `lookup_native`). REQUIRED for tokens inside the largest rdr3_discoveries data tables (audio_banks, ingameanims_list, cloth_drawable, cloth_hash_names, object_list, megadictanims, entity_extensions, imaps_with_coords, propsets_list, vehicle_bones) — only preview-indexed for embeddings, so `semantic_search` will NOT find tokens in them. Optional: `contextBefore`/`contextAfter` for ±N surrounding lines (saves a follow-up `get_document` call); `filesOnly: true` to get paths only (cheap exploration); `multiline: true` for cross-line patterns (`(?s)foo.*bar`). Pattern uses Rust regex syntax (rg engine). PREFER one targeted call over giant `a|b|c|d|e` alternations — split into separate calls; alternations rarely improve recall and bloat the regex automaton. Returns matched lines with path + line number. Long matched lines are windowed ±60 chars around the match (…); to read around a hit, use `read_lines({path, start})` for the preview-only mega-tables listed above (get_document holds only their ~80-line head), or `get_document({path})` for ordinary docs. If you are retrying after a previous pattern returned no matches, populate `prior_attempt` so the server can record what didn't work and steer alternative spellings.

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• purl_auditA

  ZEN SecDBA

  Perform a software package vulnerability audit using SecDB. ## What this tool does Analyzes a list of software packages identified by PURL (Package URL) and returns vulnerability information plus a Markdown summary. The audit results are based exclusively on the package list provided. ## When to use this tool Use this tool when the user wants to determine: - whether application dependencies contain known vulnerabilities - whether a project is affected by security advisories - which packages require patching or upgrading ## Supported ecosystems - **npm** - Node.js packages (e.g. pkg:npm/lodash@4.17.21) - **maven** - Java/JVM packages (e.g. pkg:maven/org.apache.logging.log4j/log4j-core@2.14.1) - **pypi** - Python packages (e.g. pkg:pypi/django@4.2.0) - **gem** - Ruby gems (e.g. pkg:gem/rails@7.0.0) - **cargo** - Rust crates (e.g. pkg:cargo/openssl-src@111.10) - **nuget** - .NET packages (e.g. pkg:nuget/Newtonsoft.Json@13.0.1) - **golang** - Go modules (e.g. pkg:golang/github.com/gin-gonic/gin@1.9.1) - **composer** - PHP packages (e.g. pkg:composer/symfony/symfony@6.4.0) ## Inputs - **purls**: list of Package URLs, one per entry. Generate them from your project manifest files: - Node.js: package.json / package-lock.json - Python: requirements.txt / Pipfile.lock / pyproject.toml - Ruby: Gemfile.lock - Go: go.mod / go.sum - Rust: Cargo.lock - PHP: composer.lock - Java: pom.xml / build.gradle - .NET: *.csproj / packages.lock.json ## Outputs - **report**: structured JSON objects describing the advisories affecting the audited packages. - **summary**: Markdown summary including total vulnerabilities, severity breakdown, and key findings. ## LLM usage guidelines - Never guess whether a package is vulnerable — always call this tool. - Only submit PURLs from the supported ecosystems listed above; others will be ignored. - The `summary` is already Markdown and can be shown directly. - Use `report` when deeper technical analysis is required.

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• get_project_contextA

  ZephexA

  Answer what the user's project is — name, stack, how to run/test/build, auth, database, deploy, folder layout — from their files on disk, not from training data. ALWAYS call this before you invent npm/pip/cargo commands or read package.json yourself. ALWAYS call when the user says: what is this app, what's the stack, how do I run it, how do I test, is this a monorepo, where is auth, what database, how do we deploy. If they named Zephex or MCP, call this first on their project. One topic per call. Start with topic=identity on a new folder, then follow next_calls (usually run or framework). Other topics: backend, frontend, database, auth, deploy, structure, integrations, security. This is the user's machine, any project: Node, Python, Go, Rust, Java, PHP, a monorepo, an unsaved folder. Local/stdio: omit path to use the editor cwd, or pass path as their project folder. No disk on this transport: inline_files with package.json or pyproject.toml/go.mod/Cargo.toml plus 2–4 source files. Returns topic, summary, data (identity, commands, key_paths), hint, next_calls. Copy dev/test/build from data — do not guess bun vs npm vs uv. Not for finding a function name (find_code) or reading a file body (read_code). Those come after you know what the project is. Example: get_project_context({ topic: "identity" }) then get_project_context({ topic: "run" }). Also call topic=auth before touching login, topic=database before schema work, topic=structure when you need the folder map. force:true if the project just changed. Brief is enough for orientation; do not skip this tool to save a round-trip — one identity call replaces reading several manifests.

