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retrieve_findings

Find relevant research findings by combining semantic and keyword matching, even when standard keyword search returns no results.

Instructions

Run intelligent retrieval combining semantic similarity and keyword matching.

AUTOMATIC TRIGGERS - Call this when:

  • Keyword search (search_fts) didn't find relevant results

  • Looking for findings related to a concept or topic

  • You need the most relevant findings for a research question

  • Broad exploration of what knowledge exists

WORKFLOW POSITION: Use AFTER search_fts returns insufficient results. This tool automatically combines semantic (meaning-based) and lexical (keyword) search.

CONFIRMATION TIER: READ OPERATION - No confirmation needed. Safe to call freely.

PARAMETERS:

  • query: Search query (required) - describe what you're looking for

  • project: Filter by project name (optional)

  • tags: Filter by tags (optional)

  • confidence_min: Minimum confidence 0.0-1.0 (optional) - filter low-confidence findings

  • final_k: Number of results to return (optional, default: 10)

ADVANCED: semantic_k and lexical_k control how many candidates are fetched before reranking. Usually not needed - use final_k instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNo
queryYes
final_kNo
projectNo
lexical_kNo
semantic_kNo
created_afterNo
confidence_minNo
created_beforeNo
Behavior4/5

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

With no annotations provided, the description carries the behavioral disclosure burden. It explicitly states this is a READ OPERATION, requires no confirmation, is safe to call freely, and automatically combines semantic and lexical search with reranking. This is valuable transparency, though it does not mention return format, pagination, or potential edge cases.

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 well structured with clear headings, bold labels, and front-loaded purpose. It is longer than minimal but each section earns its place by providing selection triggers, parameter semantics, and safety context. Minor redundancy exists around the read-operation and safe-to-call messaging.

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?

Given nine parameters, no annotations, and no output schema, the description does substantial work: it explains the retrieval mechanism, when to call it, parameter semantics, and confirmation status. It is missing return-value details and the date-range parameters, but overall it is sufficiently complete for an agent to select and invoke the tool correctly.

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 description coverage is 0%, so the description must compensate. It explains query, project, tags, confidence_min, final_k, and the advanced semantic_k/lexical_k parameters, including defaults and purpose. However, created_after and created_before are not described at all, leaving two parameters without semantic guidance.

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 opens with a specific verb and resource: 'Run intelligent retrieval combining semantic similarity and keyword matching.' It clearly distinguishes this tool from simple keyword search by highlighting the combined semantic and lexical mechanism, and the AUTOMATIC TRIGGERS section further clarifies its role.

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 description provides explicit when-to-use triggers and workflow position: use when keyword search returns insufficient results, for concept/topic exploration, or for a research question. It names search_fts as the alternative, though that exact sibling is not present in the provided sibling list, which introduces slight ambiguity; otherwise the usage guidance is strong.

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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