Search the dataset
dataset_searchRows of the Cmmsly 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 Cmmsly 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, the description carries the disclosure burden and does reveal case-insensitive matching and a 50-row upper bound. However, it does not explicitly state substring semantics, ordering, default limit behavior, or whether the tool is read-only, leaving some behavioral ambiguity.
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 front-loads the core behavior and every clause adds value: result type, matching scope, case sensitivity, and row cap. There is no redundancy or filler.
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?
Adequate for a simple two-parameter search, but with no output schema and no annotations, it omits the return row structure, the default limit, and any route to sibling tools. An agent knows roughly what it returns but not the full contract.
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?
The schema already describes the query parameter and constrains limit with min/max; the description adds 'up to 50' but little else. At 50% schema coverage, the description only partially compensates and does not clarify the default limit or matching nuances beyond what the schema implies.
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 behavior: return rows of the Cmmsly dataset whose cells contain the query, with case-insensitivity and a 50-row cap. It is clear on the operation and resource but does not distinguish this from siblings like dataset_top or dataset_row, which could also return rows.
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?
No guidance is given on when to use this tool instead of a sibling, nor any exclusions or context for the search. The description implies a general search capability but leaves the agent to infer the appropriate situation.
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.
The seven tools are mostly distinct: schema, provenance, exact-match row lookup, substring search, value-list comparison, numeric stats, and top-N ranking each serve a clear purpose. dataset_row and dataset_compare both filter rows by column values, and dataset_stats/dataset_top both operate on numeric columns, so there is minor potential for confusion, but the descriptions disambiguate them adequately.
All tools share a consistent dataset_ prefix and use lowercase snake_case throughout. The second segment is a concise operation or concept (columns, compare, provenance, row, search, stats, top), providing a predictable naming pattern.
Seven tools is well-scoped for a single-dataset exploration server. Each tool addresses a distinct query type without redundancy or bloat.
The surface covers the full range of common dataset questions: schema discovery, provenance/citation, exact and fuzzy row retrieval, value comparison, numeric statistics, and ranking. No significant dead ends or missing operations are evident for the stated purpose.