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Keyword (BM25) search

infino_keyword_search
Read-onlyIdempotent

Search a table by literal keywords or phrases and get results ranked by BM25 relevance, including scores and selected columns. Ideal for identifiers, error codes, or exact phrases.

Instructions

Use when the query is literal terms — identifiers, error codes, product names, exact phrases — and you want results ranked by relevance. BM25 full-text search over a text column: ranks rows by how well the query's tokens (and their stems) match, each with a relevance score (higher is better) plus the columns you project ('columns'; the full text column by default). Matches exact tokens, not synonyms or paraphrases. Prefer this over SQL LIKE for known literal terms. For meaning-based search use infino_semantic_search; for both at once use infino_hybrid_search.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kNoMaximum results to return.
modeNoMatch any query token ('or', the default) or require every token ('and').
queryYesQuery terms, matched as literal tokens.
statsNoBM25 statistics scope: 'per_superfile' (the default; each segment scored against its own statistics) or 'global' (one table-wide idf, so a table written in many small batches ranks like one corpus).
tableYesTable to search.
columnNoText column to search; inferred from the table schema when omitted.
columnsNoWhich of the table's columns each hit returns, with full values (a projection passed straight to the engine). Defaults to the searched column; '_id' and 'score' are always included. Any column works: ['id'] for compact hits at a large k, ['id', 'text'] to get the full text alongside an id to cite, ['title', 'created_at'] for metadata. Nothing is truncated; read fewer columns or a smaller k to keep results small.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed3 schema fields changedv0.14.0
    • changedInput schema / properties / columns / description
      Previous value: -"Columns to return with each hit (e.g. an id, path, or line range to cite). Defaults to the searched column; '_id' and 'score' are always included."New value: +"Which of the table's columns each hit returns, with full values (a projection passed straight to the engine). Defaults to the searched column; '_id' and 'score' are always included. Any column works: ['id'] for compact hits at a large k, ['id', 'text'] to get the full text alongside an id to cite, ['title', 'created_at'] for metadata. Nothing is truncated; read fewer columns or a smaller k to keep results small."
    • addedInput schema / properties / mode
      Added value: +{
+  "description": "Match any query token ('or', the default) or require every token ('and').",
+  "enum": [
+    "or",
+    "and"
+  ],
+  "type": "string"
+}
    • addedInput schema / properties / stats
      Added value: +{
+  "description": "BM25 statistics scope: 'per_superfile' (the default; each segment scored against its own statistics) or 'global' (one table-wide idf, so a table written in many small batches ranks like one corpus).",
+  "enum": [
+    "per_superfile",
+    "global"
+  ],
+  "type": "string"
+}
  2. Changed1 schema field changedv0.10.0
    • removedInput schema / additionalProperties
      Removed value: -false
  3. First observedv0.7.0

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already mark the tool read-only, idempotent, and non-destructive, so the description adds valuable behavioral context beyond that: it matches exact tokens and stems, returns relevance scores where higher is better, and never truncates projected values. This helps the agent predict output size and semantics without overstepping the annotation safety profile.

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

Conciseness5/5

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

The description is front-loaded with the key use condition, then explains the ranking behavior, then routes to alternatives, and then details parameter nuances. Every sentence adds value, and the structure makes it easy for an agent to quickly decide whether to use this tool.

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

Completeness5/5

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

For a 7-parameter tool with no output schema, the description covers the essential return shape by stating that results include a relevance score and projected columns, with '_id' and 'score' always included. It also addresses result-size concerns through the 'k' parameter and the 'columns' projection guidance, making the tool fully usable without additional inference.

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%, so the baseline is 3, but the description adds meaningful guidance beyond the schema, especially for 'columns' with concrete examples like ['id'] for compact hits and ['id', 'text'] for citing, plus the caveat that nothing is truncated. It also clarifies the practical effect of the 'stats' scope, which the schema describes but does not motivate.

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 clearly states a specific verb and resource: 'BM25 full-text search over a text column' that ranks rows by token/stem relevance. It also distinguishes itself from siblings by noting it matches exact tokens, not synonyms or paraphrases, and explicitly contrasts with infino_semantic_search and infino_hybrid_search.

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

Usage Guidelines5/5

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

It opens with an explicit when-to-use condition: 'Use when the query is literal terms — identifiers, error codes, product names, exact phrases.' It also gives direct routing guidance: 'Prefer this over SQL LIKE for known literal terms' and names the exact alternatives for meaning-based and combined search.

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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