Server definition
- Hash
- sha256:44eb31b1a24a094af501af847fa53c54c5993823e5485129bae77aef5e858346
- What it is
- What a remote MCP server returned when asked what it offers: 2 tools
The blob, as servednamed by its sha256
{
"instructions": null,
"tools": [
{
"description": "Search documentation with hybrid semantic (vector) and keyword (BM25) search. Use semanticWeight to choose keyword-only (0), semantic-only (1), or a blend; mid values fuse rankings with RRF. Supports Tiger Cloud (TimescaleDB), PostgreSQL, and PostGIS.",
"inputSchema": {
"$schema": "http://json-schema.org/draft-07/schema#",
"properties": {
"limit": {
"anyOf": [
{
"maximum": 9007199254740991,
"minimum": -9007199254740991,
"type": "integer"
},
{
"type": "null"
}
],
"description": "The maximum number of matches to return. Defaults to 20."
},
"query": {
"description": "The search query. Used for BM25 when keyword or hybrid search applies, and for the embedding when semantic or hybrid search applies.",
"type": "string"
},
"semanticWeight": {
"anyOf": [
{
"maximum": 1,
"minimum": 0,
"multipleOf": 0.1,
"type": "number"
},
{
"type": "null"
}
],
"description": "Controls the balance between semantic and keyword search. 0 = keyword only, 0.5 = equal mix, 1 = semantic only. Default is 0.7 (favor semantic search)."
},
"source": {
"description": "The documentation source to search. \"tiger\" for Tiger Cloud and TimescaleDB, \"postgres\" for PostgreSQL, \"postgis\" for PostGIS spatial extension. Specific versions provided with _X.X suffixes.",
"enum": [
"tiger",
"postgres_14",
"postgres_15",
"postgres_16",
"postgres_17",
"postgres_18",
"postgis_3.3",
"postgis_3.4",
"postgis_3.5",
"postgis_3.6"
],
"type": "string"
}
},
"required": [
"source",
"query",
"limit",
"semanticWeight"
],
"type": "object"
},
"name": "search_docs",
"outputSchema": {
"$schema": "http://json-schema.org/draft-07/schema#",
"additionalProperties": false,
"properties": {
"results": {
"items": {
"anyOf": [
{
"additionalProperties": false,
"properties": {
"content": {
"description": "The content of the documentation entry.",
"type": "string"
},
"distance": {
"description": "The distance score indicating the relevance of the entry to the query. Lower values indicate higher relevance.",
"type": "number"
},
"id": {
"description": "The unique identifier of the documentation entry.",
"maximum": 9007199254740991,
"minimum": -9007199254740991,
"type": "integer"
},
"metadata": {
"description": "Additional metadata about the documentation entry, as a JSON encoded string.",
"type": "string"
}
},
"required": [
"id",
"content",
"metadata",
"distance"
],
"type": "object"
},
{
"additionalProperties": false,
"properties": {
"content": {
"description": "The content of the documentation entry.",
"type": "string"
},
"id": {
"description": "The unique identifier of the documentation entry.",
"maximum": 9007199254740991,
"minimum": -9007199254740991,
"type": "integer"
},
"metadata": {
"description": "Additional metadata about the documentation entry, as a JSON encoded string.",
"type": "string"
},
"score": {
"description": "The score indicating the relevance of the entry to the keywords. Higher values indicate higher relevance.",
"type": "number"
}
},
"required": [
"id",
"content",
"metadata",
"score"
],
"type": "object"
},
{
"additionalProperties": false,
"properties": {
"content": {
"description": "The content of the documentation entry.",
"type": "string"
},
"id": {
"description": "The unique identifier of the documentation entry.",
"maximum": 9007199254740991,
"minimum": -9007199254740991,
"type": "integer"
},
"metadata": {
"description": "Additional metadata about the documentation entry, as a JSON encoded string.",
"type": "string"
},
"rrf_score": {
"description": "Hybrid search: fused RRF score from combining semantic and keyword result rankings.",
"type": "number"
}
},
"required": [
"id",
"content",
"metadata",
"rrf_score"
],
"type": "object"
}
]
},
"type": "array"
}
},
"required": [
"results"
],
"type": "object"
}
},
{
