Search the dataset
dataset_searchRows of the Reconzy dataset whose cells contain the query (case-insensitive), up to 50.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes | text to look for in any cell |
dataset_searchRows of the Reconzy dataset whose cells contain the query (case-insensitive), up to 50.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes | text to look for in any cell |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses case-insensitivity and a limit of 50 results, which is useful. However, it omits behavioral details such as read-only nature, handling of empty results, ordering, or pagination behavior. The disclosure is partial but not misleading.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence that is front-loaded with the key action and resource. Every word contributes meaning, with no fluff or repetition. The structure is efficient and immediately clear.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and only 2 parameters, the description is too sparse. It fails to mention whether the tool is read-only, what happens when no rows match, the default limit if not specified, or the structure of the returned rows. For a search tool, this leaves the agent uncertain about critical operational details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 50% (query is described, limit is not). The tool description adds meaning to the limit parameter via 'up to 50', which clarifies its purpose. It does not explain the default limit when omitted, nor does it add anything beyond the schema for query. This partially compensates for the missing schema description but leaves gaps.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb (search) and resource (Reconzy dataset rows), and specifies the matching criterion (cells contain the query) and case-insensitivity. It clearly distinguishes from sibling tools like dataset_columns, dataset_stats, etc., which serve different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly implies when to use this tool: when you need to find rows matching a text query across any cell. It does not explicitly mention alternatives, but the purpose is so distinct from siblings that the agent can infer correct usage. No exclusions are stated, 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.
Add one secure layer between your agents and this server.
Tools are mostly distinct, but dataset_row, dataset_search, and dataset_compare have overlapping row-filtering purposes. Descriptions clarify exact match vs. contains vs. any-of, so confusion is unlikely but possible.
All tools share the dataset_ prefix, but the suffix pattern is inconsistent: some are nouns (columns, provenance, row, stats, top) and some are verbs (compare, search). A more uniform verb_noun or noun_only convention would improve predictability.
Seven tools is a well-scoped set for dataset exploration, covering schema, provenance, lookup, search, comparison, statistics, and top-values queries without redundancy or bloat.
The toolset covers the main dataset exploration operations well, but it lacks explicit distinct-value or grouped-aggregation tools, which could be useful for fuller ad-hoc analysis. It is not severely incomplete.