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BeevR

List BeevR case studies

list_case_studies
Read-onlyIdempotent

List production systems BeevR has shipped (healthcare AI, security agent, AI B2B matchmaking, fintech payments, manufacturing) with key results and stack.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
localeNoResponse language: "en" (default) or "vi" (Vietnamese).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is fully covered. The description adds content scope (results and stack per case study) but says nothing about pagination, result limits, or ordering.

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?

One sentence, front-loaded with the verb and resource, with the domain list as useful scoping detail. No wasted words.

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 read-only, zero-required-param list tool with full annotation coverage and no output schema, the description gives enough to invoke correctly. It stops short of noting whether the list is exhaustive or truncated, which is the only real gap.

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 100% and the single locale enum is fully documented in the schema, so the baseline of 3 applies. The description adds no extra locale or formatting semantics beyond what the schema 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 (List) and resource (case studies / production systems BeevR has shipped) and enumerates the domains covered, so an agent knows exactly what comes back. It is clearly distinguishable from the singular sibling get_case_study.

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

Usage Guidelines3/5

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

The plural 'List' verb implies this is the browse-all entry point versus get_case_study for one item, but the description never names that alternative or states when to prefer it. Usage is inferable from the name/siblings rather than explicit.

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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