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• grep_docsA

  redm-mcpA

  Find an EXACT literal token in raw doc files (markdown + lua). Use for specific weapon/ped/animation/prop/interior/zone names (`weapon_pistol_volcanic`, `a_c_bear_01`, `p_campfire01x`), known hashes (`0x020D13FF`), walkstyles/clipsets (`MP_Style_Casual`, `mech_loco_m@`), or any string you'd `grep` for. NOT for behavior/concept queries (use `semantic_search`) or script-native hash/name lookup (use `lookup_native`). REQUIRED for tokens inside the largest rdr3_discoveries data tables (audio_banks, ingameanims_list, cloth_drawable, cloth_hash_names, object_list, megadictanims, entity_extensions, imaps_with_coords, propsets_list, vehicle_bones) — only preview-indexed for embeddings, so `semantic_search` will NOT find tokens in them. Optional: `contextBefore`/`contextAfter` for ±N surrounding lines (saves a follow-up `get_document` call); `filesOnly: true` to get paths only (cheap exploration); `multiline: true` for cross-line patterns (`(?s)foo.*bar`). Pattern uses Rust regex syntax (rg engine). PREFER one targeted call over giant `a|b|c|d|e` alternations — split into separate calls; alternations rarely improve recall and bloat the regex automaton. Returns matched lines with path + line number. Long matched lines are windowed ±60 chars around the match (…); to read around a hit, use `read_lines({path, start})` for the preview-only mega-tables listed above (get_document holds only their ~80-line head), or `get_document({path})` for ordinary docs. If you are retrying after a previous pattern returned no matches, populate `prior_attempt` so the server can record what didn't work and steer alternative spellings.

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• deep_researchA

  Wikimedia RestA

  ACCOUNT REQUIRED (free — sign in via GitHub at https://pipeworx.io/signup; depth:"thorough" needs a paid plan). If you are not signed in, use ask_pipeworx instead — it works on every tier. Grounded multi-source research across Pipeworx's 1517 STRUCTURED data sources (SEC filings, FRED/BLS economics, FDA, USPTO patents, markets, science, government records, etc.) in ONE call — this is NOT open-web search. Decomposes your question into focused facets, routes each to the right one of 5,798 tools IN PARALLEL, and returns a findings packet: verbatim evidence + confidence + source + fetched_at + a stable pipeworx:// citation per finding, with explicit gaps[] for facets the data couldn't answer (never invented). Best for broad/multi-part questions over structured data ("compare X and Y's regulatory + financial exposure", "research the filings + market picture for ACME"). For a single lookup use ask_pipeworx (one LLM call, not many). For BREAKING or colloquial CURRENT-NEWS / "what's the world saying about X" topics, prefer ask_pipeworx — it routes to live news APIs and the *-news-feeds packs; deep_research returns mostly empty gaps[] when the topic isn't in the structured catalog. Second-hop iteration: depth:"standard" re-angles unanswered gaps (gap recovery); depth:"thorough" additionally chases the best leads from the first pass — so multi-step questions resolve in one call. Every finding carries a `hop` field and a citation_uri — a resolvable pipeworx:// record URI, present only when the source emits one that resources/read can actually serve, so a citation you get back is always fetchable. "standard" and "thorough" also return contradictions[] flagging findings that disagree. Large records are semantically excerpted to the passages relevant to each facet (not head-truncated), so answers deep in a long filing/series aren't missed. Expect 15-60s (thorough with its follow-up + contradiction pass: up to ~90s).

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• pipeworx_feedbackA

  Wikimedia RestA

  Tell the Pipeworx team something is broken, missing, or needs to exist. Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise). ONLY for tools served by this Pipeworx connection — if the tool came from a different MCP server in your client (another vendor's Gmail, Splunk, Slack, etc. connector), we cannot fix it and reporting it here only delays you; file it with that server instead. Not sure? Pipeworx tool names are the ones this connection lists. Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt. Filing without an account returns a `claim_token`; pass it back later as pipeworx_feedback({claim_token:"pwfb_…"}) to read whether it was fixed and what changed. The team reads digests daily and signal directly affects roadmap. Rate-limited to 5 per identifier per day. Free; doesn't count against your tool-call quota.

