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SQL over Infino

infino_sql
Destructive

Run analytical SQL with counts, GROUP BY, joins, aggregates, and column filters; use keyword, vector, or hybrid search functions within queries to retrieve ranked result rows.

Instructions

Use for structural or analytical questions — counts, GROUP BY, joins, aggregates, filtering by column value — returning result rows. The engine's search functions are callable as table-valued relations, so a single query can rank AND aggregate: bm25_search('table','text_col','terms', k) — also bm25_search_prefix / token_match / exact_match — need no embedding. vector_search('table','vec_col', {{q}}, k) and hybrid_search('table','text_col','terms','vec_col', {{q}}, k) need a query vector: put a {{name}} placeholder where the vector goes and pass embed:{"name":"query text"} — the server embeds the text and substitutes the vector in. Example: SELECT path, SUM(end_line - start_line + 1) AS lines FROM bm25_search('docs','body','error timeout', 300) GROUP BY path ORDER BY lines DESC. Any single statement is allowed, DDL/DML included.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
embedNoMap of placeholder name -> query text. Each text is embedded with the server's embedder and its vector is substituted for every {{name}} in the query — required to use vector_search / hybrid_search. E.g. {"q":"error timeout"} fills {{q}}.
queryYesA single SQL statement. May use search TVFs and {{name}} vector placeholders.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.14.0
    • changedInput schema / properties / query / description
      Previous value: -"A single read-only SELECT or WITH statement. May use search TVFs and {{name}} vector placeholders."New value: +"A single SQL statement. May use search TVFs and {{name}} vector placeholders."
  2. Changed2 schema fields changedv0.10.0
    • removedInput schema / additionalProperties
      Removed value: -false
    • addedInput schema / properties / embed / propertyNames
      Added value: +{
+  "type": "string"
+}
  3. First observedv0.7.0

TDQS

A4.3/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The annotation already marks the operation as destructive, and the description reinforces this by stating 'Any single statement is allowed, DDL/DML included.' It also discloses the server-side embedding behavior and placeholder substitution. It stops short of warning about potential destructive consequences or side effects, but the annotation covers the core safety signal.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long, but almost every sentence carries necessary information for correctly invoking this complex SQL tool. It is front-loaded with the purpose, then explains TVFs, embedding, gives an example, and closes with the important DDL/DML caveat. It could be tightened slightly, but it is structured and not padded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no output schema, the description adequately explains return value as 'result rows,' but the exact response shape for DDL/DML statements is left implicit. The coverage of query construction, embedding syntax, and search functions is strong and sufficient for an agent to call the tool correctly. Minor gaps remain around output format and error behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and both parameters are documented in the schema, so the baseline is 3. The description adds value by explaining the {{name}} placeholder mechanism, the embed:{"name":"query text"} pattern, and concrete search function signatures such as bm25_search and hybrid_search. The example further clarifies how to combine query and embed.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the tool is for structural or analytical questions and enumerates concrete operations: counts, GROUP BY, joins, aggregates, and column filtering. It also distinguishes this SQL gateway from the specialized sibling search/count tools by explaining that search functions are callable as table-valued relations within SQL.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The first sentence clearly scopes appropriate use cases, and the TVF explanation shows when this tool is uniquely useful: a single query can both rank and aggregate. It also gives concrete syntax for embedding vectors. However, it does not explicitly say when to prefer a sibling tool instead, such as infino_keyword_search for simple searches.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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