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Digital Experts Hypotheses

Search hypotheses

search_hypotheses

Full-text search over hypothesis titles, job stories, offers, creatives and audiences with filters. Returns compact rows (id, title, niche, year, cost per subscriber/lead, verdict, source_url). Use get_hypothesis(id) for the full card. Query in Russian works best (the base is in Russian).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
nicheNoNiche slug or name
queryNoWords to look for (Russian preferred), e.g. 'английский для детей'
verdictNo
year_fromNo
completenessNotested = launched with ads; wave = test-wave summary
max_cost_leadNoOnly rows with cost per lead ≤ this, USD

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden, and it does meaningfully disclose the return shape (compact rows listing id, title, niche, year, cost per subscriber/lead, verdict, source_url) rather than leaving it opaque. It does not cover auth, rate limits, or pagination behavior, which keeps it short of a 5.

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?

Three sentences, front-loaded with the core capability, then the return shape, then the sibling pointer and language tip. No filler; every clause earns its place.

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 7-parameter search tool with no annotations and no output schema, the description supplies the return-format detail and the alternative-tool routing that the structured fields cannot. The remaining gap is the undocumented subset of filter parameters, which slightly dents completeness.

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

Parameters3/5

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

Schema coverage is 57%, so roughly half the parameters are undocumented in the schema; the description compensates only partially by saying search covers titles, job stories, offers, creatives, and audiences and that filters exist. It adds the Russian-language hint (also present in the query schema description) but does not explain verdict, completeness, or cost semantics beyond what the schema already states.

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?

States a specific verb (full-text search) and resource (hypotheses), enumerates the searchable fields, and names the sibling get_hypothesis as the alternative for the full card. An agent can distinguish it from get_hypothesis, list_niches, and niche_stats immediately.

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?

Explicitly routes the agent: use search_hypotheses for compact filtered rows, use get_hypothesis(id) for the full card. Adds a usage tip that Russian queries perform best. Lacks explicit when-not conditions beyond the sibling pointer, but the context is clear.

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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