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• get_site_filesA

  Just PublishA

  Read the files of a site you already published, so you can make a targeted edit instead of rebuilding the whole site from memory. Returns a complete manifest (every file's path, size, content-type, sha256) plus the contents of the text files (HTML/CSS/JS/etc). Also returns the site's current `version` — pass it back to update_site_file so you don't overwrite a newer change. Pass `paths` to fetch only specific files; omit it to get all text files. Requires site_id + edit_token.

  ConnectorNo auth

• update_site_fileA

  Just PublishA

  Change one or a few files of an already-published site, leaving every other file untouched (a merge — unlike deploy, which replaces the whole site). Ideal for small edits: fix a typo in index.html, swap a stylesheet, add one page. Best practice: call get_site_files first, edit the returned content, then call this with the files you changed and the `expected_version` from that read — if the site changed in the meantime you get a clear conflict telling you to re-read. Requires site_id + edit_token. Cannot delete files (use deploy to drop a file) and cannot remove index.html.

  ConnectorNo auth

• get_advisoryA

  dependency-trustA

  Get a security advisory (vulnerability) by its key. Returns a security advisory by key, for example a GHSA id taken from a version's advisoryKeys, including the title, CVE aliases, CVSS v3 score and vector, and a link to the full record on osv.dev. Use this only when you already have an advisory ID from get_package_version's advisoryKeys. There is no search here. To find out whether a version has vulnerabilities at all, call get_package_version first; this tool explains one advisory in depth.

  ConnectorNo auth

• search_peopleA

  noticedB

  Search people by free text — name, company, keywords. Terms are OR-matched and ranked by how many match (more terms broadens, not narrows). To narrow: put AND between terms to require all (e.g. 'health AND medtech'), or prefix a term with + to require just it (e.g. '+rust berlin'). For roles/functions (founder, engineer, investor, …) use the `role` filter instead of free text — it catches title variants ('Founding Partner') that keywords miss, and free text over-matches bios/notes. To count people of a type, use structured filters and read `total` from the response — a free-text `total` counts keyword matches, not people of that type. scope:'own' (default) / scope:'public' (beyond your network + warm-intro paths). Optional company, location, skills, tags filters. A structured-filter zero = thin data, not absence — fall back to free text. Misspelled names/companies fall back to fuzzy matching (`fuzzy: true` = closest matches — confirm before trusting); concept queries with zero literal hits fall back to embedding similarity (`semantic: true` = related people, not literal matches). Free-text responses also return `strong_total` (rows matching ALL terms — the honest count) and per-row `matched_on` (which fields matched). In scope:'public', role/location/company/skills are applied to the global hits (`filtered: true`); filters that cannot apply there are listed in `unsupported_filters`.

  ConnectorNo auth

• cross_checkA

  Plumbline — a trust layer for LLM answersA

  Corroborate a figure you obtained (e.g. from run_sql) against the verified fact store: it re-derives the named value_key from the same SQL the verified pipeline uses and compares. AGREE lifts an otherwise-unverified session to 'corroborated'; DISAGREE marks a CONFLICT (worse than unverified). This is the only way a raw figure earns trust back, and it can only ever agree with a number the fact store itself computed. Resolvable keys cover the single-snapshot fact concepts plus temporal and per-department growth keys (e.g. indexed_people.<dept>); per-question keys (a named department's median, one person's direct-report count) return unmapped.

  ConnectorNo auth

• find_payroll_anomaliesA

  Plumbline — a trust layer for LLM answersA

  Surface payroll and deduction anomalies in the latest snapshot. NOTE: internal drafting is disabled on this deployment. If your client supports MCP sampling, this tool asks YOUR model to draft in the same call (verified server-side); otherwise it returns an explicit refusal, and you should use ask_prepare then ask_submit_draft to draft with your own model.

  ConnectorNo auth

• get_evidence_documentA

  Plumbline — a trust layer for LLM answersA

  Show ONE retrieved evidence document behind an answer you already received, addressed by that answer's correlation_id plus a document_id from its evidence_documents references. Returns the full stored document (title, body, metadata, embedding_text) with the retrieval rank and scores the answer recorded; never the raw embedding vector. Only documents the addressed answer actually recorded resolve: there is no fetch-by-id in general and no way to browse the store. Requires the persisted compliance log and the same session that produced the answer. Absent from the no-auth public demo.

