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457,785 tools. Updated 2026-08-14 16:00

"A tool for analyzing Python repository structure and code relationships" matching MCP tools:

  • Find working SOURCE CODE examples from 37 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) and READMEs — NOT build/data files. For sample data, use get_sample_data. Covers Python, Java, C#, Rust 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.
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  • Given per-component reliabilities and a structure ('series' or 'parallel'), return the system reliability. Series = product (all must work). Parallel = 1 − product(1−Rᵢ) (at least one works). Useful for back-of-envelope RBD calcs before reaching for full RBD tooling. For mixed-structure systems (series with parallel sub-blocks), call this tool repeatedly on the sub-blocks. ANTI-FABRICATION: exact closed-form. Quote verbatim.
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  • Check Python source without running it: parse, lint (ruff), type-check (mypy), AST security policy, credential scan. Safe on code you do not trust. Use it on every Python file you generated or edited, before writing it to disk. Alternatives: repair_python to get the corrected source instead of the diagnosis; execute_python to prove the code runs. Auth: a key is required. A free key covers this call, 25 per day, then HTTP 429; get one with POST /v1/keys. Credits are bought without an account, 1 per call: GET /v1/pricing says where to send the xDAI. Arguments: code: the whole file, 1..200000 bytes of UTF-8 measured after encoding (empty is refused with 400, larger with 413); a fragment is fine, but line and column numbers in the answer count from 1 in what you sent. language: must be 'python'; anything else is 400, and the field may be omitted. Of options only transpile_to (e.g. 'javascript', which returns a translated copy in transpiled) acts here; timeout_s, max_iterations, optimize, examples and expected_output need a pass that rewrites or runs the code, so send code alone. Ignored options are not refused, so a call that sets them looks like it worked; and code that does not parse is answered rather than refused: valid=false with the syntax error located, which is the point. Returns valid, score 0..1, diagnostics (rule, message, line, column), security findings, fixes, fixed_code and runtime; see outputSchema. The code and its verdict are retained to improve the service.
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  • This tool looks up a LOINC code in NLM Clinical Tables and returns guidance on where to obtain a LOINC → SNOMED CT mapping. It does not perform the mapping. Direct LOINC → SNOMED CT mappings are not freely available via API. UMLS Metathesaurus contains the relationships but requires an individual UMLS Terminology Services license; the LOINC SNOMED CT Expression Association is published by Regenstrief Institute as part of the LOINC release and requires authenticated download from loinc.org under the LOINC license. For programmatic LOINC → SNOMED mapping, use UMLS or the LOINC Expression Association files. For interactive lookup, use the SNOMED CT browser available to your organization or the Regenstrief RELMA desktop tool. Provide a LOINC code like "2339-0" (Glucose) or "718-7" (Hemoglobin).
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  • Get the actual Python code behind a community leaderboard strategy. Use after `browse_community`: pass an entry's `id` here to read its real `feature_engineering()` + `strategy_config()` source so the user can inspect or tweak it. To deploy it unchanged, pass the same id to `one_shot` as `community_id`. Read-only, no signup needed. Args: community_id: The `id` of a community entry (from `browse_community`). Returns: dict with: id, title, username, description, symbol, timeframe, metrics {total_ret, win_rate, profit_factor, n_trades, mdd, sharpe_strat}, and `code` (the full Python source). SHOW the code to the user, and offer to deploy it via one_shot(community_id=...) or tweak it first.
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  • Artist profile by MBID: type (person/group/…), country, life span, gender, area, aliases, tags/genres, plus the discography (release-groups) and band-membership / collaboration relationships and external links (Wikidata QID, Discogs, official site — surfaced as url-rels chainable to those servers). The 80% artist-detail call. Discography and relationships are capped at one page (25); for a prolific artist's complete release-group list, call musicbrainz_browse_entities with target_type=release-group and the artist link.
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  • Proves AI-generated Python does what you asked: lint, types, security, sandbox run, exact fixes.

  • Validates AI-generated Python: syntax, lint, security scan and deterministic repair.

