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Search the dataset

dataset_search

Rows of the RollCallWorks dataset whose cells contain the query (case-insensitive), up to 50.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
queryYestext to look for in any cell

Schema Changelog

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

  1. First observed

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description must carry the full behavioral burden. It does disclose case-insensitivity and a result limit ('up to 50'), which are useful. However, it does not state whether this is a read-only operation (though search implies it), how results are ordered, what happens when no rows match, or whether pagination is possible. Given the simplicity of the tool, this is adequate but not comprehensive.

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 a single, efficient sentence that front-loads the core action (rows whose cells contain the query) and adds the key constraint (up to 50). No wasted words, and it is immediately clear what the tool does.

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

Completeness3/5

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

The description covers the tool's purpose and basic behavior but lacks details about the output format. With no output schema, the agent does not know whether the tool returns full row objects, IDs, or some projection. It also does not mention ordering or error handling. For a simple search tool, this may be sufficient, but more detail on the return shape would improve 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 description coverage is 50%: the 'query' parameter is described ('text to look for in any cell'), but 'limit' has no schema description. The description's 'up to 50' indirectly hints at the limit parameter, but does not explicitly state that the 'limit' parameter controls it. Since the schema already has a maximum of 50, the description adds minimal value beyond confirming the cap. It does not explain default behavior or the meaning of 'limit' beyond its schema constraints.

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 the verb (search), the resource (RollCallWorks dataset), and the specific condition (cells contain the query, case-insensitive). It distinguishes itself from siblings like dataset_row (likely fetches specific rows) and dataset_columns (lists columns) by focusing on cell-content matching.

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

Usage Guidelines2/5

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

No guidance is provided about when to use this tool versus alternatives. It does not mention any sibling tools, exclusions, or conditions under which another tool (like dataset_stats or dataset_compare) would be more appropriate. The agent is left to infer usage from the name and description.

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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TDQS

A4.1/5.0
Disambiguation4/5

Each tool targets a distinct operation (schema, provenance, exact lookup, fuzzy search, comparison, stats, top-N), but dataset_row, dataset_search, and dataset_compare all return matching rows and could be confused without careful reading of their filter semantics.

Naming Consistency5/5

All tools share the dataset_ prefix and use clear lowercase snake_case names. The second part is sometimes a noun (columns, provenance, row) and sometimes a verb/search-style word, but the pattern is uniform and predictable.

Tool Count5/5

Seven tools is a well-scoped size for a single-dataset querying server. Each tool covers a distinct query mode without bloat or redundancy.

Completeness5/5

The set covers schema discovery, provenance/citation, exact value lookup, substring search, comparisons, numeric statistics, and top/lowest ranking. For a read-only dataset MCP server this is a complete lifecycle with no obvious dead ends.

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