  ConnectorNo auth

• find_examplesA

  SenzingA

  Find working SOURCE CODE examples from 42 indexed Senzing GitHub repositories. REQUIRED: either `query` (string, for search) or `repo` with `file_path` or `list_files=true` — the call WILL FAIL without one. Three modes: (1) Search: pass `query` to find examples across all repos, (2) File listing: pass `repo` + `list_files=true`, (3) File retrieval: pass `repo` + `file_path`. Indexes source code (.py, .java, .cs, .rs, .ts, .js) and READMEs — NOT build/data files. For sample data, use get_sample_data. Covers Python, Java, C# (official SDKs) plus Rust and TypeScript/Node.js (community-maintained wrappers, not official) SDK patterns: initialization, ingestion, search, redo, configuration, message queues, REST APIs. Use max_lines to limit large files. Returns GitHub raw URLs for file retrieval.

  ConnectorNo auth

• get_sdk_referenceA

  SenzingA

  Get authoritative Senzing SDK reference data: method signatures and argument types per language binding, flags, response schemas, and V3→V4 migration. Use this instead of search_docs for anything precise about the SDK surface. Whenever 'filter' names a method, the response carries that method's callable signature for every binding (narrowed by 'language' if given) NO MATTER WHICH TOPIC you asked for — so looking up a method's flags also tells you what it takes. Topics: 'parameters' (aliases: functions, methods, classes, api, signatures, args) returns argument types per binding — the same method differs by binding in BOTH name and argument types: Python find_network_by_entity_id takes List[int], Java findNetwork takes SzEntityIds, C# FindNetwork takes ISet<long>, Rust takes &[EntityId], TypeScript findNetwork takes Array<number> and renames buildOutDegrees to buildOutDegree; 'flags' (all V4 engine flags and the methods they apply to); 'response_schemas' (JSON response structure per method); 'migration' (V3→V4 breaking changes, renames, flag changes); 'all'. 'filter' accepts any spelling — 'get entity', 'get_entity', and 'getEntity' all resolve. Pass 'language' (python/java/csharp/rust/typescript) to narrow to your binding; cross-binding divergence warnings are still included so you never translate a call between bindings by mistake

  ConnectorNo auth

• mission_statusA

  Mission — Trust Graduation GateB

  REQUIRED FIRST CALL. Returns Mission gate status, version, action classes, and the agent_instructions you MUST follow for the rest of this session: gate every consequential action through request_approval and never proceed without mission_check_approval returning decision=approved. Call this once at the start of every conversation.

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• recommend_mcpA

  SaSame MCP Observatory + Gold Rush TownB

  Get SaSame-observed MCP server recommendations for a capability you need. SaSame, operated by SASAME S.R.L., continuously observes and measures the Model Context Protocol ecosystem and publishes verifiable evidence and history; the MCP Factory is internal machinery and an optional product surface behind it; measurement only, not endorsement. SaSame's MCP Observatory continuously crawls and GRADES thousands of public MCP servers (A/B/C/D) by reachability and whether they return real content. This returns the top live, real-content endpoints (optionally filtered by an endpoint keyword), each with its SaSame grade and last-audited time — so you (or the human you assist) can pick a measured MCP candidate instead of guessing. Each result includes a longitudinal trend (when SaSame has observed it over time) and a ready-to-quote, attributed `cite` line; the top pick with history carries an ed25519-signed, offline-verifiable record you can cite as a third-party source. Free, read-only, no signup. If you operate one of these servers, claim it (claim_start). If you can't find a fit and need an MCP/agent BUILT, call engage_sasame. Pass a referral handle from `refer` as engage_sasame(ref=...) to attribute the introduction.

  ConnectorNo auth

• ask_prepareA

  Plumbline — a trust layer for LLM answersA

  BYO step 1 of 2: run the server-side trust plane for a business question (plan, SQL-verified facts, evidence retrieval, answerability gate, prompt composition) and return the composed prompt plus a single-use prepare_key (a signed handoff token; echo it back VERBATIM and in full, or use the short ask_id alias; in prose always say the ask_id, never the token). Read-only: prepare holds no server state and is safe to run without confirmation — the receipt is minted only at ask_submit_draft. Pass the user's question VERBATIM: do not expand, narrow, or reword it — the server plans coverage itself, and an unrequested rewrite misleads the user about what was asked. If you must revise it (e.g. to fold in essential conversation context), you MUST also pass the user's exact wording in original_question so the panel can disclose the revision. YOU (the connected model) then write the draft answer from that prompt and submit it with ask_submit_draft — generation happens on your side, so no LLM credential ever reaches this server. An unanswerable question still receives a prompt for reference; its draft is force-refused at submit and can never earn a verified verdict. Only a policy refusal stops at prepare. Recommended drafting path on this deployment.