  • Recording (a specific performance/track, distinct from the abstract work) by MBID: length, artist credits, ISRCs, the releases it appears on, the work(s) it performs (work-rels — chain to musicbrainz_get_work), and performance/production relationships (who played, produced, engineered, conducted — each with the role and the credited artist MBID). Relationships are capped at one page; for a heavily-covered recording call musicbrainz_browse_entities with target_type=recording and link.work.
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  • Deterministic structure/checksum validation of an EU VAT number (incl. the Belgian modulo-97 checksum) with the normalized identifier and a stable issue code. Format plausibility only — not a live VIES result. Validation and readiness only; never sends a Peppol invoice and gives no legal, fiscal or compliance guarantee.
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  • Return a self-contained stdlib Python client for scoring at ZERO per-call LLM tokens. Purpose: Hand the caller an HTTP consumer that runs locally so bulk scoring doesn't burn LLM tokens per book. Use when: You need to score more than ~200 books, or `kirk_score_book_batch` returned `batch_too_large`, or the caller is running an autonomous bulk workload that would otherwise pay per-tool-call LLM tokens for every book. Do not use when: You are running a one-off interactive call — a direct `kirk_score_book` invocation is simpler; don't route through the client for a single book. Capability class(es): Cost-steering / delivery-path tool. Hands the caller a runner that exercises the same C2 / C5 / C6 capabilities as the MCP scoring tools, but at zero per-call LLM token cost. Path fit: The returned client is an HTTP consumer of the same MCP endpoint. Production integrations run in-process under sealed-engine attestation — same binary sha as this endpoint. Contact Kavara for deployment options. Cost: 0 IU. Free tool. Once running locally, the returned client bills against the same tools it drives: single-book calls at 1 IU each, and batch calls at 1 IU per 50 books (minimum 1 IU per call). A full 500-book batch → 10 IU. No LLM tokens on top. Cost comparison (2.7M-book validation rerun via 500-book batches — ~5400 batches, 54000 IU billed either way): MCP via Sonnet 5: $1,968 LLM + $540 IU + ~15 days wall clock MCP via Haiku 4.5: $656 LLM + $540 IU + ~10 days Python client (this tool): $0 LLM + $540 IU + ~55 min Return structure: { "language": "python", "filename": "kirk_online_client.py", "requirements": str, "usage": str, "code": str (the client source, ~500 LOC), "example": str (2-line copy-paste demo) }
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  • Returns runnable code that creates a Solana keypair. Solentic cannot generate the keypair for you and never sees the private key — generation must happen wherever you run code (the agent process, a code-interpreter tool, a Python/Node sandbox, the user's shell). The response includes the snippet ready to execute. After running it, fund the resulting publicKey and call the `stake` tool with {walletAddress, secretKey, amountSol} to stake in one call.
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  • Render a mingrammer/diagrams Python snippet to PNG and return the image. The code must be a complete Python script using `from diagrams import ...` imports and a `with Diagram(...)` context manager block. Use search_nodes to verify node names and get correct import paths before writing code. Read the diagrams://reference/diagram, diagrams://reference/edge, and diagrams://reference/cluster resources for constructor options and usage examples. Args: code: Full Python code using the diagrams library. 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.
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  • Validate a single International Bank Account Number (IBAN) against the official ISO 13616 structure for its country. What it checks: the country code, total length for that country, the national BBAN structure, and the MOD-97 check digits. When the bank/branch code maps to a known institution, the response also includes the bank name, BIC/SWIFT code, and country. Returns JSON with fields such as `valid` (boolean), `countryCode`, `checkDigitsValid`, the `formatted` IBAN, and an optional `bank` object. On a malformed input the call still succeeds with `valid: false` and a `reason` (e.g. INVALID_FORMAT, INVALID_CHECKSUM); it does not throw for invalid IBANs. Use this when you have one account number to verify. For many IBANs prefer `validate_bulk_ibans`; to pull IBANs out of prose use `extract_ibans_from_text` first. No account data is stored; validation runs in memory and is discarded.