"description": "Retrieve detailed skills for TimescaleDB operations and best practices.\n\n## Available Skills\n\n<available_skills>\n[10\t]{name\tdescription}:\n design-postgis-tables\tComprehensive PostGIS spatial table design reference covering geometry types, coordinate systems, spatial indexing, and performance patterns for location-based applications\n design-postgres-tables\t\"Use this skill for general PostgreSQL table design.\\n\\n**Trigger when user asks to:**\\n- Design PostgreSQL tables, schemas, or data models when creating new tables and when modifying existing ones.\\n- Choose data types, constraints, or indexes for PostgreSQL\\n- Create user tables, order tables, reference tables, or JSONB schemas\\n- Understand PostgreSQL best practices for normalization, constraints, or indexing\\n- Design update-heavy, upsert-heavy, or OLTP-style tables\\n\\n\\n**Keywords:** PostgreSQL schema, table design, data types, PRIMARY KEY, FOREIGN KEY, indexes, B-tree, GIN, JSONB, constraints, normalization, identity columns, partitioning, row-level security\\n\\nComprehensive reference covering data types, indexing strategies, constraints, JSONB patterns, partitioning, and PostgreSQL-specific best practices.\\n\"\n find-hypertable-candidates\t\"Use this skill to analyze an existing PostgreSQL database and identify which tables should be converted to Timescale/TimescaleDB hypertables.\\n\\n**Trigger when user asks to:**\\n- Analyze database tables for hypertable conversion potential\\n- Identify time-series or event tables in an existing schema\\n- Evaluate if a table would benefit from Timescale/TimescaleDB\\n- Audit PostgreSQL tables for migration to Timescale/TimescaleDB/TigerData\\n- Score or rank tables for hypertable candidacy\\n\\n\\n**Keywords:** hypertable candidate, table analysis, migration assessment, Timescale, TimescaleDB, time-series detection, insert-heavy tables, event logs, audit tables\\n\\nProvides SQL queries to analyze table statistics, index patterns, and query patterns. Includes scoring criteria (8+ points = good candidate) and pattern recognition for IoT, events, transactions, and sequential data.\\n\"\n migrate-postgres-tables-to-hypertables\t\"Use this skill to migrate identified PostgreSQL tables to Timescale/TimescaleDB hypertables with optimal configuration and validation.\\n\\n**Trigger when user asks to:**\\n- Migrate or convert PostgreSQL tables to hypertables\\n- Execute hypertable migration with minimal downtime\\n- Plan blue-green migration for large tables\\n- Validate hypertable migration success\\n- Configure compression after migration\\n\\n**Prerequisites:** Tables already identified as candidates (use find-hypertable-candidates first if needed)\\n\\n**Keywords:** migrate to hypertable, convert table, Timescale, TimescaleDB, blue-green migration, in-place conversion, create_hypertable, migration validation, compression setup\\n\\nStep-by-step migration planning including: partition column selection, chunk interval calculation, PK/constraint handling, migration execution (in-place vs blue-green), and performance validation queries.\\n\"\n pgvector-semantic-search\t\"Use this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search.\\n\\n**Trigger when user asks to:**\\n- Store or search vector embeddings in PostgreSQL\\n- Set up semantic search, similarity search, or nearest neighbor search\\n- Create HNSW or IVFFlat indexes for vectors\\n- Implement RAG (Retrieval Augmented Generation) with PostgreSQL\\n- Optimize pgvector performance, recall, or memory usage\\n- Use binary quantization for large vector datasets\\n\\n**Keywords:** pgvector, embeddings, semantic search, vector similarity, HNSW, IVFFlat, halfvec, cosine distance, nearest neighbor, RAG, LLM, AI search\\n\\nCovers: halfvec storage, HNSW index configuration (m, ef_construction, ef_search), quantization strategies, filtered search, bulk loading, and performance tuning.\\n\"\n postgres\t\"Use this skill for any PostgreSQL database work — table design, indexing, data types, constraints, extensions (pgvector, PostGIS, TimescaleDB), search, and migrations.\\n\\n**Trigger when user asks to:**\\n- Explore an existing PostgreSQL database to understand its objects and relationships\\n- Design or modify PostgreSQL tables, schemas, or data models\\n- Choose data types, constraints, indexes, or partitioning strategies\\n- Work with pgvector embeddings, semantic search, or RAG\\n- Set up full-text search, hybrid search, or BM25 ranking\\n- Use PostGIS for spatial/geographic data\\n- Set up TimescaleDB hypertables for time-series data\\n- Migrate tables to hypertables or evaluate migration candidates\\n- Plan or execute safe schema migrations with zero downtime\\n\\n**Keywords:** PostgreSQL, Postgres, SQL, schema, table design, indexes, constraints, pgvector, PostGIS, TimescaleDB, hypertable, semantic search, hybrid search, BM25, time-series, migration\\n\"\n postgres-database-migration\t\"Use this skill for planning, testing, and safely executing PostgreSQL schema migrations — especially when working with production data or shared databases.