Skip to main content
Glama
213,351 tools. Last updated 2026-06-19 15:39

"Zig" matching MCP tools:

  • Run a read-only SQL query in the project and return the result. Prefer this tool over `execute_sql` if possible. This tool is restricted to only `SELECT` statements. `INSERT`, `UPDATE`, and `DELETE` statements and stored procedures aren't allowed. If the query doesn't include a `SELECT` statement, an error is returned. For information on creating queries, see the [GoogleSQL documentation](https://cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax). Example Queries: -- Count the number of penguins in each island. SELECT island, COUNT(*) AS population FROM bigquery-public-data.ml_datasets.penguins GROUP BY island -- Evaluate a bigquery ML Model. SELECT * FROM ML.EVALUATE(MODEL `my_dataset.my_model`) -- Evaluate BigQuery ML model on custom data SELECT * FROM ML.EVALUATE(MODEL `my_dataset.my_model`, (SELECT * FROM `my_dataset.my_table`)) -- Predict using BigQuery ML model: SELECT * FROM ML.PREDICT(MODEL `my_dataset.my_model`, (SELECT * FROM `my_dataset.my_table`)) -- Forecast data using AI.FORECAST SELECT * FROM AI.FORECAST(TABLE `project.dataset.my_table`, data_col => 'num_trips', timestamp_col => 'date', id_cols => ['usertype'], horizon => 30) Queries executed using the `execute_sql_readonly` tool will have the job label `goog-mcp-server: true` automatically set. Queries are charged to the project specified in the `project_id` field.
    Connector
  • Run a SQL query in the project and return the result. Prefer the `execute_sql_readonly` tool if possible. This tool can execute any query that bigquery supports including: * SQL Queries (SELECT, INSERT, UPDATE, DELETE, CREATE, etc.) * AI/ML functions like AI.FORECAST, ML.EVALUATE, ML.PREDICT * Any other query that bigquery supports. Example Queries: -- Insert data into a table. INSERT INTO `my_project.my_dataset`.my_table (name, age) VALUES ('Alice', 30); -- Create a table. CREATE TABLE `my_project.my_dataset`.my_table ( name STRING, age INT64); -- DELETE data from a table. DELETE FROM `my_project.my_dataset`.my_table WHERE name = 'Alice'; -- Create Dataset CREATE SCHEMA `my_project.my_dataset` OPTIONS (location = 'US'); -- Drop table DROP TABLE `my_project.my_dataset`.my_table; -- Drop dataset DROP SCHEMA `my_project.my_dataset`; -- Create Model CREATE OR REPLACE MODEL `my_project.my_dataset.my_model` OPTIONS ( model_type = 'LINEAR_REG' LS_INIT_LEARN_RATE=0.15, L1_REG=1, MAX_ITERATIONS=5, DATA_SPLIT_METHOD='SEQ', DATA_SPLIT_EVAL_FRACTION=0.3, DATA_SPLIT_COL='timestamp') AS SELECT col1, col2, timestamp, label FROM `my_project.my_dataset.my_table`; Queries executed using the `execute_sql` tool will have the job label `goog-mcp-server: true` automatically set. Queries are charged to the project specified in the `project_id` field.
    Connector
  • Estimate token count for arbitrary content via the Zig WASM engine. Sub-millisecond, zero allocations. Useful for context-budget planning.
    Connector
  • Validate .faf YAML content via the Mk4 Zig-WASM engine. Returns true if mission-ready (>= 100).
    Connector

Matching MCP Servers

  • F
    license
    -
    quality
    B
    maintenance
    MCP server for Zig that connects AI coding assistants to ZLS (Zig Language Server) via LSP. Provides 16 tools for code intelligence (hover, go-to-definition, references, completions, diagnostics, rename, format) and build/test operations.
    Last updated
    6
  • A
    license
    A
    quality
    C
    maintenance
    Provides Zig language intelligence for Claude Code by wrapping ZLS (Zig Language Server) and exposing 8 tools for diagnostics, formatting, hover info, go-to-definition, references, completions, document symbols, and building.
    Last updated
    8
    2
    2
    MIT

Matching MCP Connectors

  • Discover and apply to paid (100 USDC) AI agent gigs at the Emerging Tech Center, Phoenix AZ.

  • The BigQuery remote MCP server is a fully managed service that uses the Model Context Protocol to connect AI applications and LLMs to BigQuery data sources. It provides secure, standardized tools for AI agents to list datasets and tables, retrieve schemas, generate and execute SQL queries through natural language, and analyze data—enabling direct access to enterprise analytics data without requiring manual SQL coding.

  • Purchases airtime by converting currency to minutes and seconds. Confirm (yes/no) before executing Purchases airtime by converting currency to minutes and seconds. @param api_key: The api key allocated to your application @param token: The wallet_api_token provided by /access/login @param account_fk: The account_fk (this is a database ID (int) not an account address (str) and may not be 0) The account_fk from which to deduct the amount @param amount: The amount in currency, 2 decimals @param currency_code: The currency code, e.g USD, ZAR, BWP, ZIG @return: a json object
    Connector
  • Re-ground on .faf content — re-score via the Mk4 Zig-WASM Enterprise scorer (33-slot, honors the authored app-type shape), report drift vs an optional baseline score, and return a stamped re-ground. The explicit re-grounding primitive for long sessions: drift → refresh → re-grounded. Built for Grok, by request.
    Connector
  • Score .faf YAML content via the Mk4 Zig-WASM engine. Returns 0-100 (capped). Same engine as xai-faf-rust + xai-faf-zig (parity-tested). Sub-ms at the edge.
    Connector
  • List the live services on the ETC public Service Catalog — productized services sold under the ETC brand on behalf of the venture that delivers. Each service carries six required attributes: name, scope, price, delivery owner, quality bar, capacity.
    Connector
  • Re-ground on .faf content — re-score via the Mk4 Zig-WASM Enterprise scorer (33-slot, honors the authored app-type shape), report drift vs an optional baseline score, and return a stamped re-ground. The explicit re-grounding primitive for long sessions: drift → refresh → re-grounded. Built for Grok, by request.
    Connector