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  • Use when the user needs a canonical open-source example, usage pattern, or API snippet that is not tied to one already-known dependency/repository. Best for "how do I use X", cross-project patterns, up-to-date examples, or when package-scoped search was not enough. For inspecting a specific known package or repository, use `search`, `code_*`, or `docs_*` instead. Default output is markdown, with source repository provenance when available and a trailing `solution_id: ...` line when available. When presenting an example to a user, report the source repositories/citations from GitHits' generated references/provenance section whenever present; they are core evidence, not optional metadata. Pass `format: "json"` for `{result, solution_id?}`. Pass `solution_id` to `feedback` after using or rejecting the example. For searching indexed dependency and repository code/docs, use the unified `search` tool instead.
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  • Use when investigating a known package or repository and you need to discover relevant docs, source files, examples, tests, or APIs before reading exact files. Search indexed dependency and repository code, docs, and explicit symbols. Required: `query` plus either `target` or `targets`; pass `target` or `targets`, not both. Omit `source` to let GitHits select the best sources; set it only to restrict results to docs, code, or symbols. Structured parameters combine with the `query` using AND semantics. For `source:"docs"`, code/symbol-only filters (`category`, `kind`, `file_intent`, `public_only`) are ignored because docs search does not support them. Complete by default — if indexing is still running, the response carries a `searchRef` and no hits; pass it to `search_status` to follow up. Set `allow_partial_results: true` to opt into hits from sources that finished while others continue indexing. Each hit's `type` tells you the follow-up tool: `documentation_page` and `repository_doc` → `docs_read` with `locator.pageId`; `repository_code` and `repository_symbol` → `code_read` with `locator.filePath` (and `locator.startLine`/`endLine` when present).
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  • Get a thought's full connection graph. Requires npub for credit billing. ⚠️ NOT AUTHORITATIVE FOR RECENT CHANGES. Served through the vendor's cached graph layer (Azure App Service response cache), which lags writes by hours-to-days and reflects creates but NOT updates or deletes — it can return renamed/retyped thoughts with their old values and even serve thoughts that were already deleted (upstream: TheBrainTech/thebrain-api-quickstart-python#2). Use this for fast traversal of established structure and for finding older thought IDs. Do NOT use it to verify a recent write — confirm mutations by ID with get_thought, which reads the authoritative command store.
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  • Scan source code for injection vulnerabilities: SQL injection, command injection, path traversal via unsafe string concatenation/unsanitized input. Supports Python, JavaScript, TypeScript, Java, Go, Ruby, Shell, Bash. Use to detect input-handling bugs; for secrets use check_secrets. Companion code-security tools: check_secrets (hard-coded credential detection), check_dependencies (known-CVE vulnerability audit), check_headers (live HTTP security-header validation), scan_headers (live HTTP scan via domain). Free: 30/hr, Pro: 500/hr. Returns {total, by_severity, findings}. No data stored.
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  • Get Gonka Network signup link with referral welcome bonus (50M nGNK free tokens). Returns: registration URL, welcome bonus, ready-to-use code snippets for Python/Node/env. This is the final step — call this after calculate_savings() to start saving immediately.
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  • Fast deterministic preflight for tool-only clients. Call this before any other WORKS tool when eligibility is uncertain, especially for mutable or abbreviated refs, local or private repositories, and build, test, runtime, deployment, or production claims. It does not download a repository or persist data. If eligible is false, stop without calling verification.
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  • Get the data structure definition (DSD) for a dataflow: its dimensions and the valid codes for each, which you need to build a series key for get_series. Returns SDMX 2.1 structure XML. The DSD id differs from the dataflow id (e.g. dataflow BBEX3 uses DSD "BBK_ERX"). Pass the dataflow id (flowRef) and this tool resolves the DSD for you; the dimensions appear in <DimensionList> in key order. Set withCodes=true (default) to inline the codelists (references=children).
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  • Get pre-built template schemas for common use cases. ⭐ USE THIS FIRST when creating a new project! Templates show the CORRECT schema format with: proper FLAT structure (no 'fields' nesting), every field has a 'type' property, foreign key relationships configured correctly, best practices for field naming and types. Available templates: E-commerce (products, orders, customers), Team collaboration (projects, tasks, users), General purpose templates. You can use these templates directly with create_project or modify them for your needs. TIP: Study these templates to understand the correct schema format before creating custom schemas.
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