get-key-value-store-recordRead one exact record from a caller-owned Apify key-value store.
| Name | Required | Description | Default |
|---|---|---|---|
| recordKey | Yes | Exact record key. | |
| keyValueStoreId | Yes | Key-value store ID or username~store-name. |
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?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, covering the safety profile. The description adds useful context beyond annotations by specifying 'caller-owned' (an ownership/permission constraint) and 'exact record' (no pattern matching). It does not describe return format or error behavior, but the annotation coverage lowers the burden.
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?
The description is a single, tightly worded sentence with no filler. Every element—read, exact record, caller-owned, Apify key-value store—contributes to the agent's understanding.
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?
For a simple read operation with only two fully documented parameters and safety annotations, the description is nearly complete. It covers ownership scope and exactness. It does not describe the response shape, but given the absence of an output schema and the tool's simplicity, this is a minor gap.
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 coverage is 100%, so both parameters are already well documented. The description adds no additional parameter-level meaning beyond the schema, making the baseline 3 appropriate.
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 ('Read'), a specific resource ('exact record from a caller-owned Apify key-value store'), and a precise scope ('one exact record'). This clearly differentiates it from siblings like get-dataset-items, which target dataset items rather than key-value store records.
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 implies when to use the tool: when you need exactly one record from a key-value store you own. However, it does not explicitly mention alternatives or exclusions, such as using get-dataset-items for dataset data, so the guidance is contextual but not fully explicit.
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.
Most domain tools are well-scoped with explicit cross-references (e.g., FL DBPR vs. STR registry, Texas vs. multistate childcare). However, the evidence-gateway overlaps with EPA, U.S. property, and other specialized tools by describing similar intake categories, creating ambiguity about when to use the router versus the domain-specific tool.
The specialized tools consistently use the civicdataforge-- prefix with descriptive noun phrases, while the generic actor tools use imperative verb_noun style. The naming is readable and predictable within each subgroup, but the mixed conventions and the awkward doubled prefix in civicdataforge--civicdataforge-evidence-gateway prevent full consistency.
Fourteen tools is reasonable for a broad government-evidence server covering many data domains plus an async run lifecycle. The count is not excessive, though the gateway and several overlapping domain-specific tools add some redundancy.
The tool set covers a wide range of evidence domains and provides complete async workflow coverage: launch queries, check run status, fetch dataset items, read KVS records, and abort runs. Minor gaps remain, such as no explicit way to enumerate supported jurisdictions or sources, and the gateway's broad categories are underspecified.