\\n\\n**Trigger when user asks to:**\\n- Test a schema migration before applying it to production\\n- Add, remove, or rename columns safely on a live table\\n- Change a column's data type without downtime\\n- Add or drop indexes, constraints, or foreign keys on large tables\\n- Understand which ALTER TABLE operations lock the table\\n- Roll back a failed migration\\n- Plan a zero-downtime migration strategy\\n- Fork a database to test a migration safely\\n\\n**Keywords:** migration, schema change, ALTER TABLE, add column, drop column, rename column, change type, zero downtime, lock, AccessExclusiveLock, concurrent index, forking, rollback, backfill, deploy\\n\\nCovers: lock-level reference for every common DDL operation, safe migration patterns, fork-based testing, zero-downtime column changes, index creation, constraint addition, backfill strategies, pre/post-migration validation, and rollback planning.\\n\"\n postgres-hybrid-text-search\t\"Use this skill to implement hybrid search combining BM25 keyword search with semantic vector search using Reciprocal Rank Fusion (RRF).\\n\\n**Trigger when user asks to:**\\n- Combine keyword and semantic search\\n- Implement hybrid search or multi-modal retrieval\\n- Use BM25/pg_textsearch with pgvector together\\n- Implement RRF (Reciprocal Rank Fusion) for search\\n- Build search that handles both exact terms and meaning\\n\\n\\n**Keywords:** hybrid search, BM25, pg_textsearch, RRF, reciprocal rank fusion, keyword search, full-text search, reranking, cross-encoder\\n\\nCovers: pg_textsearch BM25 index setup, parallel query patterns, client-side RRF fusion (Python/TypeScript), weighting strategies, and optional ML reranking.\\n\"\n schema-exploration\t\"Explore an existing PostgreSQL database before answering questions about its data or writing SQL. Use this skill whenever a user asks for a query or a data-backed answer against an unfamiliar schema (counts, missing or failed records, recent changes), asks where a business concept lives, or asks how tables, joins, views, routines, triggers, RLS, or extensions work. Find the relevant objects with read-only pg_catalog queries, then request approval before inspecting data-derived statistics or rows. Not a schema-design or migration guide.\\n\"\n setup-timescaledb-hypertables\t\"Use this skill when creating database schemas or tables for Timescale, TimescaleDB, TigerData, or Tiger Cloud, especially for time-series, IoT, metrics, events, or log data. Use this to improve the performance of any insert-heavy table.\\n\\n**Trigger when user asks to:**\\n- Create or design SQL schemas/tables AND Timescale/TimescaleDB/TigerData/Tiger Cloud is available\\n- Set up hypertables, compression, retention policies, or continuous aggregates\\n- Configure partition columns, segment_by, order_by, or chunk intervals\\n- Optimize time-series database performance or storage\\n- Create tables for sensors, metrics, telemetry, events, or transaction logs\\n\\n**Keywords:** CREATE TABLE, hypertable, Timescale, TimescaleDB, time-series, IoT, metrics, sensor data, compression policy, continuous aggregates, columnstore, retention policy, chunk interval, segment_by, order_by\\n\\nStep-by-step instructions for hypertable creation, column selection, compression policies, retention, continuous aggregates, and indexes.\\n\"\n</available_skills>",
"inputSchema": {
"$schema": "http://json-schema.org/draft-07/schema#",
"properties": {
"path": {
"description": "A relative path to a file or directory within the skill to view.\nIf empty, will view the `SKILL.md` file by default.\nUse `.` to list the root directory of the skill.",
"type": "string"
},
"skill_name": {
"description": "The name of the skill to browse, or `.` to list all available skills.",
"type": "string"
}
},
"required": [
"skill_name",
"path"
],
"type": "object"
},
"name": "view_skill",
"outputSchema": {
"$schema": "http://json-schema.org/draft-07/schema#",
"additionalProperties": false,
"properties": {
"content": {
"description": "The content of the file or directory listing.",
"type": "string"
}
},
"required": [
"content"
],
"type": "object"
}
}
]
}Verify it yourself
curl -s https://api.teppi.xyz/v1/evidence/sha256:44eb31b1a24a094af501af847fa53c54c5993823e5485129bae77aef5e858346 | sha256sum