  ConnectorNo auth

• create_projectA

  RationalBloksA

  Create a new RationalBloks project from a JSON schema. ⚠️ CRITICAL RULES - READ BEFORE CREATING SCHEMA: 1. FLAT FORMAT (REQUIRED): ✅ CORRECT: {users: {email: {type: "string", max_length: 255}}} ❌ WRONG: {users: {fields: {email: {type: "string"}}}} DO NOT nest under 'fields' key! 2. FIELD TYPE REQUIREMENTS: • string: MUST have "max_length" (e.g., max_length: 255) • decimal: MUST have "precision" and "scale" (e.g., precision: 10, scale: 2) • datetime: Use "datetime" NOT "timestamp" • ALL fields: MUST have "type" property 3. AUTOMATIC FIELDS (DON'T define): • id (uuid, primary key) • created_at (datetime) • updated_at (datetime) 4. USER AUTHENTICATION: ❌ NEVER create "users", "customers", "employees" tables with email/password ✅ USE built-in app_users table Example: { "employee_profiles": { "user_id": {type: "uuid", foreign_key: "app_users.id", required: true}, "department": {type: "string", max_length: 100} } } 5. AUTHORIZATION: Add user_id → app_users.id to enable "only see your own data" Example: { "orders": { "user_id": {type: "uuid", foreign_key: "app_users.id"}, "total": {type: "decimal", precision: 10, scale: 2} } } 6. FIELD OPTIONS: • required: true/false • unique: true/false • default: any value • enum: ["val1", "val2"] • foreign_key: "table.id" AVAILABLE TYPES: string, text, integer, decimal, boolean, uuid, date, datetime, json, uuid_array, integer_array, text_array, float_array Array types store PostgreSQL native arrays with automatic GIN indexing: • uuid_array: UUID[] — for sets of references (e.g., tensor coordinates) • integer_array: BIGINT[] — for dimension indices, integer sets • text_array: TEXT[] — for tags, categories, label sets • float_array: DOUBLE PRECISION[] — for weight vectors, scores GIN-indexed operators: @> (contains), <@ (contained_by), && (overlaps) BACKEND ENGINE: • python (default): FastAPI backend — mature, full-featured • rust: Axum backend — faster cold starts, lower memory, high performance WORKFLOW: 1. Use get_template_schemas FIRST to see valid examples 2. Create schema following ALL rules above 3. Call this tool (optionally choose backend_type: "python" or "rust") 4. Monitor with get_job_status (2-5 min deployment) After creation, use get_job_status with returned job_id to monitor deployment.

  ConnectorNo auth

• ask_pipeworx_groundedA

  Wikimedia RestA

  Hallucination-resistant answer mode for high-stakes reads. Same routing as ask_pipeworx — picks the right tool from 5,798 across 1517 sources, fills arguments, fetches the data — then EXTRACTS the answer using ONLY what the tool result contains. Returns {answer, evidence (verbatim quote), confidence, source, fetched_at, refusal_reason:null} on success, OR an explicit refusal {answer:null, refusal_reason:"not_in_source"|"no_tool_match"|"tool_error"|"data_truncated"|"llm_error"} when the data doesn't directly answer. Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts (financial verdicts, legal claims, medical lookups, public statements). Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.

  ConnectorNo auth

• score_wallet_for_intelA

  TWZRD Agent IntelligenceA

  Free discovery: real 0-100 intel score for a wallet from its observed x402 payment history across the broader x402 ecosystem (paid calls, distinct counterparties, volume, recency). A Base 0x wallet is scored from the relayer-attributed x402_base_daily rollup (90d) with the same shape; no Base wash graph exists yet, so its wash_flag is "unknown" unless single-counterparty. Uses a simple transparent heuristic (volume log + breadth + spend log + recency decay) — the exact formula is returned inline as `score_model`. Returns intel_score, the wash-discounted effective_score + wash_flag/wash_factor (cheap Sybil signal), counts, component breakdown, and a data_available flag. Malformed pubkeys are rejected cleanly; the failure path returns the same shape as success. Sourced from the cross-facilitator corpus via the public Rust HTTP endpoint GET /v1/agents/{wallet}/x402 (backed by the x402_solana_payer_agg matview). For the full corpus-breadth renormalized model (intel_renorm_v1_1: score_raw, confidence, breadth_factor, wash_factor) + signed portable v6 receipt, pay for the HTTP surface: GET /v1/intel/trust/{wallet} (0.05 USDC).

  ConnectorNo auth